Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works. 

Differential evolution: A recent review based on state-of-the-art works

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

Graphical abstractDisplay Omitted HighlightsWe propose a novel binary coded ant colony algorithm by blending of GA and BACO.The proposed algorithm is adopted to handle with the problem of feature selection.Results show the method outperforms GA, BPSO, BACO, ABACO, BDE in feature selection. Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

A feature selection method based on modified binary coded ant colony optimization algorithm

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications.

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

Genetic learning particle swarm optimization (GL-PSO) improves the performance of particle swarm optimization (PSO) by breeding superior exemplars to guide the motion of particles. However, GL-PSO adopts a global topology for exemplar generation and cannot preserve sufficient diversity to enhance exploration, and therefore, its performance on complex optimization problems is unsatisfactory. To further improve GL-PSO's performance and adaptability, two modifications are incorporated into the original GL-PSO. A ring topology is adopted in exemplar generation to enhance diversity and exploration, while a global learning component (GLC) with linearly adjusted control parameters is employed to improve the algorithm's adaptability. The resultant algorithm is referred to as global genetic learning particle swarm optimization with diversity enhancement by ring topology (GGL-PSOD). To validate the effectiveness of these two modifications, they are combined with GL-PSO separately and together and further tested experimentally. The comparison results on the CEC2017 test suite show that the adoption of the ring topology in exemplar generation can enhance the diversity and exploration capability of GL-PSO, while combining GLC alone with GL-PSO cannot achieve significant improvement. Incorporating both modifications into GL-PSO, the resultant GGL-PSOD exhibits high performance and strong adaptability on different types of CEC2017 functions. It outperforms seven representative PSO variants and five non-PSO meta-heuristics, including GL-PSO, SL-PSO, HCLPSO, EPSO, ABC, CMA-ES and CS.

Global genetic learning particle swarm optimization with diversity enhancement by ring topology

This paper introduces a design for multi-objective PID controller using non-dominated sorting genetic algorithm (NSGA-II). When selecting the objectives to be optimized, it is taken into account to cover some important characteristics of the system like performance, robustness and control signals’ smoothness. The decision making is done using Level diagram tool. Three tanks liquid level system control is discussed as a case study. The results show that this tool improves the process of decision making (DM). Also, comparisons with Ziegler and Nichols (Z-N) and different optimization methods are presented.

/pdf/design-of-optimal-pid-controller-using-nsga-ii-algorithm-and-3urjvszszm.pdf

Design of Optimal PID Controller Using NSGA-II Algorithm and Level Diagram

This paper outlines a design method for tuning of PID controller using new algorithm based on Particle Swarm Optimization. The tuned PID controller is used in liquid level system control. Compared to different algorithms; the proposed method was more efficient. It gets the best results

https://bfemu.journals.ekb.eg/article_100766_a1d52b541b865e550e4a3a4b62ecb520.pdf

A New Method of Particle Swarm Optimization Approach for Tuning PID Controller. (Dept. E)

Propose a modified PSO-DV algorithm using aging mechanism.The algorithm is tested on 12 benchmark functions.The algorithm is used for determining the optimal parameters of PID controller.Comparisons with different evolutionary algorithms show that the algorithm is efficient and robust. This paper presents a new algorithm designed to find the optimal parameters of PID controller. The proposed algorithm is based on hybridizing between differential evolution (DE) and Particle Swarm Optimization with an aging leader and challengers (ALC-PSO) algorithms. The proposed algorithm (ALC-PSODE) is tested on twelve benchmark functions to confirm its performance. It is found that it can get better solution quality, higher success rate in finding the solution and yields in avoiding unstable convergence. Also, ALC-PSODE is used to tune PID controller in three tanks liquid level system which is a typical nonlinear control system. Compared to different PSO variants, genetic algorithm (GA), differential evolution (DE) and Ziegler-Nichols method; the proposed algorithm achieve the best results with least standard deviation for different swarm size. These results show that ALC-PSODE is more robust and efficient while keeping fast convergence.

Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers

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Design of Optimal PID Controller Using NSGA-II Algorithm and Level Diagram

A New Method of Particle Swarm Optimization Approach for Tuning PID Controller. (Dept. E)

Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers