A clonal selection algorithm for dynamic multimodal function optimization

Luo, Wenjian; Lin, Xin; Zhu, Tao; Xu, Peilan

doi:10.1016/j.swevo.2018.10.010

Cited by 46 publications

(29 citation statements)

References 24 publications

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“…We also note that Luo et al [26] proposed a clonal selection method for dynamic multimodal optimization problems and proved the effectiveness of the method. When the global peaks of the problem change with time, how to use the forgetting mechanism to quickly adapt to the new height of global peaks and forget the outdated experience in time will be our future research direction.…”

Section: Resultsmentioning

confidence: 61%

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

et al. 2020

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The antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection. A large number of antibodies with high affinity make it difficult to participate in clonal selection and exist in antibody concentration for a long time. This part of inactive antibody forms a “black hole” of the antibody set, which is difficult to remove and update in a timely manner, thus affecting the speed at which the algorithm approximates the optimal solution. Inspired by the mechanism of biological forgetting, an improved clonal selection algorithm is proposed to solve this problem. It aims to use the abstract mechanism of biological forgetting to eliminate antibodies that cannot actively participate in high-frequency mutations in the antibody candidate set and to improve the problem of insufficient diversity of antibodies in the clonal selection algorithm, which is prone to fall into the local optimal. Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability.

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Section: Resultsmentioning

confidence: 61%

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

et al. 2020

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“…[3], [81], [124]- [130] Moving valleys benchmark (MVB) c [131] Gaussian peaks benchmark (GPB) [15], [132] MPBs with local environmental changes d [133], [134] MPBs with cyclic and pendulum changes e [135]- [138] Multimodal MPB f [139] MPBs with varying number of peaks [140]- [143] MPBs whose peaks have different change severity g [186] Constrained MPBs h [187], [188] Modular MPBs i [144], [145], [173] DRPBG j [6], [75], [97], [106], [111], [114], [115], [146]- [148], [150]- [172], [174] Free [189]. Each peak has its own shift, height, and width severity values which result in generating peaks with different levels of robustness.…”

Section: Discussion On Dop Benchmarksmentioning

confidence: 99%

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

Yazdani

Cheng

Yazdani

et al. 2021

IEEE Trans. Evol. Computat.

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“…In many DOP algorithms, especially multi-population methods, covering multiple optima is done in order to increase and maintain overall diversity, and accelerate the process of finding new global optimum after environmental changes. In [50], a multimodal version of MPB with multiple global optima is proposed. In this benchmark, a predefined number of components are global optima whose heights are constant over time, and only their locations and widths are time varying.…”

Section: B Dop Benchmarks Whose Landscape Is Formed By Joining Severmentioning

confidence: 99%

Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite

Yazdani

Omidvar

Cheng

et al. 2022

IEEE Trans. Cybern.

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Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this paper, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.

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A clonal selection algorithm for dynamic multimodal function optimization

Cited by 46 publications

References 24 publications

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite

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