Applied Intelligence

, Volume 39, Issue 1, pp 41–56 | Cite as

Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems

  • Ming-Feng Han
  • Shih-Hui Liao
  • Jyh-Yeong Chang
  • Chin-Teng Lin
Article

Abstract

This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all individuals in the population are grouped into a superior group and an inferior group based on their fitness values. The two groups perform different mutation operations. The local mutation model is applied to individuals with better fitness values, i.e., in the superior group, to search for better solutions near the current best position. The global mutation model is applied to the inferior group, which is composed of individuals with lower fitness values, to search for potential solutions. Subsequently, the GDE algorithm employs crossover and selection operations to produce offspring for the next generation. In this paper, an adaptive tuning strategy based on the well-known 1/5th rule is used to dynamically reassign the group size. It is thus helpful to trade off between the exploration ability and the exploitation ability. To validate the performance of the GDE algorithm, 13 numerical benchmark functions are tested. The simulation results indicate that the approach is effective and efficient.

Keywords

Evolutionary algorithm (EA) Differential evolution (DE) Adaptive strategy Optimization 

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Ming-Feng Han
    • 1
  • Shih-Hui Liao
    • 1
  • Jyh-Yeong Chang
    • 1
  • Chin-Teng Lin
    • 1
  1. 1.Institute of Electrical Control EngineeringNational Chiao Tung UniversityHsinchuTaiwan, ROC

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