Dynamic Multi-group Self-adaptive Differential Evolution Algorithm with Local Search for Function Optimization
Chaohua Dai · Dianzi xuebao · 2010
An improved algorithm based on differential evolution algorithms,dynamic multi-group self-adaptive differential evolution algorithm with local search(DMSDELS),is applied to optimize functions in this paper.In DMSDELS,the population is randomly and dynamically divided into multi-group individuals,which can exchange information.To speed up search,in the mutation phase the best individual is chosen as the base vector,and the selection of the direction for difference vector is benefit to search.The scaling factor F and the crossover rate CR are self-adapted in order to balance the local search and the global search.To accelerate the convergence,elitist individuals could search in local after they explored specified generations.DMSDELS is tested on thirteen complex benchmark functions.The results are compared with those of other seven algorithms.The results show that DMSDELS is better in the search precision,convergence property and has strong ability to escape from the local sub-optima.