A niching two-layered differential evolution with self-adaptive control parameters

Yongxin Luo, Sheng Huang, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Differential evolution (DE) is an effective and efficient evolutionary algorithm in continuous space. The setting of control parameters is highly relevant with the convergence efficiency, and varies with different optimization problems even at different stages of evolution. Self-adapting control parameters for finding global optima is a long-term target in evolutionary field. This paper proposes a two-layered DE (TLDE) with self-adaptive control parameters combined with niching method based mutation strategy. The TLDE consists of two DE layers: A bottom DE layer for the basic evolution procedure, and a top DE layer for control parameter adaptation. Both layers follow the procedure of DE. Moreover, to mitigate the common phenomenon of premature convergence in DE, a clearing niching method is brought out in finding efficient mutation individuals to maintain diversity during the evolution and stabilize the evolution system. The performance is validated by a comprehensive set of twenty benchmark functions in parameter optimization and competitive results are presented.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1405-1412
Number of pages8
ISBN (Print)9781479914883
DOIs
Publication statusPublished - 2014 Sep 16
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing
Duration: 2014 Jul 62014 Jul 11

Other

Other2014 IEEE Congress on Evolutionary Computation, CEC 2014
CityBeijing
Period14/7/614/7/11

Fingerprint

Niching
Differential Evolution
Adaptive Control
Control Parameter
Evolutionary algorithms
Mutation
Parameter Adaptation
Premature Convergence
Evolution System
Parameter Optimization
Global Optimum
Evolutionary Algorithms
Efficient Algorithms
Vary
Benchmark
Optimization Problem
Target

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Luo, Y., Huang, S., & Furuzuki, T. (2014). A niching two-layered differential evolution with self-adaptive control parameters. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1405-1412). [6900407] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2014.6900407

A niching two-layered differential evolution with self-adaptive control parameters. / Luo, Yongxin; Huang, Sheng; Furuzuki, Takayuki.

Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1405-1412 6900407.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Luo, Y, Huang, S & Furuzuki, T 2014, A niching two-layered differential evolution with self-adaptive control parameters. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900407, Institute of Electrical and Electronics Engineers Inc., pp. 1405-1412, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, 14/7/6. https://doi.org/10.1109/CEC.2014.6900407
Luo Y, Huang S, Furuzuki T. A niching two-layered differential evolution with self-adaptive control parameters. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1405-1412. 6900407 https://doi.org/10.1109/CEC.2014.6900407
Luo, Yongxin ; Huang, Sheng ; Furuzuki, Takayuki. / A niching two-layered differential evolution with self-adaptive control parameters. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1405-1412
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