2021
DOI: 10.1109/access.2021.3083220
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Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Abstract: As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS an… Show more

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Cited by 46 publications
(13 citation statements)
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References 98 publications
(88 reference statements)
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“…In Formula (10) e random range is [0, 1]. In the improved gray wolf optimization algorithm, the competitive cooperation between wolf leaders is incorporated to improve the overall search performance [18]. e division and update of the wolf leader level would be determined by the real-time error evaluation value, and the active area of the wolf group would be determined by Formulas ( 11)-( 13):…”
Section: Improved Grey Wolf Optimization Supportmentioning
confidence: 99%
“…In Formula (10) e random range is [0, 1]. In the improved gray wolf optimization algorithm, the competitive cooperation between wolf leaders is incorporated to improve the overall search performance [18]. e division and update of the wolf leader level would be determined by the real-time error evaluation value, and the active area of the wolf group would be determined by Formulas ( 11)-( 13):…”
Section: Improved Grey Wolf Optimization Supportmentioning
confidence: 99%
“…Due to its unpredictable behavior, particularly at high dimensions, F2 was removed [45]. These benchmark functions are frequently employed, which is sufficient to confirm the effectiveness of an optimization strategy from multiple perspectives [46]. We established three distinct function dimensions (D), D = 30, 50 and 100.…”
Section: Experimental Results On Cec2017mentioning
confidence: 99%
“…Many studies in the field of evolutionary algorithms have demonstrated that historical information generated by population iterations has a favorable influence on new individual variation [16], [28]. Therefore, with the fundamental aim of further increasing the weight of historical information in individual variation while avoiding over-reliance on specific individuals, an archive population (S) independent of the main population (C) is designed in this study.…”
Section: A Novel Bis-population-based Non-revisiting Mechanismmentioning
confidence: 99%