Differential evolution using a neighborhood-based mutation operator

  • Authors:

  • Swagatam Das;Ajith Abraham;Uday K. Chakraborty;Amit Konar

  • Affiliations:

  • Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, Trondheim, NO, Norway and Machine Intelligence Research Labs;Department of Math and Computer Science, University of Missouri, St. Louis, MO;Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India

  • Venue:
  • IEEE Transactions on Evolutionary Computation
  • Year:
  • 2009

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Abstract

Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and real-world problems. DE, however, is not completely free from the problems of slow and/or premature convergence. This paper describes a family of improved variants of the DE/target-to-best/1/bin scheme, which utilizes the concept of the neighborhood of each population member. The idea of small neighborhoods, defined over the index-graph of parameter vectors, draws inspiration from the community of the PSO algorithms. The proposed schemes balance the exploration and exploitation abilities of DE without imposing serious additional burdens in terms of function evaluations. They are shown to be statistically significantly better than or at least comparable to several existing DE variants as well as a few other significant evolutionary computing techniques over a test suite of 24 benchmark functions. The paper also investigates the applications of the new DE variants to two reallife problems concerning parameter estimation for frequency modulated sound waves and spread spectrum radar polyphase code design.