Multiple Chaos Embedded Gravitational Search Algorithm
Multiple Chaos Embedded Gravitational Search Algorithm
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DOI：
10.1587/transinf.2016edp7512
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Zhenyu Song;Shangce Gao;Yang Yu;Jian Sun-;Yuki Todo
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文献类型：
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作者：
Zhenyu Song;Shangce Gao;Yang Yu;Jian Sun-;Yuki Todo
本文提出了一种新的多重混沌嵌入引力搜索算法（MCGSA），它同时利用多个不同的混沌映射，并采用局部搜索的方式。嵌入式混沌局部搜索由于其固有的局部利用能力，可以利用一个小区域来细化由正则引力搜索算法（GSA）获得的解。同时，它也有机会利用混沌的遍历性来探索巨大的搜索空间。为了充分利用混沌的动力学特性，我们提出了三种嵌入策略。多个混沌映射被随机地、随机地或记忆选择性地结合到GSA中。为了评估所提出的MCGSA的有效性和效率，我们将其与GSA和12种仅使用特定混沌映射的混沌GSA变体在一组48个基准优化函数上进行了比较。实验结果表明，MCGSA在收敛速度和求解精度方面优于竞争对手。此外，基于Friedman检验的统计分析表明，嵌入式策略对提高GSA的性能最有效。
SUMMARY This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple di ff erent chaotic maps with a manner of local search. The embedded chaotic local search can exploit a small region to refine solutions obtained by the canonical gravitational search algorithm (GSA) due to its inherent local exploitation ability. Meanwhile it also has a chance to explore a huge search space by taking advantages of the ergodicity of chaos. To fully utilize the dynamic properties of chaos, we propose three kinds of embedding strategies. The multiple chaotic maps are randomly, parallelly, or memory-selectively incorporated into GSA, respectively. To evaluate the e ff ectiveness and e ffi ciency of the proposed MCGSA, we compare it with GSA and twelve variants of chaotic GSA which use only a certain chaotic map on a set of 48 benchmark optimization functions. Experimental results show that MCGSA performs better than its competitors in terms of convergence speed and solution accuracy. In addition, statistical analysis based on Friedman test indicates that the parallelly embedding strategy is the most e ff ective for improving the performance of GSA.