Differential evolution with the search strategy of artificial bee colony algorithm
Weifeng Gao · Kongzhi yu juece · 2012
For the problems of premature convergence frequently appeared in differential evolution(DE) and its poor convergence,a differential evolution with the search strategy of artificial bee colony algorithm is proposed.The method makes full use of the exploration ability of the search strategy of artificial bee colony algorithm to guide the algorithm to jump out of the likely local optima.In addition,to enhance the global convergent speed,when producing the initial population,the opposition-based learning method is employed.Moreover,the performance of the proposed approach is testified on a suite of 12 benchmark functions and the comparisons with other algorithms are provided.Simulation results show that the proposed approach has the better convergence rate and the strong ability of preventing premature convergence.