Multiple Chaos Embedded Gravitational Search Algorithm

Zhenyu SONG; Shangce GAO; Yang YU; Jian SUN; Yuki TODO

doi:10.1587/transinf.2016EDP7512

Multiple Chaos Embedded Gravitational Search Algorithm

Zhenyu SONG, Shangce GAO, Yang YU, Jian SUN, Yuki TODO

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This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different 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 effectiveness and efficiency 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 effective for improving the performance of GSA.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.4 pp.888-900

Publication Date: 2017/04/01

Publicized: 2017/01/13

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016EDP7512

Type of Manuscript: PAPER

Category: Biocybernetics, Neurocomputing

Authors

Zhenyu SONG
  University of Toyama
Shangce GAO
  University of Toyama
Yang YU
  University of Toyama
Jian SUN
  University of Toyama,Taizhou University
Yuki TODO
  Kanazawa University

Keyword

chaos, gravitational search algorithm, local search, optimization, meta-heuristics

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Zhenyu SONG, Shangce GAO, Yang YU, Jian SUN, Yuki TODO, "Multiple Chaos Embedded Gravitational Search Algorithm" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 4, pp. 888-900, April 2017, doi: 10.1587/transinf.2016EDP7512.
Abstract: This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different 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 effectiveness and efficiency 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 effective for improving the performance of GSA.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7512/_p

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@ARTICLE{e100-d_4_888,
author={Zhenyu SONG, Shangce GAO, Yang YU, Jian SUN, Yuki TODO, },
journal={IEICE TRANSACTIONS on Information},
title={Multiple Chaos Embedded Gravitational Search Algorithm},
year={2017},
volume={E100-D},
number={4},
pages={888-900},
abstract={This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different 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 effectiveness and efficiency 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 effective for improving the performance of GSA.},
keywords={},
doi={10.1587/transinf.2016EDP7512},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Multiple Chaos Embedded Gravitational Search Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 888
EP - 900
AU - Zhenyu SONG
AU - Shangce GAO
AU - Yang YU
AU - Jian SUN
AU - Yuki TODO
PY - 2017
DO - 10.1587/transinf.2016EDP7512
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2017
AB - This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different 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 effectiveness and efficiency 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 effective for improving the performance of GSA.
ER -