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This work focuses on minimum-time low-thrust orbit transfers from a prescribed low Earth orbit to a specified low lunar orbit. The well-established indirect formulation of minimum-time orbit transfers is extended to a multibody dynamical framework, with initial and final orbits around two distinct primaries. To do this, different representations, useful for describing orbit dynamics, are introduced, i.e., modified equinoctial elements (MEE) and Cartesian coordinates (CC). Use of two sets of MEE, relative to either Earth or Moon, allows simple writing of the boundary conditions about the two celestial bodies, but requires the formulation of a multiple-arc trajectory optimization problem, including two legs: (a) geocentric leg and (b) selenocentric leg. In the numerical solution process, the transition between the two MEE representations uses CC, which play the role of convenient intermediate, matching variables. The multiple-arc formulation at hand leads to identifying a set of intermediate necessary conditions for optimality, at the transition between the two legs. This research proves that a closed-form solution to these intermediate conditions exists, leveraging implicit costate transformation. As a result, the parameter set for an indirect algorithm retains the reduced size of the typical set associated with a single-arc optimization problem. The indirect heuristic technique, based on the joint use of the necessary conditions and a heuristic algorithm (i.e., differential evolution in this study) is proposed as the numerical solution method, together with the definition of a layered fitness function, aimed at facilitating convergence. The minimum-time trajectory of interest is sought in a high-fidelity dynamical framework, with the use of planetary ephemeris and the inclusion of the simultaneous gravitational action of Sun, Earth, and Moon, along the entire transfer path. The numerical results unequivocally prove that the approach developed in this research is effective for determining minimum-time low-thrust Earth–Moon orbit transfers. • Earth–Moon minimum-time low-thrust orbit transfers are investigated. • Problem is formulated as a multiple-arc optimal control problem. • Costate transformations are used, related to different state representations. • Indirect heuristic algorithm with stratified objectives is employed. • Numerical results prove effectiveness of the optimization methodology. [ABSTRACT FROM AUTHOR]

The space-based gravitational wave observatory mission consists of three drag-free controlled spacecraft, which form an equilateral triangle configuration. Due to the injection error, the configuration will deviate from the originally designed one, and the injection configuration cannot keep stability for a long term, which will affect the performance of the gravitational wave detection. In this study, we propose a method for reconfiguration using multiple-revolution Lambert solutions. In addition, assuming that the real-time data of spacecraft's motion states can be obtained with high accuracy and the propulsion system can ensure the magnitude and accuracy of maneuvers, and taking the cost of reconfiguration and configuration stability requirements into account, a novel method of redesigning configuration based on differential evolution algorithm is proposed. Finally, the numerical simulations show that the redesigned configuration not only can reduce the maneuver cost significantly, but also can meet the requirements of long-term stability requirements. [ABSTRACT FROM AUTHOR]

In the 2016 International Astronautical Congress, SpaceX's Mars immigration plan was first formally proposed alongside a fully-reusable transportation infrastructure. In the 12th edition of the China Trajectory Optimization Competition (CTOC-12) held in 2022, a Mars transportation trajectory design problem using reusable transporters from a parking distant retrograde orbit (DRO) in the Earth–Moon system was released. It is expected to transport as many immigrants as possible using a maximum of 50 transporters within a total of 20 years. The BIT-DFH-BJUT team reported a solution that can deliver 9080 immigrants to Mars, which ranked first in the competition. In this paper, the methods and results from the winning team are presented, primarily including an overall analysis, underlying round-trip trajectory design, and top-level scheduling. Specifically, a round-trip is divided into four phases, leaving the DRO to man-boarding at the perigee, Earth–Mars interplanetary transfer, Mars's return to the Earth, and return to the DRO. An overall optimization framework is constructed by synthesizing techniques such as data set creation and patching, differential evolution, nonlinear programming, greedy algorithm, and mixed-integer programming. Finally, we outline the final solution of our team and compare the results with those from the top five teams. This competition demonstrates that a large-scale Mars immigration plan is possible by using reusable transporters from the Earth–Moon system. • Trajectory designing and optimization problem for large-scale Mars immigration is established. • The practical designing and optimization methods are presented, including underlying round-trip trajectory design, and top-level scheduling. • The solution that can deliver 9080 immigrants to Mars is demonstrated. [ABSTRACT FROM AUTHOR]

• An approximate optimization model for perturbed orbit rendezvous is obtained via a quasi-optimal thrust strategy. • A fast process to judge the low-thrust accessibility and obtain the parameters of thrust is proposed to reduce the dimensionality of the optimization model and improve the efficiency. • A fast analytical correction process is proposed to modify the solution by the numerical errors between the predicted orbit and target orbit. Trajectory optimization of low-thrust perturbed orbit rendezvous is a crucial technology for space missions in low Earth orbits, which is difficult to solve due to its initial value sensitivity, especially when the transfer trajectory has many revolutions. This paper investigated the time-fixed perturbed orbit rendezvous between low-eccentricity orbits and proposed a priori quasi-optimal thrust strategy to simplify the problem into a parametric optimization problem, which significantly reduces the complexity. The optimal trajectory is divided into three stages including transfer to a certain intermediate orbit, thrust-off drifting and transfer from intermediate orbit to the target orbit. In the two transfer stages, the spacecraft is assumed to use a parametric law of thrust. Then, the optimization model can be then obtained using very few unknowns. Finally, a differential evolution algorithm is adopted to solve the simplified optimization model and an analytical correction process is proposed to eliminate the numerical errors. Simulation results and comparisons with previous methods proved this new method's efficiency and high precision for low-eccentricity orbits. The method can be well applied to premilitary analysis and high-precision trajectory optimization of missions such as in-orbit service and active debris removal in low Earth orbits. [ABSTRACT FROM AUTHOR]

• An energy efficiency perspective to improve the performance sustainability in building retrofitting projects is offered. • A novel maintenance plan optimization is developed to find proper maintenance intensities and instants simultaneously. • The optimal maintenance control is transformed into a parametric optimization by impulsive and switched system modelling. • An improved DE algorithm is employed as a numerical solver to deal with the non-analytic components in the sub-objectives. The maintenance time schedule optimization into the investment budget decision for building energy retrofitting and maintenance planning is incorporated. The aggregate performance and population dynamics of the totality of retrofitted items is described by an impulsive switched system model. The items are switched to different working states by maintenance actions with impulsive effects. Considering the retrofitting project with deterioration. The objective of optimal maintenance control is to maximize the overall energy savings and financial payback. The optimal maintenance control problem is showed to be feasible and bounded. The maintenance time schedule and intensities are simultaneously optimized by an improved differential evolution (DE) algorithm. Compared with the fixed maintenance time schedule, the proposed approach is validated by simulation results, specifically, 3.36% of energy saving and 23.09% of internal rate of return (IRR) can be improved. [ABSTRACT FROM AUTHOR]

A pseudo random number generator (PRNG) is built with a chaotic map without fixed points implemented using fixed-point arithmetic. The generated binary sequences by the PRNG pass all the TestU01 statistical tests to prove the randomness of the generated binary sequences. Three multimodal benchmark functions are optimized with the heuristic Differential Evolution (DE) using the proposed PRNG, and these are compared with the DE using the standard random function available in the C programming language. If the shuffling technique is added to the generated chaotic random sequences, both approaches give the same statistically solutions. A hardware design in FPGA of the PRNG also is shown. • A PRNG is designed with a chaotic map without fixed points. • The PRNG is tested with the heuristic differential evolution. • A hardware design in FPGA is shown and compared with the state of the art designs. [ABSTRACT FROM AUTHOR]

• A time-varying external archive differential evolution can obtain multiple roots for system of nonlinear equations. • A judgment criterion dividing individuals into the ordinary, local and global optimal subpopulations is proposed. • A new difference mutation vector is used to ensure the diversity of the population. • The generality, computation efficiency, and applicability of the proposed method are verified. In this work, a time-varying external archive differential evolution algorithm is proposed, which can obtain the multiple roots for system of nonlinear equations for the forward displacement of parallel mechanisms. In time-varying external archive differential evolution three improvements are presented. At first, a time-varying subpopulation strategy is proposed to consider both global and local searching capabilities. Then, an individual judgment criterion is proposed, which employs the weighted basis vector mutation strategy for ordinary individuals and a hybrid mutation operator for locally optimal individuals. Finally, the external archive strategy of neighborhood-based crowding differential evolution is improved to enhance the domain searching ability of the algorithm. To evaluate the performance of time-varying external archive differential evolution, twenty benchmark functions are selected as the test suite. Moreover, the solutions for forward displacements of four strongly coupled parallel mechanisms with different degrees of freedom are derived to test the generality of this method. From the acquired experimental results, it is demonstrated that fairly-competitive results can be provided compared to other methods, in both benchmark functions and forward displacement solutions of parallel mechanisms. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new application of WDE for estimation of nonlinear Hammerstein-Wiener systems. • Designed WDE is implemented for measured input, internal and output data viably. • The sundry scenarios of HWM establish robustness, stability and convergence of WDE. • Statistical observations prove efficacy for controlled autoregressive scenarios. Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability. The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters. The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials. Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme. Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios. [ABSTRACT FROM AUTHOR]

One of the industries with the fastest growth rates worldwide, and notably in Thailand, is medical tourism. With connections to Cambodia and Laos, Ubon Ratchathani is located in lower northeastern Thailand, close to Vietnam and Myanmar. Therefore, there is a significant chance that this region will welcome medical travelers. High-quality medical facilities are available in Ubon Ratchathani to fulfill the needs of medical tourists. A visitor's decision to travel to Ubon Ratchathani for medical treatment is influenced by factors other than the high-level medical facilities, such as lodging, accessibility to public transportation, and tourist attractions. The public transportation services in Ubon Ratchathani, especially the public bus system, are poorly designed and may let down visitors. The purpose of this study is to develop a smart public bus route design that will meet tourists' demands. The concept of open innovation will be utilized to develop the model. We surveyed 400 visitors to Ubon Ratchathani. The tourists' opinions and views of public transportation will be made public and used as an input parameter when designing bus routes. The bus route can then be constructed using the differential evolution algorithm (DE). A web-based smart public transportation system was built. In order to construct an efficient smart public bus system (SPBS), open innovation was used in the development phase. According to the computational results, the new routes using DE lead to a 5.97% reduction in travel distance when compared to the output of the more well-known genetic method. More than 98.5% of visitors are satisfied with the new routes, and once they start running, 99.5% of all respondents plan to use public transit. [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]

• The Prophet model is used to forecast wind and photovoltaic power generation. • An Upgraded Kernel Search Optimization (UKSO) is proposed. • A Two Time Energy Dispatch model is developed for joint scheduling. • More competitive results are offered by UKSO in dynamic power dispatch problems. Integrating renewable energy sources, such as wind and photovoltaic power generation, into the power grid is crucial for sustainable power system development and mitigating pollutant emissions. However, these sources' inherent uncertainty and randomness pose significant challenges to grid operations. Metaheuristic algorithms offer efficient solutions to optimization problems in this context and are widely employed in practice. Kernel Search Optimization (KSO) has emerged as a prominent metaheuristic algorithm due to its parameter-free nature and applicability to power dispatch problems. Nevertheless, KSO's limited local search capabilities necessitate enhancements for improved performance. This paper introduces an enhanced variant of KSO, termed Upgraded Kernel Search Optimization (UKSO), which incorporates differential evolution techniques, including mutation, crossover, and selection mechanisms, to bolster KSO's search capabilities and overall performance. The efficacy and feasibility of UKSO are evaluated through comprehensive testing using the CEC2017 benchmark. Comparative analysis demonstrates that UKSO outperforms other algorithms by achieving more optimal solutions. To address power dispatch challenges in the context of renewable energy sources, a Two-Time Energy Dispatch (TTED) model is proposed, leveraging a weighted summation approach to minimize total fuel costs and pollution emissions simultaneously. The application of the Prophet model, based on Bayesian fitting, facilitates accurate prediction of wind and photovoltaic power outputs. Through experimentation across small (8 and 14 units) and large (58 units) systems, the feasibility of UKSO and TTED is validated. In complex scenarios, UKSO demonstrates a 0.69% to 3.64% reduction in pollution emissions and a 0.99% to 1.23% decrease in economic costs compared to KSO under varying weight configurations. Furthermore, UKSO achieves a renewable energy generation utilization rate exceeding 90%, emphasizing its ability to iterate efficiently through complex problems and its efficacy in addressing renewable power dispatch challenges. These results highlight the practical significance of UKSO in optimizing power systems and provide valuable insights for future research and applications in this domain. [ABSTRACT FROM AUTHOR]

This study presents a machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions heavily reliant on groundwater irrigation. The framework utilizes a comprehensive set of predictive factors, including meteorological, hydrological, and human activity data. An optimal combination of input variables and their temporal delays was determined using a novel selection method. To address overfitting, a mathematical model for hyperparameter optimization was developed, leveraging sample subset cross-validation and an improved differential evolution algorithm. Numerical experiments on the YingGuo region in the Huaihe River Basin demonstrated that the hyperparameter optimization resulted in an 11.6%–38.5% increase in the Nash-Sutcliffe Efficiency (NSE) indicator. Additionally, fine-tuned temporal scales, from monthly to five-day resolution, significantly improved predictive performance, with NSE increasing from 0.629 to 0.952 (33.9% enhancement). However, longer forecasting horizons led to a 29.4% reduction in NSE. The study also implemented a multi-core parallel computing framework, which achieved a 15.35-fold improvement in computational efficiency while maintaining predictive precision. The integration of external factors enhanced the predictive performance across various observation wells. These findings contribute to a better understanding of groundwater dynamics and highlight the potential of machine learning models in improving groundwater depth predictions in agricultural regions with high reliance on groundwater irrigation. [Display omitted] • Achieving accurate predictions with optimal combination of input variables and delays. • Overfitting reduced by optimizing hyperparameters, NSE increase: 11.6%–38.5%. • Enhanced efficiency with multi-core parallel computing: 15.35-fold speedup. • Fine-tuned time scales boost performance: Monthly to Five-day, NSE increase: 33.9%. • Decreased accuracy for longer horizons: NSE reduced by 29.4% at Five-day time scale. [ABSTRACT FROM AUTHOR]

Shovel attachment is one of the most commonly used working attachments in hydraulic excavators. Its mechanical design remains a challenging optimization problem. To tackle this issue, the optimizing model of ordinary shovel attachment is firstly established which contains twenty-three design variables, six objective functions, and sixty-three constraints. Then, an improved decomposition-based constrained many-objective evolution algorithm is proposed, which integrates advanced push & pull search constraint handling technique and differential evolution operator to deal with the optimization problems of shovel attachment. Finally, the proposed algorithm has demonstrated its superior optimization ability for designing shovel attachments through the case studies of a 70-ton face-shovel hydraulic excavator, outperforming seventeen state-of-the-art constrained multiobjective evolutionary algorithms. Furthermore, the first quantitative comparison between ordinary shovel attachment and TriPower shovel attachment under the same main machine is presented by the optimized results. The result demonstrates the complexity of the optimal design for shovel attachment and the effectiveness of multiobjective evolutionary algorithm. • Constrained many-objective optimization model of ordinary shovel attachment is established for the first time. • A decomposition-based constrained many-objective differential evolution algorithm is proposed. • Ordinary and TriPower shovel attachment are compared on the same main machine for the first time. [ABSTRACT FROM AUTHOR]

During the past two decades, evolutionary algorithms have seen great achievements in solving complex optimization problems owing to the advantages brought by their properties, especially constrained multi-objective optimization problems (CMOPs) with multiple conflicting objective functions and constraints which widely exist in industry, scientific research, and daily life. Among the real-world CMOPs, mechanical design problems (MDPs) from the industry widely exist and are important, while unfortunately, most constrained multi-objective evolutionary algorithms (CMOEAs), developed based on benchmark CMOPs, neglect the specific features and challenges of MDPs and thus cannot solve them well to provide the practitioners promising Pareto optimal solutions for decision making. To overcome this limitation, this paper analyzes the features and challenges of MDPs, including badly scaled objective space, decision space properties, and decision variable linkages. Then, we propose a new CMOEA named CMORWMDP. First, instead of the homogeneous operator in existing CMOEAs, a heterogeneous operator strategy is adopted to use the operator of Genetic Algorithm to enhance the convergence and the operator of Differential Evolution to tackle variable linkages. In addition, an improved fitness function that considers normalization is designed for environmental and mating selections. The proposed algorithm is simple, parameter-free, and easy to implement. Experiments on 21 real-world MDPs show its superiority compared to 20 state-of-the-art CMOEAs under the Friedman test and Wilcoxon test on different metrics, demonstrating the effectiveness of the heterogeneous operator and normalization-based fitness for selections for real-world MDPs. Moreover, the effectiveness of the proposed algorithm in solving other real-world CMOPs is also verified, revealing that our methods are very promising in tackling real-world problems. • Challenges of real-world constrained multi-objective mechanical design problems. • Heterogeneous operator strategy to tackle the challenges. • Improved fitness function with normalization. • A parameter-free constrained multi-objective optimization evolutionary algorithm. [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]

Designing efficient heat-exchanger networks is essential for the effective use of energy in the process industries, fostering sustainable and low-carbon development. Traditional heuristic algorithms often fail to balance global and local search processes adequately when solving heat exchanger network synthesis. This frequently leads to early structural fixation and limits the exploration of integer variable search spaces. This study presents an innovative structural enhancement approach, incorporating a dynamic penalty for utility operating costs and an adaptive evolution strategy. The developed method increases the number of heat exchangers by dynamically penalizing utility operating costs and allows evolutionary parameters to be adaptively adjusted based on historical solutions. This approach diminishes the dependence on manual parameter tuning while enhancing flow matching diversity. Moreover, novel metrics for assessing structural diversity, based on node-based nonstructural model with stream splitting are established to facilitate the transition between various optimization paths, thereby improving the algorithm's robustness throughout different stages of the optimization process. Finally, computational analysis on five industrial-scale cases yields more optimal solutions with varied structural configurations compared to previous studies, demonstrating the method's feasibility and effectiveness. [Display omitted] • A dynamic penalty strategy is designed to disturb the current structure. • The adaptive evolutionary strategy is proposed to adjust evolutionary parameters. • The proposed method benefits for the structural evolution. • Multiple industrial-scale cases have validated the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Currently, how to effectively control carbon emissions is a global concern. Accurate carbon price prediction can help us more effectively control carbon emissions and reduce environmental pollution. Due to the complexity of carbon valence, this study proposes an improved decomposition ensemble prediction model. Firstly, a novel signal decomposition algorithm is designed to adaptively determine the number of decomposition modes for carbon price sequences, obtaining a set of intrinsic mode functions (IMFs). Secondly, a creative method for assessing the contribution of feature components is developed. It utilizes a genetic algorithm-based mutual-information method to calculate the contribution of each IMFs to the original sequence, classifying all feature components into high and low contribution components. Then, this study developed a differential learning method that partitions the contribution of feature components and applies targeted learning and integration using temporal convolutional network with different structures. Compared to traditional forecasting frameworks, this framework improves prediction performance while reducing structural complexity and computational costs. Empirical results demonstrate the superiority and robustness of the proposed model with the outcomes of this study present the performances that the proposed model outperforms the others with Guangzhou's mean absolute percentage error of 3.30313%, which are beneficial for governments and enterprises to predict adverse signals in the carbon market in a more timely manner, and to take measures in advance to maintain the stability of carbon emissions. [ABSTRACT FROM AUTHOR]

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high levels of chlorine and/or other organic acids that act as catalysts or precursors in PCDD/Fs formation. This study aims to predict PCDD/Fs emissions in peat soil using an artificial neural network (ANN) approach based on limited emission data and selected physico-chemical properties. The ANN's prediction performance is affected by uncertainties in its initial connection weights. To improve prediction performance, an optimisation algorithm, termed differential evolution (DE), is used to optimise the ANN's initial connection weights and bias. The study adopts several ANNs with fixed architecture to predict PCDD/Fs emissions, each consisting of a multilayer perceptron (MLP) with a backpropagation algorithm. Eight input variables and one output variable were adopted to train and test various neural network architectures using real-world datasets. The model optimisation procedure was conducted to ascertain the network architecture with the best predictive accuracy. The evolved ANN based on 5 hidden neurons, with the assistance of self-adaptive ensemble-based differential evolution with enhanced population sizing (SAEDE-EP), successfully produced the lowest MSE test (6.1790 × 10−3) and highest R2 (0.97447) based on the mean among the other HNs. An evolutionary-optimised ANN-based methodology is a viable solution to predict PCDD/Fs in peat soil. It is cost-effective for pollution control, environmental monitoring and capable of aiding authorities prevent PCDD/Fs exposure, e.g., during a fire. [Display omitted] • PCDD/Fs prediction model is built based on an evolutionary-optimised ANN. • Adopted limited datasets of PCDD/Fs emissions and physico-chemical of peat samples. • Differential evolutionary algorithm improved accuracy and error estimates. • Cost-effective solution for pollution and environmental monitoring. [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]

Particle swarm optimization (PSO) is one of the most classical metaheuristic algorithms that has gained significant attention since its inception. It has some inherent advantages, such as easy implementation, rapid convergence, low computational complexity and so on. However, the drawbacks of being prone to local optimization and insufficient diversity cannot be ignored. Therefore, a new multiple adaptive co-evolved particle swarm algorithm (ACEPSO) with adaptive population grouping strategy, pros-cons coevolution mechanism, new co-evolved mechanism and adaptive mutation strategy is proposed in this paper. Firstly, ACEPSO partitions the overall population into two distinct subpopulations: elite population and common population. The size of the subpopulations undergoes variations at different stages. Secondly, the introduced pros-cons coevolution mechanism effectively improves the exploration ability of PSO. Meanwhile, a new co-evolved mechanism is proposed here aiming to enhance population diversity and balance the exploration and exploitation ability. This mechanism can better transfer information between individuals and promote effective collaboration. Finally, an adaptive mutation strategy is introduced. It improves the population diversity and prevents the algorithm from falling into local optimality productively. To validate the outstanding performance of ACEPSO, this paper compares it with various state-of-the-art metaheuristic algorithms as well as their variants on CEC2017 and CEC2022 test sets. The results exhibit that ACEPSO has a standout comprehensive performance. In addition, ACEPSO is utilized to tackle a set of twelve engineering optimization problems as well as 2D robot path planning problems. On all these complex optimisation problems, ACEPSO obtains the relatively best results. All the above results manifest that ACEPSO has great advantages and competitiveness in solving some of the optimization problems. [ABSTRACT FROM AUTHOR]

Snow Ablation Optimizer (SAO) is a cutting-edge nature-inspired meta -heuristic technique that mimics the sublimation and melting processes of snow in its quest for optimal solution to complex problems. While SAO has demonstrated competitive performance in comparison to classical algorithms in early research, it still exhibits certain limitations including low convergence accuracy, a lack of population diversity, and premature convergence, particularly when addressing high-dimensional intricate challenges. To mitigate the above-mentioned adverse factors, this paper introduces a novel variant of SAO with featuring four enhancement strategies collectively referred as MSAO. Firstly, the good point set initialization strategy is employed to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain rapidly. Secondly, the greedy selection method is adopted to reserve better candidate solutions for the next iteration, thus striking a robust exploration–exploitation balance. Then, the Differential Evolution (DE) scheme is introduced to expand the search range and enhance the exploitation capability of the algorithm for higher convergence accuracy. Finally, to reduce the risk of falling into local optima, a Dynamic Lens Opposition-Based Learning (DLOBL) strategy is developed to operate on the current optimal solution dimension by dimension. With the blessing of these strategies, the optimization performance of MSAO is comprehensively improved. To comprehensively evaluate the optimization performance of MSAO, a series of numerical optimization experiments are conducted using the IEEE CEC2017 & CEC2022 test sets. In the IEEE CEC2017 experiments, the optimal crossover probability CR = 0.8 is determined and the effectiveness of each improvement strategy is ablatively verified. MSAO is compared with the basic SAO, various state-of-the-art optimizers, and CEC2017 champion algorithms in terms of solution accuracy, convergence speed, robustness, and scalability. In the IEEE CEC2022 experiments, MSAO is compared with some recently developed improved algorithms to further validate its superiority. The results demonstrate that MSAO has excellent overall optimization performance, with the smallest Friedman mean rankings of 1.66 and 1.25 on both test suites, respectively. In the majority of test cases, MSAO can provide more accurate and reliable solutions than other competitors. Furthermore, six realistic constrained engineering design challenges and one photovoltaic model parameter estimation issue are employed to demonstrate the practicality of MSAO. Our findings suggest that MSAO has excellent optimization capacity and broad application potential. [ABSTRACT FROM AUTHOR]

The design and selection of tools for mechanized excavations is a crucial topic in the field of tunneling. Chisel bits are commonly used tools for soft ground, with the required cutting force being a vital factor in tool design. However, current methods either lack accuracy or fail to consider all necessary parameters. To address this issue, a new cutting force prediction model was developed to improve response quality and consider the most effective parameters. 206 data points were collected from studies on rock cutting with different strength, including information such as Uniaxial compressive strength, Brazilian tensile strength, Depth, Width, Rake angle, and clearance angle. Five different algorithms were then used to optimize the Hyperparameters in the XGBoost method, including Grid Search, Random Search, Bayesian Algorithm, Differential Evolution, and Optuna. Results showed that while most algorithms provided appropriate responses, the Grid Search and Bayesian Algorithm methods were the most effective, with R2 values (in test and train) of 0.876 and 0.93 in GS and 0.872 and 0.926 in BO, respectively. Upon comparison of the two methods, it was discovered that the GS approach yields a superior solution; however, it is also more sensitive to tuning, requires more calculations, and takes longer to provide a solution. When assessing the newly presented approach against existing methods, it was noted that the Evans and Roxborough methods produced R2 values of 0.48 and 0.53, respectively, which are notably lower than those of the new method. Lastly, the parametric analysis revealed that the cutting force is primarily affected by the depth, width, and rake angle in that order. [ABSTRACT FROM AUTHOR]

• This study proposes a novel hybrid design method of steady-state throttling control schedules by combining an offline optimization phase and an online compensation phase. • In the offline optimization phase, a novel multi-objective differential evolution algorithm with neighborhood-based hierarchical mutation and cluster-based environment selection is first proposed to optimize the control schedules. The stratified selection method is then proposed to select the optimal and smooth control schedules. • In the online compensation phase, the sensitivity calculation is first employed to identify the variable geometry component most sensitive to engine performance, and the compensator based on the extreme gradient boosting method is then designed to adjust the control schedules online. • The comparison results demonstrate that the proposed method can optimize the engine performance under the premise of ensuring good thrust linearity and that the control schedules can adapt to performance degradation during online use. It is critical for variable cycle engine to design throttling control schedules that exploit its full steady-state performance potential. However, the increasing amount of variable geometry components makes it difficult to design the optimal control schedules, and the control schedules cannot adapt to the engine performance degradation. Therefore, a novel hybrid design method of steady-state throttling control schedules is proposed in this study by combining an offline optimization phase and an online compensation phase. In the offline optimization phase, a novel multi-objective differential evolution algorithm with neighborhood-based hierarchical mutation and cluster-based environment selection is first proposed to optimize the control schedules. The stratified selection method is then proposed to select the optimal and smooth control schedules. In the online compensation phase, the sensitivity calculation is first employed to identify the variable geometry component most sensitive to engine performance, and the compensator based on the extreme gradient boosting method is then designed to adjust the control schedules online. The hybrid design method is applied to the high-flow variable cycle engine. By comparing with the traditional design method, the results demonstrate that the proposed method can optimize the engine performance under the premise of ensuring good thrust linearity and that the control schedules can adapt to performance degradation during online use. [ABSTRACT FROM AUTHOR]

Repetitive construction project scheduling is a crucial aspect of modern construction project management. This study focuses on the scheduling of non-unit repetitive construction projects with non-serial activity groups, multiple crews, flexible or fixed sequences, and under correlated uncertainties. An integrated model has been developed by combining novel algorithms for non-unit repetitive project scheduling and probabilistic scheduling with correlated uncertainties, alongside evolutionary optimization algorithms (e.g., Differential Evolution (DE), Firefly Algorithm (FA), and the DEFA hybrid). The proposed model is capable of generating near-optimal or optimal schedules with minimal project duration under uncertainties and constraints of work continuity, thereby enhancing the reliability and efficiency of scheduling across various examples. This advancement provides project planners with a valuable tool to manage the complexities of repetitive construction project scheduling under uncertainty. Furthermore, the study lays the groundwork for future research in high-performance computing to enhance optimization techniques and broaden the model's application in construction. • Introduces an integrated model for optimizing Non-Unit Repetitive Construction Projects. • Incorporates optimal scheduling with multiple crews, non-serial activity groups, and flexible or fixed sequences. • Utilizes probabilistic methods such as PERT, Triangular, and Normal distributions for random activity durations. • Examines correlated uncertainties within the same activity group, enhancing project reliability. • Validates the proposed model through four comprehensive case studies, demonstrating improved reliability in scheduling. [ABSTRACT FROM AUTHOR]

The advanced information technology has enabled supply chain to make centralized optimal decision, allowing to make a global optimal solution. However, dealing with uncertainty is important in inventory management. Besides demand and supply uncertainties, supplier discounts also often arise unexpectedly. Further, suppliers or third-parties typically offer various delivery options in which trade-off occurs between cost and lead time. Thus, this study introduces new problem namely Multi-Echelon Inventory Optimization with Delivery Options and Uncertain Discount (MEIO-DO-UD). As a solution, Neuroevolution Reinforcement Learning (NERL) framework is developed for minimizing total system cost. The environment is modeled via System Dynamics (SD) and actor is presented by integration of Artificial Neural Network and Evolutionary Algorithm (EA), creating an effective decision-making model under dynamic uncertainty. The experimental study has been conducted where two different supply chain networks are given namely serial and divergence. Three EA algorithms are compared namely Differential Evolution (DE), Memetic Algorithm (MA), and Evolution Strategy (ES). Furthermore, NERL is also compared with the EA-optimized classical continuous review model namely (s,Q). The result shows that regardless what EA type is used, the proposed NERL always outperforms EA-optimized (s,Q) model. The more complex the problem, the further improvement can be made i.e. cost reduction up to 58%, followed by the fill rate improvement. The result also shows that NERL can avoid overfitting. Managerial implications are highlighted where NERL provides the more stable inventory level among all supply chain partners and bull-whip effect can be damped. [ABSTRACT FROM AUTHOR]

The article proposes a dual-time dual-population multi-objective evolutionary algorithm, DTDP-EAMO, to address the challenges faced by multi-objective differential evolution algorithms in solving complex optimization problems, such as weakened late-stage search capability, susceptibility to local optima, and insufficient diversity. Firstly, DTDP-EAMO divides the population into three sub-populations using fast nondominated sorting. Then, it introduces a dual-stage and multi-population adaptive mutation strategy to maintain the convergence and diversity of each sub-population. Meanwhile, a dual external archive mechanism is introduced to enable the population to escape local optima and facilitate information exchange between sub-populations through archiving high-quality solutions from both stages. Finally, the individuals in the external archive are sorted, and high-quality solutions are selected and recombined to generate a new population. To demonstrate the effectiveness of DTDP-EAMO, fourteen test functions and a portfolio optimization problem are selected. Experimental results show that the algorithm's overall performance is superior to three comparative algorithms. Furthermore, practical application results demonstrate DTDP-EAMO's ability to reduce risk and increase returns in portfolio optimization problems, further confirming its effectiveness and feasibility. DTDP-EAMO outperforms comparative algorithms in convergence and distribution, indicating higher accuracy and superiority in solving portfolio models. [ABSTRACT FROM AUTHOR]

Feature selection in machine learning is a crucial step to effectively address the issue of feature redundancy in classification problems. Numerous feature selection algorithms have been developed to minimize the number of features, reduce computational cost, and improve classification accuracy. Differential evolution algorithms have the advantage of being simple in structure, robust, fast in convergence, and frequently used to solve feature selection problems. However, it is worth noting that differential evolution algorithms are susceptible to local optimum and stagnation issues, particularly when applied to high-dimensional data. To address this issue, in this study, we propose a differential evolution framework based on the fluid model, named DEF - F M , for feature selection. DEF - F M has the capability to speed up the convergence of differential evolution algorithms and alleviate the effects of local optima. The proposed framework is validated and compared against eight popular differential evolution algorithms using 12 publicly available benchmark datasets and experimental results unequivocally demonstrate the superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

Enhancing the performance of optimization algorithms to accurately extract parameters for photovoltaic modules is a significant challenge in the field. While several metaheuristic algorithms have been explored, achieving a balance between exploration and exploitation capabilities remains elusive. In this context, hybrid metaheuristics have emerged as a promising approach to address this issue and improve optimization efficiency. In our study, we propose a novel hybrid algorithm that integrates the backtracking search optimization algorithm (BSA) with the differential evolution (DE) algorithm. This hybridization aims to enhance the precision of parameter extraction for various photovoltaic models, including single-diode, dual-diode, and commercially available modules such as the ST40 Thin Film, SM55 Monocrystalline, and S75 Polycrystalline. The BSA is initially employed to leverage historical experiences and guide the evolutionary process, thereby enhancing the algorithm's exploration capabilities. Subsequently, the DE algorithm is introduced to accelerate convergence during iterations and improve exploitation of the search space. By combining these two algorithms, we aim to achieve superior accuracy and efficiency in parameter estimation. To validate the effectiveness of our approach, extensive experiments are conducted, and the results demonstrate its superiority over other sophisticated algorithms. The proposed algorithm achieves remarkable performance, as evidenced by the lowest mean square error values obtained: 9.86021877891501E-04 for the single-diode model, 9.82484851785148E-04 for the dual-diode model, 2.42507486809511E-03 for Photowatt-PWP201, 1.72981370994069E-03 for STM6-40/36, and 1.66006031250855E-02 for STP6-120/36. This highlights the algorithm's exceptional accuracy and efficiency in extracting parameters for photovoltaic modules. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed assembly flexible job shop scheduling problem (DAFJSP), which involves three stages: production stage, assembly stage, and delivery stage. The production stage is accomplished in a few flexible job shops, the assembly stage is accomplished in a few single-machine factories, and the delivery stage is to deliver the obtained products to the corresponding customers. To address the problem, a hybrid estimation of distribution algorithm based on differential evolution operator and variable neighborhood search (HEDA-DEV) is proposed with the goal of minimizing the total cost and tardiness. Firstly, a new multidimensional coding method is designed based on the features of the DAFJSP. Secondly, two mutation operators and the similarity coefficient based on the probability matrix are put forward to implement the dynamic mutation. Thirdly, five types of neighborhood structures satisfying cooperative search strategies are employed to adequately improve the local exploitation ability. Finally, the comparison experiment results suggest that the proposed HEDA-DEV has competitive performance compared to the selected efficient algorithms. Moreover, a real case study is used to demonstrate that HEDA-DEV is an effective method for solving DAFJSP. [ABSTRACT FROM AUTHOR]

The Active Disturbance Rejection Control (ADRC) has been employed in many industrial applications in recent year. One of the key issues when applying ADRC to industrial process control is determining controller parameters. This paper presents a new robust optimal tuning rule for first order reduced ADRC for the industrial processes which can be approximated to First Order Plus Dead Time (FOPDT) model. The tuning rule is derived to achieve a good control performance and robustness of the closed loop system by minimizing the Integrated Absolute Error (IAE) or Integrated Time-weighted Absolute Error (ITAE) under robustness constraints. It is formulated as an optimization problem with the strong nonlinear inequality constraints such as stability margin and maximum sensitivity function constraint. It is difficult to solve these optimization problems analytically. From this, the optimization problem is effectively solved by using the Particle Swarm Optimization - Differential Evolution (PSO-DE) hybrid intelligence algorithm, which is one of the swarm intelligent optimization techniques. The validity of the proposed tuning rule is verified via simulations for the benchmark processes and the temperature control for yeast culture process. [ABSTRACT FROM AUTHOR]