Chaotic Local Search-Based Differential Evolution Algorithms for Optimization

Shangce Gao, Yang Yu, Yirui Wang, Jiahai Wang, Jiujun Cheng, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

222 Scopus citations


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.

Original languageEnglish (US)
Article number8937719
Pages (from-to)3954-3967
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number6
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Chaotic local search (CLS)
  • chaotic map
  • differential evolution (DE)
  • incorporation scheme
  • optimization algorithm


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