Gravitational search algorithm combined with chaos for unconstrained numerical optimization

Gao, Shangce; Vairappan, Catherine; Wang, Yan; Cao, Qiping; Tang, Zheng

doi:10.1016/j.amc.2013.12.175

Cited by 98 publications

(39 citation statements)

References 29 publications

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“…Compared with the methodology which uses chaotic sequences to substitute random values of the controlling parameters in GSA, chaotic local search has been demonstrated to be more effective for improving the performance of GSA [28]. As a matter of fact, chaotic local search is often adopted in related researches [17]- [27].…”

Section: Chaotic Gravitational Search Algorithm (Cgsa)mentioning

confidence: 99%

“…Both embedding strategies of chaos were found to be benefit for improving GSA's search ability, and the latter seemed to be more efficient. The work [28] has been extended by conCopyright c 2017 The Institute of Electronics, Information and Communication Engineers sidering five different chaotic maps. Preliminary experimental results in [29] empirically showed that all introduced five chaotic maps generally exhibited effectiveness of improving the performance of GSA.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [28], we proposed two kinds of chaos-based GSAs. One used chaotic sequences to substitute random numbers for different parameters, and the other used chaotic variables to perform a chaotic local search.…”

Section: Introductionmentioning

confidence: 99%

“…Few research studies the integration of multiple chaotic systems which simultaneously perform the search. The prior chaotic GSA in [28] only utilized the well-known Logistic maps to realize the chaotic search, and the extended one [29] compared the performance difference during five chaotic maps. Obviously, the search capacity of such single chaos embedded GSA is limited.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Chaos Embedded Gravitational Search Algorithm

Song

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis 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.

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Section: Chaotic Gravitational Search Algorithm (Cgsa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiple Chaos Embedded Gravitational Search Algorithm

Song

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

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“…Radu et al [23,24] applied three modifications: define constraint regarding system, modify deprecation equation of gravitation constant and extended symmetrical method and proposed new GSA to reduce parametric sensitivity of fuzzy based control system for optimal tuning. Gao et al [25] proposed a chaotic GSA which combines GSA with chaos. Rashedi et al [26] proposed Binary GSA for solving discrete optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

Singh¹,

Deep²

2015

IJISA

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Abstract-The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

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