Accelerating Differential Evolution Using an Adaptive Local Search

• Published in: IEEE transactions on evolutionary computation Vol. 12; no. 1; pp. 107 - 125
• Main Authors: Noman, N., Iba, H.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Adaptive algorithms; Algorithm design and analysis; Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Design optimization; Differential evolution (DE); Evolution; Evolutionary algorithms; Evolutionary computation; Exact sciences and technology; Genetic algorithms; global optimization; Heuristic; Lips; local search (LS); Mathematical analysis; memetic algorithm (MA); Parallel processing; Particle swarm optimization; Performance enhancement; Robustness; Searching; Stochastic processes; local search (LS); memetic algorithm (MA); global optimization; Differential evolution (DE); Local search; Evolutionary algorithm; Heuristic method; Hill climbing method; Global optimum; Adaptive method
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2007.895272

We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.