Hybrid harmony search and estimation of distribution algorithm formulti-objective optimization problems
和声搜索-分布估计混合算法求解多目标优化问题

For solving the problems of basic harmony search algorithm can't be well used for multi-objective optimization, this paper proposed a kind of multi-objective hybrid harmony search and estimation of distribution(MHS-EDA). In the harmony memory, sampled every variable by estimation of distribution, so it would widen the space of harmony memory. Out of the harmony memory, every variable would be searched by external repository, so it could exchange information between the populations during the proceeding of evolutionary and enhance the global searching ability of harmony search algorithm. Numerical experiments compare with multi-objective genetic algorithm, multi-objective estimation of distribution algorithm and multi-objective harmony search algorithm on six benchmark problems. The results show that the proposed algorithm can be effective to solve multi-objective optimization problems.