动态调整策略改进的和声搜索算法

Dynamic adjustment strategy for improving the harmony search algorithm

为了得到高维复杂问题的全局高精度最优解,提出一种动态调整策略,并用该策略改进和声搜索算法。算法选取和声记忆库中最差和声向量作为优化调整目标,随着迭代的进行,逐步降低决策变量的调整概率,该方法能够使得算法在全局探索能力和局部高精度开发能力之间实现平衡,有效提高了新和声更新最差和声的成功率。通过6个高维Benchmark测试函数的仿真结果表明,提出的动态调整策略能够有效提高和声搜索算法求解高维复杂优化问题的能力。

A dynamic adjustment strategy is used to improve the harmony search algorithm for solving high-dimensional multimodal global optimization problems. It chooses the worst harmony vector from the harmony memory（ HM） as an optimization objective vector. With the process of iteration,the adjustment probability of decision variables is reduced step by step. It can achieve the balance effectively between the global exploration powers and local exploitation competence,and can increase the success rate of evolution. Finally,the experimental results of 16high-dimension benchmark functions demonstrated that the proposed method can enhance the performance and robustness of the harmony search algorithm obviously in solving large scale multimodal optimization problems.