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A novel EMD-based model with the cooperative training mechanism is proposed to enhance the tourism demand forecasting performance of tourist attraction. Our proposed model develops a cooperative training mechanism integrating a bi-level optimization model, a differential evolution algorithm and interactive fuzzy programming to optimize the neural networks of sub-series decomposed by EMD, which overcomes the difficulty of capturing the relationship among the sub-series and acquires the forecasting accuracy. To verify the performance of the model proposed in this study, extensive experiments are conducted. The experimental results illustrate the superiority of our model among all benchmark models and indicate its effectiveness in incorporating the cooperative training mechanism into the decomposition-ensemble model. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE), as a powerful population-based stochastic optimization algorithm, has attracted the attention of researchers from various fields due to its advantages such as simple operation, strong robustness, and few control parameters. However, many existing DE variants often suffer from drawbacks such as premature convergence and stagnation when solving complicated optimization problems. In view of the aforementioned issues, this paper proposes an adaptive DE with opposition learning-based diversity enhancement (OLBADE). The main contributions can be summarized as follows: Firstly, a new adaptive parameter control is proposed with a non-linear weighting strategy incorporating into the framework of parameter adaptation. Secondly, a donor vector perturbation strategy is introduced to complement existing strategy for increasing population diversity. Thirdly, a novel stagnation indicator is proposed, and then opposition learning strategy is employed to renew stagnated individuals in the population when stagnation occurs. OLBADE is compared with five excellent DE variants under a large test-bed containing CEC2013, CEC2014, CEC2017 and CEC2022 test suites to verify its effectiveness. In addition, OLBADE is applied in parameter identification problem of photovoltaic model to verify its feasibility. Experimental results demonstrate that OLBADE achieves higher solution accuracy, faster convergence speed and better stability. • A hybrid method combining the logistic and sine functions is used for the adjustment of F. • A donor vector perturbation strategy is introduced. • A diversity enhancement mechanism based on opposition learning is proposed. • To avoid over-fitting, a larger test suite is employed in algorithm validation. [ABSTRACT FROM AUTHOR]

In crowdsourcing systems, a challenge arises in efficiently recruiting workers with unknown execution speed to complete tasks with precedence constraints within the shortest possible time. Recent research either only studies recruiting unknown workers or focuses on assigning tasks with precedence constraints, but few works consider both these factors. This paper aims to address this challenge considering all these factors by proposing a digital twin-assisted Combinatorial Multi-arm bandit based multi-round work Recruitment and task Scheduling (CMRS) scheme. Firstly, with the help of digital twin technology, we build digital twin models for crowdsourcing tasks and workers in digital twin layer of platform. And we formulate a Multi-round Workers recruitment and Task scheduling with Precedence constraints (MWTP) problem with the object of minimizing task completion time (makespan) to model the above challenging issue in crowdsourcing computing. The MWTP problem can be divided into two sub-problems, the task scheduling sub-problem and the worker recruitment sub-problem. Secondly, in the CMRS scheme, the differential evolution based task scheduling (DETS) algorithm and CUCB-based worker recruitment (CWR) method are proposed to solve them, respectively. Specifically, for the task scheduling sub-problem, we first propose a priority-driven based task scheduling (PDTS) approximation algorithm to produce a primary solution with an explicit approximation ratio. Then a DETS algorithm is proposed to use the solution of PDTS for initialization and generate a better solution that further improves the performance. For the unknown workers recruitment sub-problem, we use the Combinatorial Multi-Arm Bandit framework to model this, and propose CWR method to learn the computational capabilities of workers while maintaining good performance during multiple rounds of worker recruitment by adding a tuning term to balanced exploration and exploitation. Finally, verified by extensive comparison experiments, the CMRS scheme can achieve the best performance of makespan compared with the baselines in most cases. [ABSTRACT FROM AUTHOR]

• MMODE is developed as a new hybrid gene selection method for tumor identification. • MMODE combines multi-tasking and multi-objective frameworks. • MMODE uses a new elite strategy and a new guidance strategy. • MMODE selects a few genes and achieves high classification accuracy. A key preprocessing step in tumor recognition based on microarray expression profile data and machine learning is to identify tumor marker genes. Gene selection aims to select the most relevant gene subset from the original ultra-high dimensional microarray expression profile data to improve tumor identification performance. Inspired by evolutionary multitasking (EMT) and multi-objective optimization, this paper puts forward a novel multitasking multi-objective differential evolution gene selection algorithm (MMODE) which uses new elite and guidance strategies to select the best gene subsets. MMODE initializes two different populations according to different filtering criteria to increase the diversity of the search. These two populations guide their respective populations to search in the optimal direction through knowledge transfer in the evolutionary process. In addition, MMODE employs new elite and guidance strategies that enables individuals to narrow the search range and jump out of local optima. The proposed algorithm is validated on 13 publicly available microarray expression datasets in comparison with state-of-the-art gene selection algorithms. The experimental results show that MMODE can find smaller gene subsets and achieve higher classification accuracy compared with other algorithms. [ABSTRACT FROM AUTHOR]

The differential evolution (DE) algorithm is widely regarded as one of the most influential evolutionary algorithms for addressing complex optimization problems. However, the fixed mutation strategy limits the adaptive ability of DE, and the lack of utilization of historical information limits the optimization ability of DE. In this paper, an indicator-based self-learning differential evolution algorithm (ISDE) is proposed. A jump out mechanism based on deep reinforcement learning is adopted to control the mutation intensity of the population. The neural network in the jump out mechanism is designed as a decision maker. The mutation intensity of the population is controlled by the neural network, and the neural network are trained by a double deep Q network algorithm based on the continuous data generated during the evolution process. A population range indicator (PRI) is utilized to describe individual differences in the population. A diversity maintenance mechanism is designed to maintain individual differences according to the value of PRI. The experimental results reveal that the comprehensive performance of ISDE is superior to comparison algorithms on CEC 2017 real-parameter numerical optimization. [ABSTRACT FROM AUTHOR]

Airspace surveillance is a significant issue for many countries to control and manage their airspace. The number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with the least possible number of radars in a certain region. The radar placement problem is considered as a multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and make it more suitable for solving this optimization problem. The experimental results show that a coverage rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it exceeds 99%. • This paper solves the radar placement problem using a set of metaheuristic approaches. • The problem is considered as a multi-objective optimization problem. • Two adaptive and enhanced versions of the NSGA-II algorithm are proposed. • The results show that a coverage rate of 98% can be achieved with just a few radars. [ABSTRACT FROM AUTHOR]

Renewable energy sources are progressively assuming a pivotal role in shaping our future, and wave energy stands out as a promising avenue due to its substantial potential and minimal ecological impact. Consequently, extensive research has been directed towards optimizing the layout of wave energy converters (WECs). However, as the number of optimized buoys increases, so does the complexity of calculating hydrodynamic interactions, placing great demands on computing power. Simultaneously, these dynamic interactions can yield either constructive or detrimental outcomes, amplifying the intricacy of layout evaluation. Effectively and promptly determining the optimal buoy arrangement to achieve high energy output efficiency emerges as a pivotal research challenge. We propose a chaos-based differential evolutionary algorithm with a three-layer information structure, including excavation, balancing, and recycling layers, which reasonably adjusts the population structure and makes full use of individual information to optimize the next exploration by combining with chaotic maps. We compared our method with other state-of-the-art intelligent algorithms applied to the problem of wave energy generators and tested it in four real wave scenarios (Perth, Adelaide, Tasmania, and Sydney) using numerical modeling to calculate its energy output. The experimental results show that our improved strategy improves the energy output of the oscillating buoy-type wave energy generator by an average of 101.5%, 93.1%, 23.1%, and 0.7% compared to the mainstream state-of-the-art algorithm for the four scenarios, respectively. • An improved algorithm applied to WEC layout optimization is proposed. • A triple-layered information chaos improvement strategy is proposed. • Compared four DE series algorithms and proved the strategy is powerful. • Collected data from four real wave scenarios and analyzed potential energies. [ABSTRACT FROM AUTHOR]

Network slicing enables diverse scenarios within The Fifth Generation of Cellular System (5G) and beyond systems. Mapping virtual networks to their physical counterparts, known as the Virtual Network Embedding (VNE) problem, is challenging with mathematical complexities. While Linear Programming (LP) approaches can address the VNE problem, they may prove impractical due to the execution time required to obtain optimal solutions. Furthermore, meta-heuristic algorithms offer a promising alternative in balancing solution optimality and execution time. In this study, we propose the Artificial Algae Algorithm (AAA) as a solution for the VNE problem in network slicing scenarios specific to 5G. We compare the performance of AAA with other commonly used meta-heuristics, including Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Since the evolutionary process of AAA consistently operates in three random dimensions, irrespective of the problem's dimensionality, the runtime performance of AAA remains unaffected by the number of virtual nodes, yielding execution times up to ten times faster than DE and PSO when considering 30 nodes. In that scenario, the proposed approach with AAA presented an improvement of more than 60% compared to DE and PSO in an execution time ten times lower. [ABSTRACT FROM AUTHOR]

Since the outbreak of the COVID-19 pandemic, reshaping global supply chains for bulk mining commodities, such as coal, copper, and iron ore, has posed significant challenges. The complexity and multi-stakeholder nature of mining supply chain network design (MSCND) require innovative optimization approaches. However, traditional literature often focuses on centralized MSCND strategies, neglecting the competitive dynamics and conflicts of interest among stakeholders. To address this gap, this study introduces a bi-level programming (BLP) model for decentralized MSCND, capturing interactions between upper-level ore production and lower-level ore processing enterprises. To overcome the computational complexity of the BLP model, we develop a novel hybrid math-heuristic algorithm called Sine Cosine and Differential Evolution Algorithm with Constraint Repair Mechanism (SCDEA-CRM). The proposed SCDEA-CRM integrates the search mechanisms of sine cosine and differential evolution algorithms, along with a novel constraint repair mechanism to fix infeasible solutions caused by chemical composition imbalances between raw ores and products. Numerical experiments demonstrate the SCDEA-CRM's superior performance in solving the BLP model. A real-world case study in a decentralized iron ore supply chain validates the model's practical applicability and highlights its advantages over the centralized counterpart model. A sensitivity analysis is conducted to assess the impact of product iron content variations on supply chain costs. • Explored new decentralized design in mining supply chain networks. • Formulated a bi-level programming model considering hierarchical decisions. • Proposed a hybrid math-heuristic algorithm integrating SCA and DE. • Developed a constraint repair mechanism for infeasible upper-level solutions. • Validated the proposed methodology in a real-world case study. [ABSTRACT FROM AUTHOR]

The surveillance or monitoring of places is crucial to ensuring security, protecting people and assets, preventing crimes, and detecting emergencies, to mention some. Unmanned Aerial Vehicles (UAVs) play a vital role in these applications, offering versatility, agility, and aerial vision. A crucial step for such tasks is to protect the UAV path ahead. This paper focuses on a methodology harnessing the unpredictable nature of chaotic systems to generate trajectories around a closed area or contour. However, although a vast quantity of research papers mention the use of chaotic path generation, they have yet to learn about the control system and the dynamics affecting the UAV, where developing the control theory is challenging. In this paper, we design controllers based on predetermined-time stability, ensuring the achievement of the desired trajectory before a specified time. Additionally, adjusting control parameters is a crucial step during the control design, impacting the control performance. Hence, we present a method to optimize and adapt controller parameters through evolutionary optimization, demonstrating precision enhancement. We validate the proposed system's performance and the controllers through numerical simulations, indicating that the UAV effectively and accurately follows some types of chaotic trajectories like a square contour, aiming at the feasibility of this methodology in real UAV surveillance applications. • Design of Predefined-Time Control (PTC) for chaotic trajectory tracking with UAVs. • Generation of complex and unpredictable trajectories based on chaotic systems. • Optimization of controller parameters by Differential Evolution metaheuristic. • Lyapunov analysis for the design and convergence of Predefined-Time Controllers. [ABSTRACT FROM AUTHOR]

Adversarial sample generation problem is a hot issue in the security field of deep learning. Evolutionary algorithm has been widely used to solve this problem in recent years because of its good global search ability. However, existing methods still suffer from the "curse of dimensionality" when attacking high-resolution images. In this paper, a two-stage frequency domain generation algorithm of black-box adversarial samples based on differential evolution is proposed. In the first stage, a representative image-guided differential evolution method is proposed to quickly generate a universal adversarial perturbation with a high attack success rate in the frequency-domain. In the second stage, a space reduction strategy based on frequency-domain pixel blocks is designed to reduce the search space and alleviate the problem of "curse of dimensionality". In addition, a new space–frequency interaction sensitivity measure is introduced to evaluate the similarity between the adversarial samples and the original images. The adversarial perturbations obtained by the measure are more in line with the subjective perception of the human eye. Finally, compared with several typical black-box adversarial sample generation algorithms, experimental results show that the proposed algorithm can achieve higher attack success rate with less prediction times. • Proposing a two-stage evolutionary generation framework of adversarial samples. • Developing a space–frequency interaction sensitivity measure. • Presenting a representative image-guided differential evolutionary algorithm. • Designing an individual adversarial perturbation generation algorithm. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant pitfall of DE lies in its susceptibility to getting trapped in local minima, leading to algorithmic stagnation and substantial performance impairments. To counteract the shortcoming, the A daptative D ifferential E volution with D iversity M aintenance and R estart M echanism (ADE-DMRM) is proposed. This approach integrates three primary innovations. Firstly, the algorithm controls the number of generations that individuals stay in the external archive based on the successful evolution rate, thereby optimizing the mutation strategy to effectively maintain population diversity. Secondly, ADE-DMRM proposes a novel restart mechanism in which the current stagnant individuals are identified by combining a stagnation tracker and a diversity assessment indicator and then regenerated using a dimension-learning-based approach. Thirdly, wavelet basis functions and Cauchy distributions are employed for scaling factor implementation across different stages, while the dimension change information of successfully evolved individuals is harnessed to refine the adaptive parameter control scheme. Finally, comprehensive experiments were undertaken on the CEC2013, CEC2014, and CEC2017 test sets, assessing accuracy, convergence speed, the efficacy of each module, and time complexity. The results confirm that ADE-DMRM is an efficient single-objective optimization algorithm that outperforms current advanced variants. • An adaptive DE with Diversity Maintenance and Restart Mechanism is proposed. • The external archive is selectively optimized based on the successful rate of the population. • A novel restart mechanism is proposed to avoid premature convergence. • Wavelet basis functions and Cauchy distributions are employed in generating F. [ABSTRACT FROM AUTHOR]

Surface roughness is a critical parameter for quantifying the surface quality of a workpiece. Accurate surface roughness prediction can significantly influence the overall quality of the final components by reducing costs and enhancing productivity. Although various prediction methods have been explored in previous research, less attention has been given to explainable prediction methods for surface roughness. This paper proposes a surface roughness prediction model based on the Differential Evolution (DE) algorithm with ensemble learning. It aims to elucidate the intrinsic correlation between processing parameters and surface roughness values in Multi-Jet Polishing (MJP) using explainable analysis methods. The proposed Ensemble Regression with Differential Evolution (ERDE) algorithm comprises two main modules: data processing and analytics, and ensemble model prediction. These modules investigate the surface roughness of MJP from the data and model levels, respectively. To validate the effectiveness of ERDE, MJP experiments were conducted on three-dimensional printed components of 316L stainless steel. The experimental data indicated that ERDE outperforms other existing algorithms, reducing the mean absolute percentage error by approximately 42 %. [ABSTRACT FROM AUTHOR]

The multi-objective multi-task evolutionary algorithm is emerging in the field of evolutionary computating. A great deal of multi-objective multi-tasking algorithms have been proposed recently and proved to be superior in solving many problems. However, in the real world, there are numerous high-dimensional objective problems that need to be solved. With the increasing number of objectives, the slow convergence speed, high computational complexity and reduced population diversity will occur in the existing multi-objective multi-tasking algorithms. There is a growing need to study high-dimensional objective algorithms in multi-tasking environment. To fulfill this research gap, a novel many-objective multi-tasking evolutionary algorithm (MaMTO-ADE) is put forward in this paper. The reference points-based non-dominated sorting method is introduced, which guarantees the diversity of the population in high-dimensional space. And a new offspring generation strategy is proposed to accelerate the population convergence and enables the population to generate high-quality offspring. The performance of MaMTO-ADE is verified on the classical benchmark problems, and the experimental results emphasize the excellent competitiveness of MaMTO-ADE compared to other related algorithms. • A novel many-objective multitask framework is proposed to solve the high-dimension multi-objective problems. • This algorithm uses a new mixture probability model-based knowledge transfer method. • A novel offspring generation strategy is proposed. • The new individual selection method is used for inter-task crossover. [ABSTRACT FROM AUTHOR]

Proven as an efficient population-based optimization algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) features two evolution paths, one to update the covariance matrix and the other to adapt its mutation strength. Considering the time and space complexity of CMA-ES, there are several attempts in the literature to realize a single-path algorithm. However, such attempts require altering the original structure of CMA-ES and consequently eliminating some vital features crucial to the overall algorithm performance. In this paper, we show that the two evolution paths of CMA-ES are highly correlated and one can be expressed in terms of the other thus reducing the computational cost of the algorithm while preserving the original algorithmic framework. Based on experimental studies conducted using 30 functions from the IEEE CEC 2014 benchmark suite, the proposed algorithm shows comparable results with the standard CMA-ES as well as five other state-of-the-art CMA-ES variants. Furthermore, it is shown that the proposed algorithm can be applied to policy search in Deep Reinforcement Learning (DRL). Performance results based on selected DRL problems from different application domains prove the efficiency of the proposed algorithm compared to other population-based algorithms often applied for policy search in DRL. • A correlated evolution path CMAES algorithm is proposed for optimization. • The proposed algorithm is evaluated on IEEE CEC 2014 Benchmark. • The proposed algorithm is applied in optimal policy search on RL tasks. • Comparative performance is demonstrated with other start-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Population-based algorithms aim to explore the entire solution space in global numerical optimization problems. However, it is important to acknowledge that the solution spaces of different problems possess distinct characteristics, and even distinct regions within the same solution space can vary significantly. Efficient exploration of these diverse regions necessitates the utilization of distinct search models. To address this challenge, this study proposes a new variant of the Differential Evolution (DE) algorithm called MHDE. The MHDE algorithm introduces hash clustering technology combined with adaptive mutation strategies. The integration of hash clustering technology enables fast population clustering, significantly enhancing clustering efficiency. Additionally, a method for evaluating the population state is designed that allows for an adaptive clustering quantity to adapt the clustering quantity to the population state. Furthermore, a novel method is devised to calculate individual improvements considering the varying levels of difficulty that individuals face in achieving improvements within a population. This method is combined with a parameter adaptive mechanism, resulting in a weighted parameter adaptive mechanism. Experiments are conducted on the CEC2017 benchmark suite to evaluate the performance of the MHDE algorithm. The experimental results demonstrate that MHDE exhibits competitive performance compared with other efficient DE variants. Moreover, the MHDE algorithm is applied to the node deployment problem in wireless sensor networks (WSNs). The MHDE algorithm demonstrates efficient performance in node deployment problems through simulation experiments in different scenarios. • Introducing a BLSH technique for population clustering. • Designing a new method to evaluate population states. • Presenting a novel approach for calculating individual contributions. • Utilizing an external population to accelerate algorithm convergence. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation. [ABSTRACT FROM AUTHOR]

The mission-oriented systems (MOS) are often required to operate a consecutive of missions interspersed with finite breaks. For MOS, an appropriate maintenance strategy is the selective maintenance (SM). The SM problem (SMP) aims then to identify an optimal set of components to be maintained under maintenance resource constraints such as limited break durations. However, most existing SM models merely deals with either stochastic independent (s -independent) components in MOS or deterministic mission and break durations. To overcome this unrealistic restriction, this paper develops a novel SM model for optimizing the SMP in MOS operating under stochastic mission and break durations and s -dependent competing risks (CRs). Durations of missions and breaks are respectively governed by normal and uniform distributions, while the s -dependent CRs are captured by the copula function of which the unknown parameters are estimated relying on the two-stage MLE method. Furthermore, due to the stochastic break durations, an integrated importance measure (IIM) is introduced to measure the maintenance priority on selected components. The resulting SM optimization model is therefore formulated as a mixed-integer nonlinear program (MINLP) whose objective is to minimize the total cost while ensuring the required minimum mission reliability. A differential evolution (DE) algorithm is designed to solve the MINLP. Two numerical experiments are conducted on a ball screw system. Results obtained demonstrate the validity of our proposed model, in addition to the robustness of the solution algorithm designed. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient and effective global optimization algorithm, and many DE variants with adaptive strategy selection have been proposed. However, among the existing DE variants with adaptive strategy selection, only a few last generations of population information are utilized to conduct statistical analysis for strategy selection, and the population cumulative historical experience information does not be fully used to assist population to effectively search. In this paper, a DE with autonomous strategy selection (ASS-DE) is proposed to make full use of the population cumulative historical experience information. In ASS-DE, an individual can autonomously choose the better mutation strategy guided by the cumulative historical experience. As the same time, a parameter updating mechanism with archive is introduced to assign appropriate control parameters to the strategy. Additionally, an evolutionary learning mechanism based on individual similarity is proposed to guide the learning of historical experience information. To verify the performance of ASS-DE, CEC2015 and CEC2017 benchmark functions and the real-world optimization problem of wavelet parameter optimization in remote sensing image denoising is compared with some state-of-the-art intelligence algorithms. The experimental results show that ASS-DE obtains the promising performance compared with other DE variants. The source code of ASS-DE can be found in GitHub at https://github.com/Ramessis/ASS-DE_denoising.git. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficacious global optimization algorithm, and many variants have been advanced since its inception. During the iterative search process, any individual in any DE-type algorithm can likely locate a local optimum, and once that happens it may need many attempts for this individual to find another better solution, thus leading to ineffective consumption of computing resources and decline in the opportunity to search other promising regions. Therefor, this article proposes a DE with migration mechanism and information reutilization (MIDE). Specifically, a migration mechanism is first presented to make individuals located at local optima abandon current locations and move to other regions to continue their search, tending to solve the problem above. Meanwhile, a new mutation strategy named DE/ p best/1 with external archive is introduced to reutilize abandoned local optima to provide helpful information of evolution. Additionally, the settings of control parameters associated with this mutation strategy are designed in such a manner that they can contribute to well balancing exploration and exploitation. To evaluate the performance of MIDE, extensive experiments are conducted on CEC 2017 and CEC 2014 test suites, and the comparison results between MIDE and 19 competitors (including 13 state-of-the-art DE variants and six winner algorithms of CEC 2014 and CEC 2017 competitions) demonstrate MIDE's competitive performance. [ABSTRACT FROM AUTHOR]

Locating multiple roots of nonlinear equation systems (NESs) remains a challenging and meaningful task in the numerical optimization community. Although a large number of NES-solving approaches have been put forward, they can only find the roots of one NES at a time. In this paper, we develop a novel NES-solving algorithm based on evolutionary multitasking referred to as EMNES, the goal of which is to effectively find the multiple roots of multiple different NESs simultaneously in a single run through knowledge sharing and transfer. Specifically, firstly a NES-solving framework based on evolutionary multitasking is proposed. Then an efficient multi-task evolutionary algorithm based on neighborhood-based speciation differential evolution for NESs is designed. Finally, combining Gaussian distribution and uniform distribution, a novel resource release strategy is proposed to release the found roots to improve resource utilization and increase population diversity. Numerous experimental results reveal that the proposed EMNES algorithm can achieve a higher root rate and success rate when compared with several well-established algorithms on thirty NESs. Furthermore, simulation results on a more complex test set show that the proposed EMNES is able to locate more roots than most comparison algorithms. • An evolutionary multitasking NES-solving framework is proposed. • Relationship of different NESs is established by a unified search space. • An adaptive mutation strategy with knowledge transfer is presented. • A novel resource release strategy is developed to improve the utilization of computing resources. [ABSTRACT FROM AUTHOR]

• Formulation of a perishable inventory model in multi-echelon distribution systems. • Integration of surrogate models significantly improves the performance of particle swarm optimization and differential evolution algorithms. • Development of a co-evolutionary strategy for efficient data point sampling of surrogate models in differential evolution algorithm. • The proposed algorithm outperformed 12 different metaheuristics. • Introduction of a new performance indicator to analyze the mechanism of surrogate-assisted evolutionary computation techniques. Simulation is widely used for analyzing supply chains with complex structures and stochastic nature. However, optimizing supply chain simulation models is usually computationally expensive. This study proposes a surrogate-assisted evolutionary optimization approach to optimize the inventory policies in multi-echelon distribution systems for perishable items under a limited number of evaluations. The random forest algorithm is used to build the surrogate model for a faster estimation of the performance of the inventory policies. A co-evolutionary differential evolution algorithm is proposed to simultaneously evolve the population through multiple searching strategies. The generated solutions are estimated by the low-cost surrogate model to select the promising solutions, which will be evaluated by the inventory model. Moreover, this study also integrates the surrogate model into two classic metaheuristic algorithms, particle swarm optimization and differential evolution. Also, a new performance indicator is proposed to examine the efficiency of the surrogate model in evolutionary computation. Two case studies are used to investigate the performance of the proposed algorithms. The experimental results show that both particle swarm optimization and differential evolution exhibit performance improvements exceeding 55% by using surrogate models under the limited number of function evaluations. Furthermore, the surrogate model reduces computational time for both algorithms by over 34% to achieve equivalent objective values. Finally, the proposed co-evolutionary differential evolution algorithm is compared with 12 algorithms, and the results show that the proposed algorithm consistently outperforms them. These findings confirm the usefulness of the surrogate model in evolutionary algorithms and the effectiveness of the proposed co-evolutionary strategy for solving perishable inventory problems. [ABSTRACT FROM AUTHOR]

A meta-evolutionary framework called Differential Evolution Ensemble Designer (DEED) has been proposed in this paper to automate the design of DE ensemble algorithms. Given the design components of DE ensembles and a set of optimization problems, DEED evolves effective and robust DE ensemble designs. The design components of DE ensemble algorithms include population management, constituent algorithms in the ensemble, information mixing amongst the sub-populations in the ensemble and the numerical parameters associated with various aspects of the ensemble. DEED employs Dynamic Structured Grammatical Evolution (DSGE) as the meta-evolutionary algorithm. A Backus–Naur form (BNF) grammar has been developed in this paper to represent the design space of DE ensembles and used by DSGE to evolve DE ensemble designs. DEED has been employed to evolve DE ensemble designs for solving 30-dimensional CEC'17 benchmark functions. The evolved designs (both the best design as well as all the final evolved designs) have been validated on CEC'14 and CEC'17 functions at 10, 30 and 50 dimensions and on real-world numerical optimization problems in CEC'11 benchmark suite. The DEED evolved designs have also been tested against the state-of-the-art algorithm configurator - irace. The performance of DEED evolved ensemble designs have been observed to be very competitive against that of manually designed and tuned state-of-the-art DE ensemble algorithms in the literature. DEED has also been demonstrated to evolve both co-operative and competitive style DE ensembles. The simulation experiments demonstrate the effectiveness as well as robustness of the evolved ensemble designs and the reliability of DEED framework in consistently evolving effective DE ensemble designs. • An automated ensemble differential evolution (DE) designer named DEED is presented. • A meta-evolutionary approach to automatically choose ensemble DE design components. • Grammatical evolution forms the meta-evolutionary engine in DEED. • DEED can support researchers and practitioners in the ensemble DE design process. [ABSTRACT FROM AUTHOR]

In bioinformatics, biclustering is a crucial optimization task that can reveal hidden patterns and identify groups of genes that behave similarly under certain conditions. This study aims to efficiently identify high-quality and cohesive biclusters that share common characteristics across two data dimensions. To achieve this, we propose the first biclustering approach that utilizes Multi-objective Differential Evolution (DE), which is a novel technique for gene group discovery. Additionally, we introduce the Biclustering Binary Differential Evolution (BBDE), a new mutation operator that combines node addition and deletion, guided by an adaptive factor F. We thoroughly tested our method's effectiveness taking into account biological relevance, noise, overlap resistance, and statistics. We compared our results to state of the art algorithms using both synthetic and real datasets like Yeast Cell Cycle, Saccharomyces cerevisiae, and Human B Cell. Our algorithms outperformed the comparisons and effectively identified significant biclusters. • Multi-objective evolutionary biclustering approach using differential evolution. • BBDE: An adaptive mutation operator to adjust DE for biclustering. • A powerful approach for identifying multiple large biclusters simultaneously. [ABSTRACT FROM AUTHOR]

Traditional differential evolution (DE) algorithms have functional limitations in effectively addressing increasingly intricate numerical optimization problems. The key to responding to this challenge is to strike a suitable balance between exploration and exploitation. Exploration is used to find the global optimal solution, and exploitation is used to improve the accuracy of the global optimal solution. Therefore, this study introduces a novel differential evolution algorithm with a stage stratification method and a dual balanced mutation strategy framework, named SbmDE. To enhance the balance between convergence and diversity, the population is stratified into exploration and exploitation layers based on the size of the fitness value and evolutionary period. In the exploitation layer, a hybridization mutation is applied as the first mutation. A novel dual improved mutation operation is proposed and applied to the exploration layer. For the first mutation, the hybridization mutation is used in the same manner as for the exploitation layer. For the secondary mutation, two improved mutation strategies are proposed to alleviate premature convergence at the early stage of evolution and to enhance the local neighborhood search at a later stage, named DE/ranking-to-rand/1 and DE/best-current-dev/1. Experiments were conducted on the CEC2017 benchmark suite, which contains 29 single-objective real-parameter numerical optimization problems, to evaluate the performance of the proposed algorithm. Compared with 11 state-of-the-art algorithms, the results demonstrate the superiority of the proposed algorithm, which does not increase the time complexity. Additionally, based on the four engineering design problems, the proposed algorithm is fully competent in solving practical constrained optimization problems. • A stage stratification method is proposed to divide population. • Dual mutation is proposed and applied to the exploration layer. • Proposed two improved mutation strategies applied to the secondary mutation. • SbmDE obtains better values than the other algorithms in the paper. • SbmDE is applied to four engineering design problems. [ABSTRACT FROM AUTHOR]

• Propose and proved simplified representation of multi-threshold Tsallis entropy and Renyi entropy. • Proved that Tsallis entropy and Renyi entropy are equivalent for multi-threshold segmentation with the same parameters. • Proposed recursive algorithm of simplified Tsallis entropy and Renyi entropy under multiple thresholds. • Compared with the traditional Tsallis entropy, the time complexity of the simplified representation is lower, and the time complexity of the recursive simplified representation is the lowest. Image segmentation is an important step in obtaining image information, and it has always been an extremely critical link in the fields of computer vision and pattern recognition. Because of its simplicity and effectiveness, threshold-based image segmentation technology has received significant attention and research by researchers. Tsallis entropy and Renyi entropy are two important global threshold selection methods in image thresholding. They not only have a special correspondence in mathematical expression, but also have a certain relationship in the field of image segmentation. Considering the number of thresholds increases, the complexity of the expression of Tsallis entropy criterion function also increases sharply, which is difficult to apply in multiple thresholds. Given this, this paper proposes a simplified expression of Tsallis entropy with multiple thresholds, which is proved by mathematical induction. Simultaneously, it is concluded that the multiple-threshold segmentation of Tsallis entropy and Renyi entropy with the same parameters is equivalent. To overcome the inconvenience of high calculation cost under multi-threshold and to improve calculation efficiency, recursive algorithms based on simplified Tsallis entropy and Renyi entropy under multi-threshold are given. The calculation costs of the traditional Tsallis entropy and Renyi entropy threshold, simplified Tsallis entropy and Renyi entropy threshold, and the recursive simplified Tsallis entropy and Renyi entropy are compared through experiments. The results have revealed that the calculation time of simplified Tsallis entropy and Renyi entropy is shorter than the traditional methods, and the calculation time of the recursive simplified Tsallis entropy and Renyi entropy is much lower than that of the simplified expression. Finally, we use the three optimization algorithms including particle swarm, differential evolution and Harris hawks algorithm to solve the optimal solutions of the six expressions in the case of multilevel thresholds. Experiments also show that recursive simplified Tsallis entropy and Renyi entropy have the lowest calculation time cost under the same parameters. [ABSTRACT FROM AUTHOR]

The cost of software testing could be reduced if faulty entities were identified prior to the testing phase, which is possible with software fault prediction (SFP). In most SFP models, machine learning (ML) methods are used, and one aspect of improving prediction accuracy with these methods is tuning their control parameters. However, parameter tuning has not been addressed properly in the field of software analytics, and the conventional methods (such as basic Differential Evolution, Random Search, and Grid Search) are either not up-to-date, or suffer from shortcomings, such as the inability to benefit from prior experience, or are overly expensive. This study aims to examine and propose parameter tuners, called DEPTs, based on different variants of Differential Evolution for SFP with the Swift-Finalize strategy (to reduce runtime), which in addition to being up-to-date, have overcome many of the challenges associated with common methods. An experimental framework was developed to compare DEPTs with three widely used parameter tuners, applied to four common data miners, on 10 open-source projects, and to evaluate the performance of DEPTs, we used eight performance measures. According to our results, the three tuners out of five DEPTs improved prediction accuracy in more than 70% of tuned cases, and occasionally, they exceeded benchmark methods by over 10% in case of G-measure. The DEPTs took reasonable amounts of time to tune parameters for SFP as well. [ABSTRACT FROM AUTHOR]

A metaheuristic-based structural optimization method, whilst being popularly adopted due to its advantages in by-passing gradient function calculations, requires the use of time-consuming advanced analyses for constraint evaluation. To overcome this drawback, the present paper proposes a robust (machine learning-based) optimization method that combines the light gradient boosting machine (LightGBM) with the efficient p-best differential evolution (E p DE) method. In essence, the LightGBM classification model is constructed to assess the constraint (safety and integrity) satisfaction of structures. An efficient framework using a so-called safety parameter is proposed to prevent inaccurate predictions of the LightGBM model. The E p DE processes the optimization procedures on the constructed classification LightGBM model. This enables an enhanced machine learning-based optimization technique that not only maintains the sufficiently accurate optimal design of structures but also significantly reduces the required computing efforts, as compared to standard optimization schemes. Various examples of steel structure designs (i.e., two of which have been provided herein) have been successfully performed by the proposed approach. These illustrate the accuracy and robustness of the proposed method, where good comparisons with reference algorithms (including standard DE with "DE/rand/1" mutational strategy, Jaya, Rao-1 and CaDE) are evidenced. The statistical values collected present the accuracy and reliability of the proposed method in obtaining the minimum total weight designs with substantial (some 60%) reduction of computing efforts required to furnish optimal results. [ABSTRACT FROM AUTHOR]

Unmanned surface vessel (USV) has a wide range of applications in oceanographic research, resource development, environment detection, and security rescue due to its advantages of maneuverability, flexibility, fast response, and intelligence. The ability of USVs to autonomously and effectively avoid obstacles in highly dynamic and uncertain marine environments is a prerequisite for the successful completion of their tasks. Therefore, in this article, a USV collision avoidance based on International Regulations for Preventing Collisions at Sea and the Collision Risk Model with the Improved Differential Evolution Algorithm (CRI-DE) has been considered. Based on the International Regulations for Preventing Collisions at Sea (COLREGs) and common practices of seafarers, an improved ship collision risk model is proposed. Specifically, the model is innovatively combined with the differential evolution algorithm (DE) as a constraint condition to further realize path planning in complex situations. Moreover, chaotic multi-population parallel optimization, parameter adaptive adjustment strategy, and the construction of fitness function based on individual path points are added to the DE. In this way, the ability to escape from local optima and enrich population diversity can be guaranteed. Finally, experiments based on the proposed CRI-DE are conducted and the results indicate the efficiency and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Before the terms "smart city" and "floating city" were introduced, the world's population had increased and land shortage across the world was already widely recognized. As a first challenge, the previous studies have developed the concept of a smart city as a creative answer, following that, several scientists proposed the floating city concept in the literature as a solution to the increased sea levels. Moreover, engineers, architects, and designers deal with city planning, for smart and floating settlements as a difficult design challenge, and evolutionary algorithms could be employed to address this complex problem by optimizing residents' needs. As a continuation of our previous studies on this topic, this time, we develop a multi-objective continuous genetic algorithm with differential evolution (DE) mutation strategy (MO_CGADE) and a multi-objective ensemble differential evolution algorithm (MO_EDE) to solve the problem on hand. Then, we compare the performance of the MO_CGADE and MO_EDE algorithms with the non-dominated sorting genetic algorithm (NSGAII) to maximize two conflicted objective functions, namely, scenery, and walkability in the proposed smart floating city model created in the Grasshopper Algorithmic Modeling Environment. The parametric model that we create in the Grasshopper software includes 64 decision variables, area constraints and objective functions to be optimized by MO_CGADE, MO_EDE, and NSGAII algorithms. Computational results show that MO_CGADE and MO_EDE algorithms generate better Pareto ranking results than the traditional NSGAII algorithm in terms of cardinality, distribution spacing, and coverage metrics. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) and flower pollination algorithm (FPA) are efficient and novel meta -heuristic algorithms, which are effective tools for solving global optimization problems (GOPs). However, a single algorithm is always restricted to its own principle and mechanism, and even suffers from the imbalance of global and local search ability when solving large-scale GOPs. The organic combination of the two algorithms with a certain strategy can improve the performance of the hybrid algorithm. Motivated by this idea, a hybrid global optimization algorithm based on DE and FPA is proposed. Firstly, an improved initial population generation method is provided for improving the quality of the initial population. Secondly, an improved DE is given, and the parameters F and CR are adjusted in an adaptive and dynamic fashion, and a combined mutation operator is designed. Thirdly, an improved FPA is proposed, and a method for adaptively and dynamically adjusting the switch probability p is given, and formulas for updating the global and local position are introduced. Lastly, a hybrid strategy of organically combining DE and FPA is proposed to form a hybrid algorithm of differential evolution and flower pollination (HADEFP). To demonstrate the performance of the hybrid algorithm, the proposed hybrid algorithm is tested against other algorithms in the literature on CEC 2017 test functions (10- D , 50- D and 100- D) and practical optimization problem, respectively. The results show that the proposed HADEFP outperforms other comparison algorithms and has better robustness. [ABSTRACT FROM AUTHOR]

Nowadays, factors such as global climate change, environmental damage, and the impact of human activities have led to an increase in natural disasters, and the frequency of natural disaster problems is increasing, which poses a great threat to the safety of people's lives and property. The increased frequency of natural disasters has increased the need to focus on the ability to prevent and respond to disasters. The increased frequency of natural disasters has increased the need to focus on the capacity for disaster prevention and mitigation. All parties need to take appropriate measures to reduce losses and provide assistance after a disaster occurs. Among them, volunteers are a newly emerged important force for rescue, but volunteers have characteristics that normal rescue organizations do not have, such as voluntary and fragmented nature, therefore, volunteer allocation becomes an important issue today. This study tries to construct a volunteer assignment method, which takes into account several factors in the assignment, such as volunteers' own willingness to the disaster site, the competency of volunteers' various abilities, the demand of the disaster site for the task and the time satisfaction of the disaster victims, etc. L-T2FNs are introduced in the assignment process to enhance the degree of certainty; Prospect theory's value function to consider the disaster victims' psychology; This study proposes an algorithm to reduce evidence fusion conflicts. This algorithm uses a differential evolutionary algorithm based on the Dempster–Shafer theory to train the lowest conflict index (determined by K and evidence distance) for BPA fusion. Subsequently, the assignment is demonstrated by using the 7.8 magnitude earthquake in Turkey in 2023 as an arithmetic example to provide a solution to the problem of how to allocate volunteers to the appropriate disaster sites. • Propose effective methods for conflict evidence fusion based on differential evolution. • Consider volunteers' preferences, competencies and victims' satisfaction in volunteer assignments. • Use linguistic type-2 fuzzy sets and evidence theory to reduce uncertainty in decision-making. [ABSTRACT FROM AUTHOR]

This article proposed a hybrid search mode-based differential evolution (HSM-DE) algorithm to overcome the complex design of an interval type-2 fuzzy logic system (IT2FLS). First, transform complex design processes of the IT2FLS into optimization problems for our proposed evolutionary algorithm by decoding the whole system into design parameters regarded as search objectives. Then, the DE algorithm with a rapid convergence characteristic is adopted as a global search method for exploration according to a memetic algorithm (MA) structure. Considering the conventional DE algorithm is lack of escaping ability for the local optimal solution, a local search method combined with a simulated binary crossover (SBX) and polynomial mutation (PM) is utilized to enhance the exploitation behavior of our proposed method. In addition, simple constraints on the design parameters of the membership function are added to balance the searching freedom of the HSM-DE algorithm and the readability of the optimal IT2FLS. Finally, a simulation of a mobile two-wheel vehicle (MTWV) under a goal search task is performed to verify the effectiveness of the optimal IT2FLS auto designed by HSM-DE. All results demonstrate that the HSM-DE realizes auto design for the IT2FLS with high-performance control and outperforms the comparative methods. Moreover, four comparison methods are performed to validate the high-performance of the proposed HSM-DE on the search ability of the optimal solution. • A hybrid search mode-based differential evolution algorithm is proposed. • Memetic algorithm structure avoids the early convergence and local optimal solution. • Proposed algorithm with strong search ability and robustness outperforms the comparison methods. • The interval type-2 logic system auto designed by the proposed algorithm has high performance. [ABSTRACT FROM AUTHOR]

Energy-saving, one of the most significant strategies for green manufacturing, has become the focus of more and more scholars and enterprise managers. Hence, this work addresses the minimization of energy and cost requirements on mixed-model multi-manned assembly line balancing with multi-skilled workers. A new mixed-integer linear programming model is proposed to define the problem. Additionally, a novel multi-objective co-evolutionary algorithm is designed to achieve the trade-off between energy and cost requirements. This algorithm includes a two-layer solution representation to achieve full coverage of the solution space and a new decoding mechanism with idle time reduction. A collaborative initialization as the first stage of the algorithm is extended to get high-quality and great-diversity initial solutions. A self-learning evolution for each sub-population with four problem-specific evolutionary operators is developed to explore task or worker assignment sequences, and a dual-cooperation strategy is proposed to enhance the interaction between sub-populations. The final experiments, based on 269 instances, demonstrate that the improvement components are effective and the proposed algorithm is superior to seven latest multi-objective evolutionary algorithms from numerical, statistical and differential analyses. • Define the MALBP-MW by a MILP model to minimize the energy and cost requirements. • Design a new decoding with idle time reduction and a collaborative initialization. • Propose four problem-specific evolutionary operators for MOCEA. • Develop MOCEA with a self-learning selection and dual-cooperation. [ABSTRACT FROM AUTHOR]

In this article, a double strategy is proposed to find the optimal gains of a cascaded PI controller to minimize the trajectory position error in a five-bar parallel robot. The first strategy employs Differential Evolution to tune constant gains during the execution time of the desired trajectory. Once Differential Evolution achieves convergence on the solution by finding the vector of optimal gains that minimize the position tracking error, all the position error data and current of the two brushless motors are saved. In the second strategy, the data generated in the first strategy is used to train a Deep Neural Network. After that, the trained Deep Neural Network replaces the constant gains of the first strategy with time-varying gains for the desired trajectory. Three working scenarios are proposed to test the generalization of the Deep Neural Network. In the first scenario, a training trajectory is executed. In the second one, a testing trajectory of the Deep Neural Network is evaluated. In the third one, a mass change is generated in the middle of the cycle. The results show that the Deep Neural Network is robust to different trajectories and mass changes during the execution of pick and place tasks. [ABSTRACT FROM AUTHOR]

Oversampling method is used to solve the class imbalanced issues. Some existing oversampling methods do not well remove noisy samples and avoid synthesizing noisy samples. Therefore, we propose a new oversampling approach based differential evolution on the safe set for highly imbalanced datasets (SS_DEBOHID). SS_DEBOHID firstly uses k -nearest neighbors (kNN) method to learn the safe area of minority; then the DEBOHID oversampling method is used to synthesize new minority samples in the safe area. The advantages of SS_DEBOHID include that (a) it generates samples in the safe area to reduce generation of noisy samples and reduce synthetic samples falling into the classification boundary and majority area; (b) it uses the DEBOHID method to synthesize samples and increase the diversity of samples; (c) the method is suitable for highly imbalanced datasets. The proposed method is compared with 10 methods on 43 highly imbalanced datasets and evaluated on AUC and G_Mean metrics. The experimental results show that SS_DEBOHID obtains more than 30 best performing datasets on KNN, SVM, and DT classifiers in terms of AUC and G_mean, respectively. The proposed method outperforms other methods by 8.07% to 24.34% on average AUC metric and by at least 6.96% and up to 45.37% on average G_mean metric. In addition, we validate the efficiency of SS_DEBOHID on 8 high-dimensional and large sample size datasets. The experimental results show that SS_DEBOHID has better classification performance and robustness. • SS_DEBOHID improves diversity of synthesized samples and reduces noisy samples. • SS_DEBOHID uses the basic strategy of DE to generate samples in the safe area. • SS_DEBOHID is better than 10 methods on 43 highly imbalanced datasets. [ABSTRACT FROM AUTHOR]

It is well known that the decision-making on treating gastric cancer is usually the summary of several experts' advice. Moreover, the interpretability and reliability of a model used to assist doctors with final decisions are very important in gastric cancer. Thus, this paper designed an ensemble belief rule base (EnBRB) model to ensemble multiple BRB models and predict the statement of pathological complete response (pCR), aiming to simulate the expert consultation widely used for clinical decisions on gastric cancer. In EnBRB, five BRB models were built individually using experts' knowledge and trained through patient treatment information. Furthermore, the final output was calculated via the evidential reasoning (ER) based ensemble strategy on the results of each BRB for a more reliable decision. Then, to improve the model's performance, a two-stage approach with a differential evolution (DE) algorithm was designed to enhance the performance of EnBRB. The experimental results demonstrated that a higher accuracy (0. 9296 ± 0. 0521) and AUC (0. 9570 ± 0. 0368) were obtained by our EnBRB. Moreover, the difference between sensitivity and specificity achieved by EnBRB was smaller than other comparison methods. [ABSTRACT FROM AUTHOR]

• An improved algorithm NAS-GWO proposed for agricultural UAV trajectory planning. • Using nonlinear parameters to balance the algorithm's local and global search. • Combining Gaussian mutation and spiral function aids algorithms skip local minima. • Improve the position utilization of optimal individuals by boundary constraints. • NAS-GWO's trajectory planning was excellent in four different scale mission tests. Unmanned aerial vehicle (UAV) trajectory planning plays an essential role in agricultural production and biological control. To solve the agricultural UAV trajectory planning problem, a multi-mechanism collaborative improved grey wolf optimization algorithm (NAS-GWO) is proposed. In NAS-GWO, the evolutionary boundary constraint processing mechanism is introduced to update the position of the grey wolf individuals that cross the boundary in time to retain the position information of the optimal individuals to the largest degree to enhance the search accuracy of the algorithm. Then, the Gaussian mutation strategy and spiral function are used as perturbation mechanisms to help the algorithm jump out of the local optimum in time to strengthen the exploitation capability of NAS-GWO. Meanwhile, the improved Sigmoid function is used as a nonlinear convergence factor for balancing the exploitation and exploration of the NAS-GWO. By comparing NAS-GWO with ten advanced metaheuristic algorithms on 20 CEC2017 benchmark functions, the experimental results show that the NAS-GWO algorithm has superior merit seeking and robustness. Moreover, the agricultural UAV trajectory planning problem is solved using NAS-GWO. The experimental results show that the NAS-GWO algorithm plans a more viable and stable trajectory path in four different scale missions, while the most important is that it requires less cost. Among them, the algorithm reduces 27.93%, 38.15%, 32.32%, 34.11%, 10.63%, and 13.48% on average in cost function values compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Aquila Optimizer (AO), Differential Evolution (DE), Dung Beetle Optimizer (DBO), and Grey Wolf Optimization algorithm (GWO), thus proving the effectiveness and significance of NAS-GWO in the agricultural UAV trajectory path planning problem. [ABSTRACT FROM AUTHOR]

Fault section diagnosis (FSD) is extremely critical to ensure the safe and stable operation of power systems. Neural networks have become popular increasingly in solving this problem and the existing applied ones rely primarily on the traditional BP and RBF neural networks. However, their generalization is weak when diagnosing large-scale power systems. To address this issue, this study suggests a divisional FSD method based on structure adaptive hierarchical extreme learning machines (HELM). To construct an optimal HELM-based sub-module diagnosis model for each subsystem of a large-scale power system, differential evolution is applied to optimize both the number of hidden layer neurons and the regularization factor of HELM simultaneously. The optimal HELM is responsible for completing both the diagnosis of internal power sections of the subsystem and the interim diagnosis of tie lines of adjacent subsystems. Based on the interim diagnosis results, Choquet fuzzy integral is used to fuse them to obtain the final results for the tie lines. Simulation results indicate that the differential evolution algorithm is effective in finding more accurate and compact HELMs with better approximation ability and generalization performance. The proposed FSD method has higher accuracy and fault tolerance to identify various complex fault cases. [ABSTRACT FROM AUTHOR]

To adapt to the uncertainty of the real environment and solve the problem of high efficiency and high quality, this study comprehensively considers a variety of uncertainty factors, focusing on modeling and solving a hybrid flow-shop scheduling problem by two-stage stochastic programming (HFSP-TSP). First, this paper uses a two-stage scenario tree to describe the uncertain factors, the probabilities and values are obtained under different discrete production scenarios, and stochastic programming theory is used to formulate a mixed-integer linear programming (MILP) model for HFSP-TSP. Second, based on a general discrete optimization algorithm framework, that is, pointer-based discrete differential evolution (PDDE) algorithm, a novel variant (H-PDDE) is proposed for effectively solving the HFSP-TSP. It adopts a permutation scheduling decoding method and introduces three new improvement strategies: the strategy of inconsistent code length of individuals in the population, adding local search in discrete mutation operation, using a heuristic algorithm to generate the initial population. Finally, computational experiments were conducted to illustrate the proposed mathematical model and optimization algorithm. The computational results show that the H-PDDE can solve the HFSP-TSP more effectively than existing algorithms and conventional PDDE variants. The stochastic programming model is increasingly superior to the deterministic model in an uncertain environment. The source code of the H-PDDE algorithm and its data files can be found at https://github.com/huangyiping-ai/H-PDDE-algorithm.git , which is publicly available. [ABSTRACT FROM AUTHOR]

A computational technique called opposition-based learning (OBL) has gained a lot of attention in the field of soft computing. Among the different forms of OBL, the iBetaCOBL variant, which is a stochastic OBL, has shown the best results in improving the performance of differential evolution (DE). However, this approach may not be effective in handling complex problems as it is susceptible to rotations in the coordinate system. To address this issue, we present a new and improved version of iBetaCOBL called iBetaCOBL-eig. This new technique uses an eigenvector-based multiple exponential crossover operator in the partial dimensional change method, making it rotationally invariant. We conducted experiments to evaluate the performance enhancements of DE using iBetaCOBL-eig on 29 challenging benchmark problems. Our results showed that the new algorithm is able to outperform not only ten strong OBL variants but also its previous version iBetaCOBL. • A rotationally invariant stochastic OBL called iBetaCOBL-eig is introduced. • iBetaCOBL-eig can direct the distribution of solutions toward the global optimum. • iBetaCOBL-eig can speed up the convergence of differential evolution variants. [ABSTRACT FROM AUTHOR]

Color Quantization (CQ) is a complex and hard problem because selecting the best set of colors from many available colors and using that set to obtain a good quality image is an NP-complete problem. The use of evolutionary computation and swarm-based methods to solve search and optimization problems has increased dramatically in recent years. This article compares some of these methods in order to solve the CQ problem. The following methods were used to generate CQ images: Particle swarm optimization, Artificial bee colony, Adaptive differential evolution, Success-history based adaptive differential evolution (with and without linear population size reduction), Cuckoo search, Firefly algorithm and Shuffled-frog leaping algorithm. For the first two methods, two variants were considered. Thus, a total of ten metaheuristics were compared with four classical CQ methods (Variance-based, Median-cut, Binary splitting and Wu's methods) applying them to a set of benchmark images and considering four different palette sizes (32, 64, 128, and 256 colors). Three error measures were considered to compare the methods: the mean squared error, the mean absolute error and the peak signal-to-noise ratio. Some of the swarm-based methods analyzed include a recently proposed CQ method using ants. Although they have a slow computational speed in the experimental studies, the ant-based methods are significantly better than all other methods according to the Wilcoxon signed rank test. In general, despite their speed, classical methods underperform the other ten methods both qualitatively and quantitatively. • Population-based methods generate better images than classical methods. • Ant-tree heuristic based color quantization yield high quality images. • Particle swarm optimization plus artificial ants outperforms the other methods. • Some good differential evolution variants do not outperform swarm-based methods. • Compared differential evolution-based methods generate similar quality images. [ABSTRACT FROM AUTHOR]

Lithology identification is a necessary task for activities of reservoir evaluation and underground engineering construction. Recently, the intelligent lithology identification method based on deep learning of image has attracted more and more attention with its excellent performance. However, the existing models are not developed specifically for lithology identification, and there is still room for improving accuracy and a large number of redundant calculations during inference. Therefore, it is necessary to develop a lightweight model specifically for lithologic identification. In order to realize accurate, rapid and effective lithology identification, we propose a novel integrated strategy of easy pruning, parameter searching, and reparameterization for lightweight intelligent lithology identification. Firstly, a model dedicated to lithology classification and easy to pruning is constructed by using the multi-branch structure; secondly, a pruning parameter search method based on differential evolution of expressivity is proposed; and finally, the model is further compressed using reparameterization. We validate the entire method using 160 lithologies. P3DNet has an accuracy of 97.69% on 160 lithologies. The pruning method based on differential evolution of expressivity can realize the optimal pruning under the condition of predetermined accuracy. The P3DNet with 134,380 parameters can achieve an accuracy of 97.50% on 160 lithologies, and it takes only 3.84 ms for the NVIDIA GeForce GTX 2080 Ti GPU to infer an image with a resolution of 224 × 224 × 3. The pruned P3DNet is 39.54% faster than MobileNet v3 and 50.65% faster than ShuffleNet V2. Our method can significantly reduce the requirements of the image-based identification method on device memory, computing power, etc., and greatly expand the application scenarios of the intelligent identification method. Relevant research can provide strong support for rapid and intelligent identification of lithology in site survey and construction. [ABSTRACT FROM AUTHOR]

Biopsy tests used in the identification and confirmation of breast cancer are time-consuming and complex. Thus, neural networks can be applied to aid specialists with a prone to be in local minima, which can be avoided using evolutionary algorithms. In that way, this work analyzed the methods of genetic algorithm, differential evolution, differential evolution with islands and migration, particle swarm optimization, and adaptive particle swarm optimization in the training of neural networks for the classification of histological images stained by hematoxylin-eosin. The differential evolution with islands and migration and particle swarm optimization presented the most promising results, with the first one reaching a maximum AUC of 0.70 for the color features, and the last one with a maximum AUC of 0.71 for the texture attributes, on the evaluated dataset. Through this proposal, we have an important contribution to breast cancer histological image classification that also allows the development of new studies in the future. • Definition of diagnosis of breast cancer by images is timeconsuming and complex. • Artificial neural networks can be used to aid specialists in this task. • Evolutionary algorithms were not yet explored in the literature for that. • We analyzed the use of these techniques for classifying breast cancer images. • The performed experiments demonstrate the contribution of the proposed method. [ABSTRACT FROM AUTHOR]

This research studies the Share-a-Ride Problem (SARP) in which a set of taxis is used to serve a set of package and passenger requests at the same time. The goal is to maximize the profit from serving the requests without violating given constraints. We develop a new matheuristic algorithm that combines the simulated annealing with mutation strategy (SAMS) and the set partitioning (SP) approach to improve the reported solutions of SARP benchmark instances. The SAMS consists of a time-slack strategy, neighborhood moves, a mutation strategy that depends on the time-slack strategy, and a penalty mechanism for infeasible solutions. The first phase of the proposed matheuristic uses SAMS to generate a set of feasible candidate routes. A route accumulation mechanism is added to the SAMS to keep the candidate routes in the route pool. The second phase of the matheuristic focuses on solving the set partitioning model to find the best route combination as the final solution. The proposed matheuristic is tested on existing small and large SARP instances, and a set of newly generated large SARP instances and then compared with SAMS algorithm. The experimental results show that the proposed matheuristic obtains optimal solutions to all the small SARP benchmark instances. For large instances, the proposed matheuristic outperforms SAMS algorithm as it obtains several new best solutions to SARP. Lastly, our algorithm obtains high-quality solutions to the new large SARP instances generated in this study. [ABSTRACT FROM AUTHOR]

A critical issue of constrained optimization evolutionary algorithms (COEAs) is how to balance the minimization of the objective function and the reduction of the constraint violation during the optimization process. This paper proposes a differential evolution (DE) algorithm with an adaptive penalty coefficient mechanism and a search history exploitation mechanism (called APCSH) to address this issue. Specifically, APCSH includes a learning stage and an evolving stage. In the learning stage, the correlation between the constraint violation and objective function values is calculated. Then, in the evolving stage, the correlation is used with a sigmoid function to determine the upper and lower bounds of the penalty coefficient, followed by a ranking strategy to assign the penalty coefficient to each solution. Furthermore, an exponentially weighted average method is used to update the search direction of solutions to promote convergence. Additionally, the scaling factor and crossover rate of DE are tuned automatically based on the successful search history. Many numerical experiments are conducted on 18 CEC2010 benchmark problems, 28 CEC2017 benchmark problems, and 4 heat pipe constraint optimization problems to demonstrate the advantages of the proposed algorithm over some state-of-the-art COEAs. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

Reducing the number of lower-level function evaluations (LFEs) is crucial to designing a bilevel evolutionary algorithm (BLEA) for bilevel optimization. Direct neighbor solution transfer (DST) is regularly used in BLEA because it can improve the lower-level search efficiency and reduce the number of LFEs. But this method still faces the challenge of misleading searches to make the algorithm converge to the wrong optimum. This paper proposes a bilevel improved multi-operator differential evolution algorithm (BL-IMODE) for bilevel optimization, which uses the knowledge transfer and adaptive coordinate systems approach. Specifically, this paper proposes a lower-level search knowledge transfer (LLS-KT) and a lower-level (LL) optimal solution distribution information transfer (LLO-DIT) mechanism to implement knowledge transfer. In LLS-KT, transferring LL solutions to the LL optimizer is similar to DST, but the LLS-KT transfers multiple solutions and thus has less possibility of misleading searches than DST. Moreover, the LLO-DIT transfers the LL optimal solution distribution information to the LL optimizer for generating promising offspring solutions. Since this method does not directly transfer LL solutions, it avoids misleading searches. Additionally, the adaptive coordinate systems (ACS) approach is used in two-level optimizers to suit different fitness landscapes and further reduce the number of LFEs. In ACS, three differential evolution mutation operators are produced in two coordinate systems. An adaptive strategy is used to select the suitable differential evolution mutation operator to generate promising offspring solutions in the Eigen coordinate or original coordinate system. Experimental studies on three test suites benchmark problems for comparison with seven state-of-the-art algorithms are conducted. The results show BL-IMODE has a significant advantage over the comparative algorithms. Finally, BL-IMODE is applied to tackle the inverse optimal control problem. • We propose a bilevel improved multi-operator differential evolution algorithm. • We propose a knowledge transfer approach to avoid misleading search. • We propose an adaptive coordinate systems approach to improve search efficiency. • We outperform all existing approaches in most test instances. [ABSTRACT FROM AUTHOR]

Multi-objective economic emission dispatch (MOEED) is not only a hotspot issue for emission reduction, but also one of the fundamental problems for optimal operation of power systems. With the increasing scale of solar energy, the uncertainty of solar power brings intractable challenges to the power system operation. In this work, a MOEED model considering the uncertainty of solar power is proposed. Both scenarios of underestimation and overestimation of solar power are modeled and penalized in the form of operating cost. To solve this model effectively, a multi-objective method by extending our quadratic interpolation learning differential evolution (QILDE) is developed. The core is to redefine the quadratic interpolation from single-objective optimization to adapt to multi-objective problems. Besides, some multi-objective processing techniques including feasible solution technique, non-dominated sorting, summation-based sorting and diversified selection method, and fuzzy decision technique are integrated. Simulation results on 20 CEC 2020 multi-objective functions and a modified IEEE 30-bus system verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]