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Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is among the best evolutionary algorithms for global optimization. However, the basic DE has several shortcomings, like the slow convergence speed, and it is more likely to be stuck at local optima. Additionally, DE's performance is sensitive to its mutation strategies and control parameters for mutation and crossover. In this scope, we present in this paper three mechanisms to overcome DE limitations. First, two novel mutations called DE/mean-current/2 and DE/best-mean-current/2 are proposed and integrated in the DE algorithm, and they have both exploration ability and exploitation trend. On the other hand, to avoid being trapped in local minima of hard functions, a new exploration operator has been proposed called Weibull flight based on the Weibull distribution. Finally, new adapted control parameters based on the Weibull distribution are integrated. These parameters contribute to the optimization process by adjusting mutation scale and alleviating the parameter setting problem often encountered in various metaheuristics. The efficacy of the proposed algorithms called meanDE, MDEW, AMDE, and AMDEW is validated through intensive experimentations using classical tests, some challenging tests, the CEC2017, CEC2020, the most recent CEC2022, four constraint engineering problems, and the data clustering problem. Moreover, comparisons with several popular, recent, and high-performance optimization algorithms show a high effectiveness of the proposed algorithms in locating the optimal or near-optimal solutions with higher efficiency. The experiments clearly indicate the effectiveness of the new mutations compared to the standard DE mutations. Moreover, the proposed Weibull flight has a great capacity to deal with the hard composition functions of CEC benchmarks. Finally, the use of adapted control parameters for the mutation scale helps overcome the parameter setting problem commonly encountered in various metaheuristics. [ABSTRACT FROM AUTHOR]

Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions—Levy, Gaussian, and Cauchy—for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative analysis reveals that ICSA outperforms well-known metaheuristic algorithms when applied to the CEC'20 test suite and significantly improves image segmentation performance, proving its ability to avoid local optima and overcome the original algorithm's drawbacks. Medical image segmentation is essential for employing digital images for disease diagnosis, particularly for post-processing activities such as analysis and disease identification. Due to poor illumination and other acquisition-related difficulties, radiologists are especially concerned about the optimal segmentation of brain magnetic resonance imaging (MRI). Multilevel thresholding is the most widely used image segmentation method due to its efficacy and simplicity of implementation. The issue, however, is selecting the optimum set of criteria to effectively segment each image. Although methods like Otsu's between-class variance and Kapur's entropy help locate the optimal thresholds, using them for more than two thresholds requires a significant amount of processing resources. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. Due to the aforementioned, we present an improved chameleon swarm algorithm (ICSA) in this paper for image segmentation and global optimization tasks to be able to address these weaknesses. In the ICSA method, the best random mutation strategy has been introduced to improve the searchability of the standard CSA. The best random strategy utilizes three different types of distribution: Levy, Gaussian, and Cauchy to mutate the search individuals. These distributions have different functions, which help enhance the quality of the solutions and avoid premature convergence. Using the IEEE CEC'20 test suite as a recent complex optimization benchmark, a comprehensive set of experiments is carried out in order to evaluate the ICSA method and demonstrate the impact of combining the best random mutation strategy with the original CSA in improving both the performance of the solutions and the rate at which they converge. Furthermore, utilizing the Otsu approach and Kapur's entropy as a fitness function, ICSA is used as an image segmentation method to select the ideal threshold values for segmenting a set of MRI images. Within the experiments, the ICSA findings are compared with well-known metaheuristic algorithms. The comparative findings showed that ICSA performs better than other competitors in solving the CEC'20 test suite and has a significant performance boost in image segmentation. [ABSTRACT FROM AUTHOR]

The cooperative coevolution framework was introduced to address the shortcomings of metaheuristic algorithms in solving continuous large-scale global optimization problems. By dividing the problem into subcomponents which can be optimized separately, the framework can improve on both the solution's quality as well as the computational speed by exposing a degree of parallelism. Distributed computing platforms, such as Apache Spark, have long been used to improve the speed of different algorithms in solving computational problems. This work proposes a distributed implementation of the cooperative coevolution framework for solving large-scale global optimization problems on the Apache Spark distributed computing platform. By using a formerly outlined distributed variant of the cooperative coevolution framework, features of the Spark platform are utilized to enhance the computational speed of the algorithm while maintaining comparable search quality to other works in the literature. To test for the proposed implementation's improvement in computational speed, the CEC 2010 large-scale global optimization benchmark functions are used due to the diversity they offer in terms of complexity, separability and modality. Results of the proposed distributed implementation suggest that a speedup of up to ×3.36 is possible on large-scale global optimization benchmarks using the Apache Spark platform. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s11227-024-05905-4.Correction to: The Journal of Supercomputing https://doi.org/10.1007/s11227-024-05906-3In this article, the wrong figure appeared as Fig. 6.:GraphThe figure should have appeared as shown below:GraphThe original article has been corrected.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Yuansheng Gao; Jiahui Zhang; Yulin Wang; Jinpeng Wang and Lang QinReported by Author; Author; Author; Author; Author [Extracted from the article]

Differential evolution (DE) is one of the most efficient evolution algorithms (ES) for dealing with nonlinear, complicated and difficult global optimization problems. The main contribution of this paper can be summarized in three directions: Firstly, a novel crossover rate (CR) generation scheme based on the zscore value of fitness, named fcr, is introduced. For a minimization problem, the proposed CR generation strategy always assigns a smaller CR value to individual with smaller fitness value. Therefore, the parameters of individuals with better fitness are inherited by their offspring with high probability. In the second direction, the control parameters are adjusted by unused bimodal settings in which each parameter setting is selected according to the evolution status of individual. The third direction of our work is introducing the L1 norm distance as the weights for updating the mean value of crossover rate and scale factor. Theoretically, compared with L2 norm, L1-norm is more efficient to suppress outliers in the difference vector. These modifications are first integrated with the mutation strategy of JADE, then a modified version, named JADEfcr, is proposed. In addition, to improve the optimization ability further, another variant LJADEfcr by using a linear population reduction mechanism is considered. So as to confirm and examine the performance of JADEfcr and LJADEfcr, numerical experiments are conducted on 29 optimization problems defined by CEC2017 benchmark. For JADEfcr, its experimental results are made a comparison with twelve state-of-the-art algorithms. The comparative study demonstrates that in terms of robustness, stability and solution quality, JADEfcr are better and highly competitive with these well-known algorithms. For LJADEfcr, its results are compared with JADEfcr and other nine powerful algorithms including four recent algorithms and five top algorithms on CEC2017 competition. Experimental results indicate that LJADEfcr is superior and statistically competitive with these excellent algorithms in terms of robustness, stability and the quality of the obtained solutions. [ABSTRACT FROM AUTHOR]

The solar system boundary exploration mission has the characteristics of long flight time, high fuel consumption, complex gravity-assist sequence and strict constraints. Therefore, the number of decision variables and the search space of the transfer trajectory are very large, resulting in poor convergence and efficiency of the global search of the metaheuristic algorithm. Moreover, the existing gravity assist space pruning algorithm is no longer applicable for solar system boundary exploration. To effectively reduce the search space and improve the effect of trajectory optimization, an improved gravity assist space pruning algorithm is proposed. In this algorithm, a unique pruning procedure is used to effectively prune the search space, a shape of solution space box bounds combining rectangle and rhombus is adopted, and a method to automatically determine the solution space box bounds is presented. To verify the effectiveness of the improved gravity assist space pruning algorithm, the sensitivity of pruning effect to parameters is analyzed and the optimization effects of three typical metaheuristics are compared. The optimization results of 50 repeated runs of the differential evolution algorithm in the entire search space and the solution space box bounds are compared. Simulation results show that the performance of differential evolution algorithm is better than bat algorithm and firefly algorithm. And the improved pruning algorithm can increase the efficiency of subsequent optimization by more than eleven times and the convergence probability of the objective function by fifty of times. The applicability and efficiency of the proposed method for the solar system boundary exploration are demonstrated. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithms tend to be limited to local optimization when solving complex optimization problems. Different iteration schemes lead to different convergence speeds. In this paper, we mainly use the dynamic mutation parameter FS to improve the DE algorithm. Based on two ideas, a total of seven DE schemes are proposed to optimize the DE algorithm. We test the performance of the improved DE scheme on 56 test functions. Experiments show that the improved DE algorithm is better than the baseline DE algorithm in terms of accuracy, convergence and8 convergence speed. [ABSTRACT FROM AUTHOR]

A differential evolution (DE) algorithm with superior-inferior mutation scheme (SIDE) is proposed to solve global optimization problems over continuous space. Firstly, a superior-inferior mutation scheme is introduced based on the superior individual and inferior individual. Secondly, the dynamic adjustment strategy of exploration factor for balancing superior individual and inferior individual is proposed, which indicates how much weight to place on local exploitation and global exploration. They are integrated to DE in different milestones of optimization to balance exploration and exploitation of the search space and can alleviate the premature convergence. In order to verify the performance of SIDE, a set of numerical experiments on 32 benchmark functions are executed for performance comparison with 8 advanced DE variants and 4 non-DE-based algorithms for 30, 50 and 100 variables. The experimental results indicate that SIDE is much better than compared DE algorithms in terms of optimization quality. Furthermore, SIDE has the best adaptability to high dimensional problems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization, which has been used in a wide range of application fields. The parameter of population size usually has important influence on the performance of DE, but it is not widely studied in the scope of DE. Based on the relationship between the population size and the exploration/exploitation ability of DE, we propose a novel fluctuant population (FP) strategy for automatically adjusting the value of population size during the runs. More specifically, the FP strategy mainly contains three parts: a monotone decreasing function is used to coordinate the focus between the exploration ability and exploitation ability, which can cause the FP strategy to decrease progressively at the macro level; a periodic function is applied to control the population diversity, which leads to the fluctuant feature of FP strategy at micro level; a rearranging and auto-grouping operation is used for removing and adding individual when the population size is changed. To evaluate the effect of the fluctuant population strategy on DE algorithm, based on 30 benchmark functions, we compare six selected DE algorithms with and without the FP strategy. The simulation results show that the fluctuant population strategy can significantly improve the performance of the six DE algorithms. [ABSTRACT FROM AUTHOR]

The width spread is one of the key indices affecting hot rolling processes and product quality. The traditional Shibahara spread prediction model (SSM) does not take into account the wear and interference of production equipment. As a result, the prediction accuracy cannot satisfy the high precision and high reliability requirements. To improve the prediction accuracy, first, this paper analyzes the strip deformation mechanism and the source of prediction errors and proposes an improved Shibahara spread model (ISSM) based on wear and interference. Second, a Bayesian-optimized differential evolution and adaptive gradient descent (BDE-AGD) algorithm is proposed for ISSM parameter optimization. This algorithm adopts the Bayesian algorithm to optimize the differential evolution (BDE) algorithm in stage 1, which improves the global search ability. Furthermore, the adaptive strategy is used for gradient descent (AGD) to improve the local search capability in stage 2. Finally, the experimental results show that the BDE-AGD algorithm reduces the error by 21.9% and 3.1% compared with DE-AGD and BDE-GD, respectively. This shows that BDE-AGD has a better global optimization capability. In addition, the prediction precision of ISSM is improved by 9.77% compared with the traditional SSM. Moreover, the precision of ISSM is improved by 8.56% on average compared with seven machine learning algorithms. This shows that ISSM optimized by BDE-AGD can achieve high-precision prediction of strip spread. [ABSTRACT FROM AUTHOR]

The slime mould algorithm (SMA) is a new meta-heuristic algorithm recently proposed. The algorithm is inspired by the foraging behavior of polycephalus slime moulds. It simulates the behavior and morphological changes of slime moulds during foraging through adaptive weights. Although the original SMA's performance is better than most swarm intelligence algorithms, it still has shortcomings, such as quickly falling into local optimal values and insufficient exploitation. This paper proposes a Gaussian barebone mutation enhanced SMA (GBSMA) to alleviate the original SMA's shortcomings. First of all, the Gaussian function in the Gaussian barebone accelerates the convergence while also expanding the search space, which improves the algorithm exploration and exploitation capabilities. Secondly, the differential evolution (DE) update strategy in the Gaussian barebone, using rand as the guiding vector. It also enhances the algorithm's global search performance to a certain extent. Also, the greedy selection is introduced on this basis, which prevents individuals from performing invalid position updates. In the IEEE CEC2017 test function, the proposed GBSMA is compared with a variety of meta-heuristic algorithms to verify the performance of GBSMA. Besides, GBSMA is applied to solve truss structure optimization problems. Experimental results show that the convergence speed and solution accuracy of the proposed GBSMA are significantly better than the original SMA and other similar products. [ABSTRACT FROM AUTHOR]

Marine Predators Algorithm (MPA) is a recent efficient metaheuristic algorithm that is enlightened by the biological behavior of ocean predators and prey. This algorithm simulates the Levy and Brownian movements of prevalent foraging strategy and has been applied to many complex optimization problems. However, the algorithm has defects such as a low diversity of the solutions, ease into the local optimal solutions, and decreasing convergence speed in dealing with complex problems. A modified version of this algorithm called ODMPA is proposed based on the tent map, the outpost mechanism, and the differential evolution mutation with simulated annealing (DE-SA) mechanism. The tent map and DE-SA mechanism are added to enhance the exploration capability of MPA by increasing the diversity of the search agents, and the outpost mechanism is mainly used to improve the convergence speed of MPA. To validate the outstanding performance of the ODMPA, a series of global optimization problems are selected as the test sets, including the standard IEEE CEC2014 benchmark functions, which are the authoritative test set, three well-known engineering problems, and photovoltaic model parameters tasks. Compared with some famous algorithms, the results reveal that ODMPA has achieved better performance than its counterparts in CEC2014 benchmark functions. And in solving real-world optimization problems, ODMPA could get higher accuracy than other metaheuristic algorithms. These practical results demonstrate that the mechanisms introduced positively affect the original MPA, and the proposed ODMPA can be a widely effective tool in tackling many optimization problems. [ABSTRACT FROM AUTHOR]

The canonical Pigeon-inspired optimization (PIO) possesses excellent local exploitation capability and can provide a very fast convergence speed, but it is also easily trapped in local optima especially when facing complex problems. In order to take full advantage of the superior local exploitation capability, and at the same time enhance the global exploration capability of PIO, we present a modified PIO called high-level target navigation PIO (HTNPIO). The HTNPIO includes three strategies, selective mutation strategy (SMS), levy-based map-compass strategy (LMS), and enhanced landmark strategy (ELS). In the strategies, two kinds of mutation strategies and a simple levy-flight operator are performed. What's more, an LMS-ELS probability is proposed to balance the exploration and exploitation. In order to test the performance of the proposed optimizer, HTNPIO is made comparisons with other 15 PIO and advanced heuristic algorithms on the IEEE CEC2017 benchmark problems and 5 real world optimization problems. Experimental results demonstrate that HTNPIO defeats all the competitors on the CEC2017 benchmark problems including the extraordinarily competitive LSHADE, and also exhibits extremely competitive performance in dealing with the real-world problems. Therefore, HTNPIO might be effective to provide promising solutions in various function and industrial optimization problems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient global optimization algorithm. However, due to its random properties, some individuals may mutate in the direction of deviating from the theoretical global optima, failing to evolve and wasting a lot of computing resources. Moreover, there is an imbalance between exploration and exploitation in mutation strategies. For these shortcomings, we propose an adaptive mutation strategy correction framework (AMSC) for DEs. In this framework, the population is firstly split into superior subpopulation and disadvantaged subpopulation. Two types of auxiliary mutant vectors based on the direction information are designed to respectively enhance the exploration ability and exploitation ability of these two subpopulations, so as to improve the search efficiency. Moreover, for achieving the proper balance between exploration and exploitation in DEs, we propose an adaptive cooperative rule for the above two auxiliary vectors based on the actual crossover rates. This rule controls the relative size of two subgroups to determine the proportion of two types of auxiliary vectors used in the whole population. To evaluate the performance of AMSC framework, we have introduced into eight original DEs and carried out comparative experiments on four practical problems and 59 test functions from CEC 2014 and CEC 2017 benchmark suites. The experiments demonstrate that the AMSC framework can increase DEs' performance dramatically. [ABSTRACT FROM AUTHOR]

Trapping in local solutions is the main issue in several metaheuristic techniques. To solve such drawbacks by enhancing the search agents, a modified search strategy becomes a more attractive tactic. In this paper, an innovative version of Manta Ray Foraging Optimization (MRFO) is proposed to solve its crucial drawbacks while handling global and engineering optimization problems. The proposed version presents an integrated variant of MRFO with the triangular mutation operator and orthogonal learning strategy, called MRTMO. The two approaches are considered to achieve a robust equipoise between algorithm cores and provide a reliable mechanism to guide the search agents during the optimization process. The proposed MRTMO was tested with challenging CEC2005 and CEC2017 functions and six engineering problems to show its performance. Additionally, several evaluation metrics were employed to ensure the efficiency and robustness of the proposed MRTMO. Furthermore, extensive comparisons with existing optimization algorithms were carried out to ensure the superiority of MRTMO. The numerical experiments proved the competitive performance of the proposed MRTMO in solving all tested CEC optimization and engineering problems. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC'20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance. [ABSTRACT FROM AUTHOR]

To resolve the issue of the curse of dimensionality in continuous large-scale optimization problems, the cooperative coevolution divide-and-conquer framework was proposed by dividing the problem into several subcomponents either randomly or based on the interaction between variables, each of which can be optimized separately using metaheuristic suboptimizers. The goal of researchers is to optimize the performance of algorithms in terms of both quality of solution and computational speed, seeing that large-scale optimization can be a computationally expensive process. This work proposes a parallel implementation to the cooperative coevolution framework for solving large-scale global optimization problems using the Graphics Processing Unit (GPU) and CUDA platform. A distributed variant of the cooperative coevolution framework is outlined to expose a degree of parallelism. Features of the GPU parallel technology and CUDA platform such as shared and global memories are used to optimize the subcomponents of the problem in parallel, speeding up the optimization process while attempting to maintain comparable search quality to works in the literature. The CEC 2010 large-scale global optimization benchmark functions are used for conducting experiments and comparing results in terms of improvements in search quality and search efficiency. Results of proposed parallel implementation show that a speedup of up to x13.01 is possible on large-scale global optimization benchmarks using the GPUs. [ABSTRACT FROM AUTHOR]

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder–Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems. [ABSTRACT FROM AUTHOR]

Niching techniques have been widely incorporated into differential evolution (DE) to improve the diversity of the search. Existing niching-based DE schemes, however, typically employ a certain niching technique during the entire evolution. Since different niching techniques possess different diversity preserving properties, employing a fixed niching technique during DE evolution may have limited performance. In this paper, we propose an adaptive niching selection-based DE for global optimization problems. In the proposed method, instead of employing a certain fixed niching technique, an adaptive niching selection scheme has been devised. In this scheme, multiple niching techniques are employed and adaptively used during the DE evolution, thus properly preserving the population diversity. Further, to appropriately facilitate the adaptive niching selection, both the fitness improvement and entropy of population resulting from niching techniques have been considered to measure their effectiveness. The performance of the proposed method has been evaluated on multi-modal and hybrid composition test functions and compared with related methods. The results show that our proposed method can deliver a satisfying performance and is competitive with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In most Differential evolution (DE) algorithms, the inferior vectors in the selection operator are always ignored during the evolutionary process. However, from the existing studies, these inferior vectors can provide valuable information in guiding the search of DE. Thus, how to effectively utilize the information from the current population together with the inferior vectors is one of the most salient and important topics in DE. This study proposes an enhanced utilization mechanism of population information (EUM) for DE. In EUM, there are two novel operators to utilize the information of the inferior and superior vectors generated during the evolution, proximity-based replacement operator (PRO) and negative direction operator (NDO). For PRO, the trial vector that is worse than its parent vector will have a chance to replace other parent vectors with the conditions based on the proximity. For NDO, the winning vectors in the selection process or RPO are stored in the archive to guide the mutation process by introducing the negative direction information. By incorporating EUM into DE, the novel DE framework, EUM-DE, is proposed. To test the effectiveness of the proposed algorithm, EUM-DE is applied to several original and advanced DE algorithms. The experimental study on the CEC2013 benchmark functions has shown that the proposed EUM is an effective approach to enhance the performance of most DE algorithms studied. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. [ABSTRACT FROM AUTHOR]

In order to solve the problems of transmission delay and data loss caused by network congestion and strengthen the quality of service of transmission delay and throughput, a method of flow control in network media information transmission based on differential evolution algorithm was presented. By analyzing the delay effect of buffer on network instability, the problems of flow control at the receiving end were clarified. Moreover, the jitter between packet and end-to-end time delay parameters in dynamic caching mechanism were analyzed. The maximum and minimum values were set to calculate the balance point of real-time communication and smooth network information transmission flow. In addition, the differential evolution algorithm based on adaptive quadratic mutation of population fitness variance was used to improve the adaptive global optimization structure, organize the minimum evolution and input the minimum sample. Through reasonable selection of controller parameters and adaptive calculation, the poles of the closed-loop control system were obtained. Finally, the transmission performance index could be optimized. Thus, the stable control of transmission flow was completed. Simulation results show that the proposed algorithm can effectively improve the transmission performance of heterogeneous networks. Meanwhile, it has obvious advantages when the asymmetric parameters are high. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a global optimization process that uses population search to find the best solution. It offers characteristics such as fast convergence time, simple and understood algorithm, few parameters, and good stability. To improve its presentation, we propose a differential evolution algorithm based on subpopulation adaptive scale and multi-adjustment strategy (ASMSDE). The algorithm separates the population into three sub-populations based on fitness scores, and different operating tactics are used depending on their characteristics. The superior population uses Gaussian disturbance, while the inferior population uses Levy flights. The intermediate population is responsible for maintaining the population's overall variety. The sizes of the three sub-populations are adaptively changed in response to evolutionary results to account for changes in individual differences over time. With the number of iterations increases and the disparities between individuals reduce, adopt a single population model instead of multi-population model in the later stage of evolution. The ASMSDE algorithm's performance is evaluated by comparing it to other sophisticated algorithms that use benchmark function optimizations. Experimental results show that the ASMSDE algorithm outperforms the comparison algorithms in the majority of circumstances, demonstrating its effectiveness and capacity to manage local optimum situations. [ABSTRACT FROM AUTHOR]

To improve the global convergence and optimization efficiency for solving high-dimensional expensive black-box optimization problems, a radial basis function-assisted adaptive differential evolution using cooperative dual-phase sampling (RBF-ADE) is proposed in this paper. In RBF-ADE, the radial basis function is integrated with the differential evolution framework to approximate the expensive functions. During the evolutionary process, a population-based construction region identification mechanism is developed to select training sample points from the database for RBF construction, which simultaneously improves the modeling efficiency and prediction accuracy of RBF surrogates. RBF is adaptively refined during the optimization via a cooperative dual-phase sampling mechanism, which produces infill samples in the vicinity of the global optimum to improve the competitiveness of the current population. Several high-dimensional numerical benchmarks are used to test the performance of RBF-ADE. Comparison results show that RBF-ADE has better global convergence than the competitive surrogate-assisted evolutionary algorithms with the same computational budget. Finally, RBF-ADE is successfully applied to a 50-dimensional airfoil aerodynamic design optimization problem and an all-electric geostationary orbit satellite multidisciplinary design optimization problem to demonstrate its practicality and effectiveness in engineering practices. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. To enhance identification accuracy, a genetic algorithm is used to iterate the feature sets of angles and line segments, and the optimum features are ultimately generated via cross evolution of generations. The character library is then utilized as an input item for average grouping for trials, and the resulting feature sets are placed in a position matrix and compared one by one to the samples in the database. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization methods; It is a well-known global optimization approach for large-scale search and optimization problems, commonly used to find the solution many different optimization problems. Slime mould optimization algorithm (SMA) is a recently presented metaheuristic technique that is inspired by the behavior of slime mould. Slow convergence speed is a fundamental problem in SMA as in other metaheuristic optimization methods. In order to improve the SMA method, 10 different chaotic maps have been applied for the first time in this article to generate chaotic values instead of random values in SMA. Using chaotic maps, it is aimed to increase the speed of SMA's global convergence and prevent it from getting stuck in its local solutions. The Chaotic SMA (CSMA) proposed for the first time in this study was applied to 62 different benchmark functions. These are unimodal, multimodal, fixed dimension, CEC2019, and CEC2017 test suite. The results of the application have been comparatively analyzed and statistical analysis performed with the well-known metaheuristic optimization methods, particle swarm optimization and differential evolution algorithm, and recently proposed grey wolf optimization (GWO) and whale optimization algorithm (WOA). In addition, in the CEC2017 test suite, the CSMA method has been compared with the SMA, WOA, GWO, harris hawk optimization, archimedes optimization algorithm and COOT algorithms that have been proposed in recent years, and statistical analyzes have been made. In addition, CSMA has been tested in 3 different real-world engineering design problems. According to the experimental results, it was observed that CSMA achieved relatively more successful results in 62 different benchmark functions and real-world engineering design problems compared to other compared methods and standard SMA. [ABSTRACT FROM AUTHOR]

In this research, a modification of the Harris hawk optimization (HHO) is introduced and applied to feature selection. The proposed HHO variant is termed as hybrid multi-leader HHO with differential evolution (MLHHDE). To help hawks overcome the situation of falling into local optimum during the searching process, an improved memory structure is introduced, which makes the hawks learn simultaneously from the historical best and global best positions. Besides, the multi-leader mechanism is also introduced to make full use of the valuable information from global best memories (leaders), enhance the diversity of hawks' search mode, and improve the hawks' exploration capability. Furthermore, differential evolution operations are integrated into HHO to further improve the weak exploration phase. Our proposed MLHHDE algorithm integrates a V-shaped transfer function, which can convert continuous solutions to binary solutions. To validate the modification of MLHHDE, we compared its performance with the advanced optimization algorithms through three experiments. In the first experiment, the performance of MLHHDE to solve a set of problems from the CEC 2017 benchmark is evaluated. Meanwhile, the second experiment aims to apply the binary version of MLHHDE to tackle the feature selection task by applying it to a set of sixteen datasets from the UCI repository. In the third, we applied the proposed model as a quantitative structure-activity relationship method to predict the influenza viruses H1N1 as a real-world application. The performance of the proposed MLHHDE is assessed using a number of evaluation indicators. The experiment results prove the powerful capability of MLHHDE to find the optimal solution in the two experiments as well as it outperforms other methods (i.e., either global optimization or feature selection). In addition, the developed MLHHDE provides accuracy overall the UCI datasets nearly 84% with difference 5% between it and traditional HHO, also, it provides accuracy 92% with standard deviation when applied to predict H1N1. [ABSTRACT FROM AUTHOR]

To provide effective resolutions for complex real-life problems and other optimization problems, abundant, various procedures have been presented in the last few decades. This paper proposes a simple but efficient hybrid evolutionary algorithm called GOA-DE for solving visual tracking problems. In the proposed hybrid algorithm, Grasshopper Optimization Algorithm (GOA) operates in refining the vector. In contrast, the Differential Evolution (DE) algorithm is used for transforming the decision vectors based on genetic operators. The improvement in maintaining the balance between exploration and exploitation abilities is made by incorporating genetic operators, namely, mutation and crossover in GOA. The success of GOA-DE is estimated by 23 classical benchmark functions, CEC05 functions, and CEC 2014 functions. The GOA-DE algorithm results prove that it is very viable associated with the metaheuristic up-to-date procedures. Similarly, visual tracking problems are resolved by the GOA-DE algorithm as a real challenging case study. Visual tracking several objects robustly in a video stream with complex backgrounds and objects are beneficial in subsequent generation computer vision structures. But, in exercise, it is problematic to plan an effective video-based visual tracking scheme owing to the fast-moving objects, probable occlusions, and diverse light circumstances. Investigational outcomes indicate that the GOA-DE-based tracker can energetically track a random target in many thought-provoking cases. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants. [ABSTRACT FROM AUTHOR]

Mutation is the main operator of differential evolution (DE), as it is responsible for combining the information of distinct solutions to generate a donor vector. Aiming at improving the search effectivity of DE, previous research incorporated the calculation of centroids into the DE mutations. In some of the existing methods, the centroids are simply calculated as the center of some selected solutions (or, the entire population); in other cases, one-step clustering is used to perform a local search, or the centroids themselves are used as actual solutions in the population. As opposed to these methods, in this paper we extend some traditional DE mutation strategies to incorporate centroids calculated by deterministic hierarchical clustering. Experimental results on two sets of well-known benchmark problems show that the proposed cluster-centroid-based mutation strategies outperform, in general, the traditional rand/1 strategy, as well as several metaheuristics from the literature. Therefore, the use of clustering is an effective way to improve the search performance that could be exploited also in other population-based metaheuristics. [ABSTRACT FROM AUTHOR]

Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson's disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE–GSO–ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE–GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE–GSO–ANFIS over the similar methods in predicting medical disorders. [ABSTRACT FROM AUTHOR]

The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions. [ABSTRACT FROM AUTHOR]

Differential privacy has been a common framework that provides an effective method of establishing privacy-guaranteed machine learning. Extensive research work has focused on differential privacy stochastic gradient descent (SGD-DP) and its variants under distributed machine learning to improve training efficiency and protect privacy. However, SGD-DP relies on the premise of convex optimization. In large-scale distributed machine learning, the objective function may be more a non-convex objective function, which not only makes the gradient calculation difficult and easy to fall into local optimization. It's difficult to achieve truly global optimization. To address this issue, we propose a novel differential privacy optimization algorithm based on quantum particle swarm theory that suitable for both convex optimization and non-convex optimization. We further comprehensively apply adaptive contraction–expansion and chaotic search to overcome the premature problem, and provide theoretical analysis in terms of convergence and privacy protection. Also, we verify through experiments that the actual application performance of the algorithm is consistent with the theoretical analysis. [ABSTRACT FROM AUTHOR]

Over the past few years, cloud computing model (e.g., Spark) has aroused huge attention. Differential evolution (DE) has been applied to cloud computing models by a number of researchers for its merits in solving large-scale global optimization problems (LSGO), and remarkable results have been achieved. Moreover, we noticed that a combination of better topology and migration strategy is critical to solve the mentioned problems when DE algorithm acts as an internal optimizer for Spark cloud computing model. However, rare studies have been conducted to combine the combination to enhance the performance of DE algorithm for solving large-scale global optimization problems. Thus, inspired by the mentioned insights, we propose a novel grouping topology model that uses DE variants as internal optimizers to solve LSGO problems, called SgtDE. In SgtDE, population is split into subgroups evenly, and various topology structures are introduced to migrate individuals between and within subgroups. In this paper, five types of DE are adopted as the internal optimizers. By comparing the 20 benchmark functions presented on CEC2010, the results demonstrate that the SgtDE, especially a combination of better topology and migration strategy, exhibits significant performance in applying various DE variants. Thus, the SgtDE can act as the next generation optimizer of the cloud computing platform. [ABSTRACT FROM AUTHOR]

Harmony search (HS) is a type of population-based optimization algorithm that is introduced based on the idea of musical instruments being tuned to obtain the best harmony state. Several versions of HS have been presented, with global harmony search (GHS) considered one of the most popular. Although GHS is efficient in solving various optimization problems, a new position updating mechanism has been added to improve its efficiency and help it avoid getting stuck in local minima. The novel algorithm proposed in this paper is called the intersect mutation global harmony search algorithm (IMGHSA), which has been tested and evaluated on a set of well-known benchmark functions. The IMGHSA is compared with several improved variants of the HS algorithm, such as the basic version of harmony search (HS), improved differential harmony search, generalized opposition-based learning with global harmony search , and novel global harmony search. The experimental results show that the proposed IMGHSA performs better than the state-of-the-art HS variants and has a more robust convergence when optimizing objective functions in terms of the solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a new population-based stochastic optimization algorithm called Hybrid Self-Adaptive Differential Evolution (HSADE) is proposed. The algorithm addresses unconstrained global optimization problems, exploring and combining the best features of some Differential Evolution (DE), obtaining a good balance between exploration and exploitation. These approaches are important for increasing the accuracy and efficiency of a population-based stochastic algorithm and for adapting the control parameter values during the optimization process. Further, they are crucial for increasing the convergence speed and reducing the risk of search stagnation. To verify the performance of the HSADE, 25 benchmark functions were tested presenting an optimal performance when compared to some state-of-the-art DEs algorithms. Furthermore, an experimental problem in an automotive sector, related to automatic internal combustion engine calibration, was used adjusting 300 decision variables. The HSADE achieved a reliable calibration, reducing the time required to perform approximately 90% when comparing to other optimization algorithms. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a readily comprehensible and highly powerful intelligent optimized method for numerical optimization. The performance of DE significantly depends on its parameters and strategies generating both mutation vector and trial vector. To further enhance its exhibition, we propose a new DE variant called JDF-DE based on JADE by introducing the improved parameter approach with weight and crossover strategy with Jrand number decreasing mechanism and feedback guide technique. The new way for updating parameter μCR and μF brings fitness value to generate more reasonable parameters with the fixed orientation during evolution. Meanwhile, Levy distribution is used to complete the adaptive distribution of CR when the population has a high clustering intensity so that solutions escape from the local optimal value. Jrand number decreasing mechanism is embedded to crossover operation to strengthen population diversity instead the number of Jrand equals 1 in primary DE algorithm. Feedback guide method is utilized to determine the step size for Jrand number to advance the ability that JDF-DE searches the optimum value. In order to investigate performance of JDF-DE, In order to analyze performance of JDF-DE, 29 benchmark functions from CEC2017 on real parameter optimization are employed to verify the validity of JDF-DE for solving complex high-dimensional problems. The experimental results show that JDF-DE is better than, or at least comparable with several state-of-the-art DE variants including DE variants JADE, SinDE, TSDE, AGDE, and EFADE and non-DE variants TSA, SHO, GWO, MVO, SCA, and GSA in the global numerical optimization problems. [ABSTRACT FROM AUTHOR]

Brain storm optimization is a swarm intelligence algorithm inspired by the brainstorming process in human beings. Many researchers have paid much more attention to it, and many attempts have been made to improve it's performance. The search ability of brain storm optimization is maintained by the creating process of ideas, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel brain storm optimization variant, named RMBSO, in which a slight relaxation selection and multi-population based creating ideas ensemble are employed to improve the performance of brain storm optimization on global optimization problem with diverse landscapes. Firstly, the basic framework of original brain storm optimization is imbedded into multi-population based ensemble of heterogeneous but complementary creating ideas to make the algorithm jump out of stagnation with strong searching ability. Secondly, a new triangular mutation ruler and a simple partition of subpopulations are designed to better balance exploration and exploitation. Thirdly, a slight relaxation selection mechanism instead of greedy choice is first developed to keep the population's diversity. Finally, extensive experiments on the suit of CEC 2015 benchmark functions and statistical comparisons are executed. Experimental results indicate that the proposed algorithm is significantly better than, or at least comparable to the state-of-the-art brain storm optimization variants and several improved differential evolution algorithms. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) is a one of the most efficient evolutionary for global optimization and widely applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploitation and exploration. To cope with these issues, a new cuckoo search algorithm extension based on self-adaptive knowledge learning (I-PKL-CS) is proposed. In this study, learning model with individual history knowledge and population knowledge is introduced into the CS algorithm. Individuals constantly adjust their position by using historical knowledge and communicate with each other by using their own knowledge in the optimization process. In order to reduce complexity of the I-PKL-CS algorithm, the optimal learning model is selected to exploit the potential of individual knowledge learning and population knowledge learning by adopting threshold statistics learning strategy, which provides a good trade-off between the exploration and exploitation. The accuracy and performance of the proposed approach are evaluated by eighteen classic benchmark functions and CEC 2013 test suite. Statistical comparisons of the experimental results showed that the proposed I-PKL-CS algorithm made an appropriate trade-off between exploration and exploitation. Comparing the proposed I-PKL-CS with various CS algorithms, variants of differential evolution, and improved particle swarm optimization algorithms, the results demonstrate that I-PKL-CS is a competitive new type of algorithm. [ABSTRACT FROM AUTHOR]

Quantum-inspired heuristic search algorithms have attracted considerable research interest in recent years. However, existing quantum simulation methods are still limited on the basis of particle swarm optimizer. This paper explores the principle of memetic computing to develop a novel memetic quantum evolution algorithm for solving global optimization problem. First, we design a quantum theory-based memetic framework to handle multiple evolutionary operators, in which multiple units of different kinds of algorithmic information are harmoniously combined. Second, we propose the memetic evolutionary operator and the quantum evolutionary operator to complete the balance between the global search and the local search. The memetic evolutionary operator emphasizes meme diffusion by the shuffled process to enhance the global search ability. The quantum evolutionary operator utilizes an adaptive selection mechanism for different potential wells to tackle the local search ability. Furthermore, the Newton's gravity laws-based gravitational center and geometric center as two important components are introduced to improve the diversity of population. These units can be recombined by means of different evolutionary operators that are based on the synergistic coordination between exploitation and exploration. Through extensive experiments on various optimization problems, we demonstrate that the proposed method consistently outperforms 11 state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new comprehensive learning gravitational search algorithm (CLGSA) is proposed to enhance the performance of basic GSA. The proposed algorithm is a new kind of intelligent optimization algorithm which has better ability to choose good elements. An intensive comprehensive learning methodology is proposed to enrich the optimization ability of the GSA. The efficiency of the proposed algorithm was evaluated by 28 benchmark functions which have been proposed in IEEE-CEC 2013 sessions. The results are compared with eight state-of-the-art algorithms IPOP, BIPOP, NIPOP, NBIPOP, DE/rand, SPSRDEMMS, SPSO-2011 and GSA. A variety of ways are considered to examine the ability of the proposed technique in terms of convergence ability, success rate and statistical behavior of algorithm over dimensions 10, 30 and 50. Apart from experimental studies, theoretical stability of the proposed CLGSA is also proved. It was concluded that the proposed algorithm performed efficiently with good results. [ABSTRACT FROM AUTHOR]

In this paper, a new spectral variables selection method, induced mutation genetic algorithm (IMGA), is proposed for near-infrared (NIR) spectroscopy. Based on the idea of genetic algorithm (GA), the IMGA greatly simplifies the process of biological evolution, which not only inherits the advantages of global optimization of the GA, but also effectively improves the convergence speed. In this study, the IMGA is applied to the selection of characteristic spectral variables for green tea origin identification. After five times of genetic evolutions, 11 characteristic spectral variables are selected from 156 spectral variables. Based on the 11 characteristic spectral variables, the classification model is built by partial least squares (PLS), and both the sensitivity and specificity of classification model are raised to 1 for prediction set. The overall results indicate that the IMGA can be well applied to the selection of characteristic spectral variables and effectively improve the prediction accuracy and calculation speed of the near-infrared model. [ABSTRACT FROM AUTHOR]

In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design optimization. Moreover, four recent variations of evolutionary-based algorithms [i.e., improved differential evolution algorithm based on an adaptive mutation scheme, weighted differential evolution algorithm (WDE), linear population size reduction success-history-based adaptive differential evolution algorithm, and biogeography-based optimization with covariance matrix-based migration] have been tackled for handling the current problem. The objective function is based on the cost of shallow foundation designs that satisfy ACI 318-05 requirements is formulated as the objective function. This study addresses shallow footing optimization with two attitudes, routine optimization, and sensitivity analysis. As a further study, the effect of the location of the column at the top of the foundation is examined by adding two additional design variables. Three numerical case studies are used for both routine and sensitivity analysis. Moreover, the most common evolutionary-based technique, genetic algorithm (GA), is considered as a benchmark to evaluate the proposed methods' efficiency. Based on the results, there is no algorithm which works as the most efficient solver over all the cases; while, BBO and WDE showed an acceptable performance because of satisfying records in most cases. There were several cases in which GA, DE, and ES were incapable of finding a valid solution which meets all the constraints simultaneously. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for numerical optimization. However, the performance of DE significantly relies on its mutation operator and control parameters (scaling factor and crossover rate). In this paper, we propose a novel DE variant by introducing a series of combined strategies into DE, called CSDE. Specifically, in CSDE, to obtain a proper balance between global exploration ability and local exploitation ability, we adopt two mutation operators with different characteristics to produce the mutant vector, and provide a mechanism based on their own historical success rate to coordinate the two adopted mutation operators. Moreover, we combine a periodic function based on one modulo operation, an individual-independence macro-control function and an individual-dependence function based on individual's fitness value information to adaptively produce scaling factor and crossover rate. To verify the effectiveness of the proposed CSDE, comparison experiments contained seven other state-of-the-art DE variants are tested on a suite of 30 benchmark functions and four real-world problems. The simulation results demonstrate that CSDE achieves the best overall performance among the eight DE variants. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population uses different mutation strategies and control parameters, individuals share different mutation strategies and control parameters by migrating among sub-populations. A novel mutation strategy is proposed, which uses the best individual and a randomly selected individual to generate base vector. The performance of mDE-brM is evaluated on the CEC 2013 LSGO benchmark suite and compared with 5 state-of-the-art optimization techniques. The results show that, compared with other contestant algorithms, mDE-brM has a competitive performance and better efficiency in LSGO. [ABSTRACT FROM AUTHOR]

Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative abilities of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications: Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition-based learning method has been used in the initial half of iterations to provide diversity among the search agents. The proposed approach has been tested on standard benchmarking functions for different population and dimension sizes to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than GWO, bat algorithm, bat flower pollinator, chicken swarm optimization, differential evolution, firefly algorithm, flower pollination algorithm (FPA) and grasshopper optimization algorithm. Statistical testing of GWO-E has been done to prove its significance over other popular algorithms. Further, as a real-world application, the GWO-E is used to design non-uniform linear antenna array (LAA) for minimum possible sidelobe level and null control. Performance of GWO-E for the synthesis of LAA is evaluated by considering the several different case studies of LAA that exists in the literature, and the results are compared with the results of other popular meta-heuristic algorithms like genetic algorithm, ant lion algorithm, FPA, cat swarm optimization, GWO and many more. Numerical results further show the superior performance of GWO-E over original GWO and other popular algorithms. [ABSTRACT FROM AUTHOR]

In the evolutionary computing community, differential evolution (DE) is well appreciated as a simple yet versatile population-based, non-convex optimizer designed for continuous optimization problems. A simple two-phase DE algorithm is presented in this article, which aims to identify promising basins of attraction on a non-convex functional landscape in the first phase, and starting from those previously identified search regions, a success history-based switch parameter DE is employed to further fine tune the search process leading to the optima of the landscape. Our proposed framework has been validated on the well-known IEEE Congress on Evolutionary Computation (CEC) benchmark suites (CEC 2013, 2014 and 2017). Results of the proposed method are compared with corresponding CEC winners (SHADE for CEC 2013, L-SHADE for CEC 2014 and jSO for CEC 2017). [ABSTRACT FROM AUTHOR]

Autonomous air combat is an inevitable trend in the development of unmanned combat aerial vehicle (UCAV) equipment. Its purpose is to generate maneuver trajectory so that UCAVs obtain better air combat situation. Therefore, aiming to solve a tactical maneuver trajectory optimization problem for an UCAV in autonomous air combat, this paper proposed a novel method by converting the problem to an optimization problem of characteristic parameters. On the one hand, the paper analyses the tactical maneuver trajectory and combines the situation evaluation model to construct a tactical maneuver trajectory optimization function based on characteristic parameters. On the other hand, multi-population rotation strategy differential evolution (MPRDE) algorithm is designed to search for the optimal characteristic parameters. The experimental results showed that the MPRDE algorithm has outstanding performance in convergence speed, global optimization ability and robustness, and the method based on characteristic parameters could effectively and quickly represent the tactical maneuver trajectory of UCAV by using MPRDE. Meanwhile, it satisfies the real-time requirements for generating tactical manoeuvring trajectory for UCAV autonomous air combat. [ABSTRACT FROM AUTHOR]