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作 者:张茂清 李东洋[1] 胡博[1] 汪镭[1] 崔志华[2] 郭为安[3] ZHANG Maoqing;LI Dongyang;HU Bo;WANG Lei;CUI Zhihua;GUO Weian(School of Electronics and Information,Tongji University,Shanghai 201804,China;School of Computer Science and Technology,Taiyuan University of Science and Technology,Shanxi 030024,China;Sino-Germany College of Applied Sciences,Tongji University,Shanghai 201804,China)
机构地区:[1]同济大学电子与信息工程学院,上海201804 [2]太原科技大学计算机科学与技术学院,山西太原030024 [3]同济大学中德工程学院,上海201804
出 处:《郑州大学学报(工学版)》2020年第1期38-43,共6页Journal of Zhengzhou University(Engineering Science)
基 金:科技部科技冬奥“AI驱动的多模态融合雪上竞技表现大数据智慧服务系统建设与应用”项目(2018YFF0300505);国家自然科学基金资助项目(71771176,61503287,61703279)。
摘 要:速非支配排序遗传算法II(non-dominated sorting genetic algorithm II,NSGA-II)是经典多目标优化算法,然而,其采用的锦标赛选择策略在选择交叉父代时会产生大量重复个体,并进一步导致减少种群个体多样性,降低算法性能。为解决此类问题,提出了基于维度扰动的NSGA-II。即通过在待交叉父代个体每个维度上引入扰动参数改变其值,然后将扰动父代做正常交叉操作产生新后代,以此避免了后代重复个体的产生。为验证算法的有效性,采用ZDT测试集作为测试函数。与现有算法相比,所提策略可有效地改善算法性能,证明了所提策略的有效性。Non-dominated Sorting Genetic Algorithm II(NSGA-II)was a classical multi-objective optimizer.However,the strategy of tournament selection employed in NSGA-II could produce a large amount of repeated individuals and further decrease the diversity of population,resulting in degrading the overall performance.To tackle this problem,this paper proposed NSGA-II based on Dimensionality Perturbation.Firstly,perturbation parameter was introduced,and then it was further used to modify each dimensionality of the parent individuals to do crossover operator.After that,the modified parent individuals did the crossover operator as usual to avoid generating the repeated offspring individuals.To verify the effectiveness of the proposed algorithm,ZDT test suit was employed as benchmark problems.Compared to the state-of-art algorithms,the proposed algorithm was capable of effectively improving the performance of NSGA-II,thus demonstrating the effectiveness of the proposed strategy.
关 键 词:NSGA-II 多目标优化算法 锦标赛选择 维度扰动
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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