关键词: 和声搜索算法, 随机交叉, 反向学习, 局部最优

Abstract: This paper proposes a Random Crosser Global Harmony Search（RCGHS） algorithm for the problem of premature convergence in harmony search algorithm. In the improvisation stage, the new harmony vector is generated dynamically by means of random crossover for the global optimization problems, i.e., the worst harmony learning from the best harmony and the random selected other harmony learning from the best harmony with different strategies. Mutation strategy is employed to improve the diversity of harmony memory. In the updating stage, the worst harmony vector is updated by the optimal individual of the improvising harmony and the random opposition-based learning harmony vector. Finally, the simulation is carried out using the optimization algorithm of AGHS, ABC, PSO, GWO and other HS variants that are recently proposed. The simulation results demonstrate the RCGHS algorithm has higher convergence precision and convergence rate.

Key words: harmony search algorithm, random crosser, opposition-based learning, local optimum