Chaotic Local Search-Based Differential Evolution Algorithms for Optimization

Document Type

Article

Publication Date

6-1-2021

Abstract

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

Identifier

85106495329 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Systems

External Full Text Location

https://doi.org/10.1109/TSMC.2019.2956121

e-ISSN

21682232

ISSN

21682216

First Page

3954

Last Page

3967

Issue

6

Volume

51

Grant

JP17K12751

Fund Ref

Japan Society for the Promotion of Science

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