A backtracking differential evolution with multi-mutation strategies autonomy and collaboration

Li, Yuzhen; Wang, Shihao; Liu, Hong; Yang, Bo; Yang, Hongyu; Zeng, Miyi; Wu, Zhiqiang

doi:10.1007/s10489-021-02577-y

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…) has better exploitation ability. But, they have low exploration capability when solving multimodal optimization problems (19,20) .…”

Section: Inspiration/motivation/research Gapsmentioning

confidence: 99%

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Design Optimization Using Modified Differential Evolution Algorithm

Tiwari,

Mishra,

Parouha

2023

IJST

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Objectives:The key objective of this article is to suggest a modified differential evolution (MDE) algorithm for design problem optimization particularly reactor network design (RND) problem. Methods: During the few decades differential evolution (DE) algorithm achieved noticeable progress and solved a wide variety of optimization issues. However, the DE suffers from low diversification, poor exploration ability and stagnation. Hence, using concept of the particle swarm optimization mechanism (PSO), suggested MDE employed new mutation operator, to balance exploitation and exploration activities. Also, on the basis of time-varying scheme new mutation operator integrates new control parameter, to avoid stagnation. A group of 6 unconstrained benchmark functions are solved, to investigate the presentation of MDE algorithm. Moreover, its practical superiority is further verified by solving RND problem. Findings: The experiential results show that the suggested MDE performs well in each case of unconstrained benchmark functions with the highest rate of success. Moreover, optimize the RND problem very effectively with the lowest time (2.98s) and fewer number of function evaluations (12729). Furthermore, outcomes suggest that the proposed MDE exhibits a better or at least competitive performance compared to evolutionary algorithms. Novelty: The exploitation and exploration ability of the suggested MDE are balanced efficiently due to use of memory facts (i.e. novel mutation operator) and adapted (i.e. new time-varying) control parameters.

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“…) has better exploitation ability. But, they have low exploration capability when solving multimodal optimization problems (19,20) .…”

Section: Inspiration/motivation/research Gapsmentioning

confidence: 99%

“…Various mutation schemes presented in the literature (13) , to decrease the DE disadvantages. But, want essential refinement to enhance the DE search capability (20) .…”

Section: Inspiration/motivation/research Gapsmentioning

confidence: 99%

Design Optimization Using Modified Differential Evolution Algorithm

Tiwari,

Mishra,

Parouha

2023

IJST

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“…In the existing research, the strategy of adaptive improvement is effective [18]. By updating parameters adaptively through individual similarity and evolutionary state and designing evolutionary backtracking strategies to control population diversity, evolutionary algorithms can be well prevented from falling into premature results [19].…”

Section: A Evolutionary Algorithmsmentioning

confidence: 99%

Improved Adaptive Komodo Mlipir Algorithm

Liu

Zhang

2022

IEEE Access

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In order to improve the global search performance of the Komodo Mlipir Algorithm, this paper proposed two adaptive Komodo Mlipir Algorithms with variable fixed parameters (IKMA-1; IKMA-2). Among them, IKMA-1 adaptively controls the parthenogenesis radius of female Komodo dragons to achieve more efficient conversion of global search and local search. Second, IKMA-2 introduces adaptive weighting factors to the ''mlipir'' movement formula of Komodo dragons to improve the local search performance. Both IKMA-1 and IKMA-2 were tested on 23 benchmark functions in CEC2013 and compared with the other seven optimization algorithms. The Wilcoxon rank-sum test and Friedman rank test were used to compare the performance of different algorithms. Furthermore, IKMA-1 and IKMA-2 are applied to two constrained engineering optimization problems to verify the engineering applicability of the improved algorithm. The results show that both IKMA-1 and IKMA-2 have better convergence accuracy than the initial KMA. In terms of the benchmark function simulation results, IKMA-1 improves the performance by 17.58% compared to KMA; IKMA-2 improves by 10.99%. Both IKMA-1 and IKMA-2 achieve better results than other algorithms for engineering optimization problems, and IKMA-2 outperforms IKMA-1.INDEX TERMS Komodo Mlipir Algorithm, variable fixed parameters, adaptive optimization, engineering design optimization problems. I. INTRODUCTIONEstablishing models to deal with practical problems is an essential means of current academic research, and how to solve models faster and better depends on the actual solution performance of various algorithms. An optimization algorithm is an application technology based on mathematics and used to solve various practical optimization problems. At present, optimization algorithms can be mainly divided into data processing algorithms, neural network algorithms, and swarm intelligence algorithms [1]. Applied optimization problems widely exist in many fields, such as signal processing, production scheduling, medical applications, image processing, and path planning.However, since these optimization problems often involve discrete, discontinuous, and uncertain factors, it is not realistic to rely on a single algorithm to solve all optimization problems in life [2]. Therefore, the innovation of newThe associate editor coordinating the review of this manuscript and approving it for publication was Muhammad Zakarya .

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“…• Novel bio-inspired algorithms (e.g., the Harris hawks optimization algorithm [5], the slime mould algorithm [6], the Multi-Verse Algorithm [7]); • Variations of existing techniques, such as variants of the Flower Pollination algorithm [8], PSO [9][10][11], or DE [12][13][14]; • Hybrid algorithms obtained by combining for instance ACO with chaotic sequences [15] and PSO with local solvers [16].…”

Section: Introductionmentioning

confidence: 99%

Permutation Tests for Metaheuristic Algorithms

Omran

Clerc²,

Ghaddar

et al. 2022

Mathematics

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Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the underlying data that must be true before it can be used. Permutation tests (P-Tests) are statistical tests with minimal number of assumptions. These tests are simple, intuitive and nonparametric. In this paper, we argue researchers in the field of metaheuristics to adopt P-Tests to compare their algorithms. We define two statistic tests and then present an algorithm that uses them to compute the p-value. The proposed process is used to compare 5 metaheuristic algorithms on 10 benchmark functions. The resulting p-values are compared with the p-values of two widely used statistical tests. The results show that the proposed P-test is generally consistent with the classical tests, but more conservative in few cases.

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A backtracking differential evolution with multi-mutation strategies autonomy and collaboration

Cited by 10 publications

References 44 publications

Design Optimization Using Modified Differential Evolution Algorithm

Design Optimization Using Modified Differential Evolution Algorithm

Improved Adaptive Komodo Mlipir Algorithm

Permutation Tests for Metaheuristic Algorithms

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