A Dynamic Generalized Opposition-Based Learning Fruit Fly Algorithm for Function Optimization

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060177
ISBN (Print)9781509060184
Publication statusPublished - Jul 2018

Publication series

NameIEEE Congress on Evolutionary Computation - Proceedings
Volume2018

Conference

Title2018 IEEE Congress on Evolutionary Computation, CEC 2018
PlaceBrazil
CityRio de Janeiro
Period8 - 13 July 2018

Abstract

As a novel evolutionary algorithm, fruit fly optimization algorithm (FOA) has received great attentions and wide applications in recent years. However, existing literature have demonstrated that the basic FOA often risks getting prematurely stuck in the local optima. In this paper, an improved FOA, named as dynamic generalized opposition-based learning fruit fly optimization algorithm (DGOBL-FOA), is proposed to mitigate the aforementioned drawback hence improve the optimization performance. Three carefully designed operators are incorporated into the basic FOA, i.e., a cloud model based osphresis search is applied to enhance the local refinement search ability in the osphresis phase, then a generalized opposition-based learning operation is adopted to strengthen the global coarse search ability, meanwhile a dynamic shrinking parameter strategy is designed to adjust the learning intensity and narrow down the search space iteratively, which contributes to a good balance between the global exploration and local exploitation. To verify the effectiveness of the proposed algorithm, numerical experiments are conducted on 18 well-studied benchmark functions with dimension of 30. The computation results and statistical analysis indicate that the proposed DGOBL-FOA achieve significantly better performance comparing to other FOA variants and the state-of-the-art metaheuristics.

Research Area(s)

  • cloud model, dynamic shrinking strategy, fruit fly optimization algorithm, generalized opposition-based learning

Citation Format(s)

A Dynamic Generalized Opposition-Based Learning Fruit Fly Algorithm for Function Optimization. / Feng, Xiaoyi; Liu, Ao; Sun, Weiliang et al.

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8477794 (IEEE Congress on Evolutionary Computation - Proceedings; Vol. 2018).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review