多子群混合和声搜索算法

Multiple-sub-groups Hybrid Harmony Search Algorithm

为提高和声搜索算法的优化性能,提出一种多子群混合和声搜索(MHHS)算法.该算法基于每个和声到最好和声的距离进行排序,并依据排序结果分层,每一层作为一个独立的子群.不同的子群融合不同的差分调整策略,以拓宽搜索范围;同时建立通信机制,使各子群以一定规格进行信息交流,促进子群的协同进化.实验仿真表明,本文算法在寻优精度、收敛性和鲁棒性方面均优于文献中报道的HS,EHS,NGHS,MPSO,CLPSO,DE,ODE和IABC算法.

To improve the optimization performance of harmony search algorithm, a multiple-sub- groups hybrid harmony search （MHHS） algorithm is proposed. The algorithm sorts the entire harmony individuals according to the space distance between each harmony and the best harmony, and then builds multiple layers based on the ranked results, where each layer is as a unique sub- group. Different sub-groups integrate various differential adjustment strategies to broaden search ranges. Meanwhile, the communication mechanism is built to facilitate the information exchange among the multiple-sub-groups and to promote multiple-sub-groups coevolution. Simulation results show that the proposed algorithm is better than the existing algorithms such as HS, EHS, NGHS, MPSO, CLPSO, DE, ODE and IABC in terms of optimization accuracy, convergence and robustness.