摘要
针对和声搜索算法在求解多目标问题时效率不高、易陷入局部最优、在算法后期收敛精度不够等不足。提出一种改进的多目标和声搜索算法,其思想是通过引入自适应操作,加强算法的全局搜索能力,增加解的多样性;同时对解集根据Pareto最优解进行非支配排序,提高算法效率,增加算法在后期的收敛精度。在数值仿真实验中选取4个测试函数进行实验,并同其他算法进行多方面比较,结果表明该算法具有更好的性能。
Aiming at the shortcomings of harmony search algorithm in solving multi-objective problems,such as low efficien⁃cy,easy to fall into local optimum,inadequate convergence accuracy in the later stage of the algorithm,etc,an improved multi-ob⁃jective harmony search algorithm is proposed in this paper.The idea is that by introducing adaptive operation,the global search abil⁃ity of the algorithm is enhanced and the diversity of solutions is enriched.At the same time,non-dominated sorting of solution sets is carried out according to Pareto optimal solution to improve the efficiency of the algorithm and increase the convergence accuracy of the algorithm in the later period.Four test functions are selected in numerical simulation experiments and compared with other al⁃gorithms in many aspects.The results show that the algorithm has better performance.
作者
谷培义
高尚
GU Peiyi;GAO Shang(School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212000)
出处
《计算机与数字工程》
2021年第6期1132-1136,共5页
Computer & Digital Engineering
关键词
多目标优化
和声搜索算法
PARETO最优
multi-objective optimization
harmony search algorithm
Pareto optimality
