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LIU Ao1; DENG Xudong1; REN Liang1; LIU Ying3; LIU Bo4
Source Publication系统科学与复杂性学报英文版
AbstractAs a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence.In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.
Document Type期刊论文
2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System
Recommended Citation
GB/T 7714
LIU Ao,DENG Xudong,REN Liang,et al. aninversepowergenerationmechanismbasedfruitflyalgorithmforfunctionoptimization[J]. 系统科学与复杂性学报英文版,2019,032(002):634.
APA LIU Ao,DENG Xudong,REN Liang,LIU Ying,&LIU Bo.(2019).aninversepowergenerationmechanismbasedfruitflyalgorithmforfunctionoptimization.系统科学与复杂性学报英文版,032(002),634.
MLA LIU Ao,et al."aninversepowergenerationmechanismbasedfruitflyalgorithmforfunctionoptimization".系统科学与复杂性学报英文版 032.002(2019):634.
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