A Hybrid Evolutionary Algorithm Based on EDAs and Clustering Analysis

An improved mixed evolutionary algorithm is proposed, which is based on evolutionary trend, EDAs (estimation of distribution algorithms) and clustering analysis. First, the population is classified by clustering algorithm, then for each class, partial individuals of next generation are generated by EDAs, and the rest are supplemented by combination of extrema among classes, which can overcome the premature effectively. When the individuals in some classes converge to a small field, an exhaustive local search replaces EDAs. Simulation shows the algorithm can not only improve the global searching greatly, but also overcome premature effectively.