基于随机扰动的多目标进化算法
An Evolutionary Algorithm for Multi-Objective Optimization Problem Based on Random Distuibance
郭修豪 1陈勇1
作者信息
摘要
运用遗传算法解多目标问题，结果往往会陷入局部最优。引入传统算法求得的外部种群，提出基于随机扰动的RDMOGA遗传算法。将新算法用标准多目标测试函数进行测验，并与韩丽霞提出的NMOGA算法进行对比，实验结果表明，新算法表现出良好的搜索性能。
Abstract
Uses genetic algorithm to solve multi-objective problem, the result is often trapped in local optimum. Introduces the external population of the traditional algorithm, and proposes a genetic algorithm based on random perturbation of the RDMOGA. The new algorithm is tested by using the standard multi objective test functions, and compared with the NMOGA algorithm proposed by Han Lixia. The test results show that the new algorithm shows good performance.
关键词
多目标优化/随机扰动/进化算法/拥挤距离排序/C-measure/U-measureKey words
Multi-Objective Optimization/Random Disturbance/Evolutionary Algorithm/Crowding Distance Sorting/C-measure/U-measure引用本文复制引用
基金项目
国家自然科学基金资助项目(60703035)
国家自然科学基金资助项目()
重庆市教委基金资助项目(No.KJ070801)
重庆市教委基金资助项目()
重庆市教委科技项目(No.KJ120622)
出版年
2015
NETL
NSTL
维普
万方数据