An improved differential evolution algorithm with fitness-based adaptation of the control parameters

Arnob Ghosh, Swagatam Das, Aritra Chowdhury, Ritwik Giri

Research output: Contribution to journalArticlepeer-review

145 Scopus citations

Abstract

Differential Evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used, which makes the scheme self-organizing in this respect. Scale Factor (F) and Crossover Rate (Cr) are two very important control parameters of DE since the former regulates the step-size taken while mutating a population member in DE and the latter controls the number of search variables inherited by an offspring from its parent during recombination. This article describes a very simple yet very much effective adaptation technique for tuning both F and Cr, on the run, without any user intervention. The adaptation strategy is based on the objective function value of individuals in the DE population. Comparison with the best-known and expensive variants of DE over fourteen well-known numerical benchmarks and one real-life engineering problem reflects the superiority of proposed parameter tuning scheme in terms of accuracy, convergence speed, and robustness.

Original languageEnglish (US)
Pages (from-to)3749-3765
Number of pages17
JournalInformation sciences
Volume181
Issue number18
DOIs
StatePublished - Sep 15 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Keywords

  • Differential evolution
  • Evolution strategies
  • Evolutionary programming
  • Genetic algorithms
  • Numerical optimization
  • Parameter tuning

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