CAJ | 학술논문

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。
위료득도고유복잡문제적전국고정도최우해，제출일충동태조정책략，병용해책략개진화성수색산법。산법선취화성기억고중최차화성향량작위우화조정목표，수착질대적진행，축보강저결책변량적조정개솔，해방법능구사득산법재전국탐색능력화국부고정도개발능력지간실현평형，유효제고료신화성경신최차화성적성공솔。통과6개고유Benchmark측시함수적방진결과표명，제출적동태조정책략능구유효제고화성수색산법구해고유복잡우화문제적능력。
A dynamic adjustment strategy is used to improve the harmony search algorithm for solving high?dimen?sional 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 vari?ables 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 16 high?dimension benchmark functions demonstrated that the proposed method can enhance the performance and ro?bustness of the harmony search algorithm obviously in solving large scale multimodal optimization problems.