Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TEVC.2007.895272 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
classic de algorithm,adaptive local search,ls scheme,accelerating differential evolution,effective evolutionary algorithm,ls technique,ls heuristics,incorporating ls heuristics,performance comparison,single ls length,adaptive ls,wide range,memetic algorithm,algorithm design and analysis,evolutionary algorithm,evolutionary computation,global optimization,parallel processing,local search,particle swarm optimization,robustness,design optimization,differential evolution,stochastic processes,hill climbing,acceleration,genetic algorithms | Evolutionary algorithm,Heuristics,Artificial intelligence,Genetic algorithm,Mathematical optimization,Crossover,Algorithm design,Algorithm,Evolutionary computation,Differential evolution,Local search (optimization),Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 1 | 1089-778X |
Citations | PageRank | References |
282 | 8.78 | 32 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Noman | 1 | 282 | 8.78 |
| Hitoshi Iba | 2 | 1541 | 138.51 |