摘要
目标维数较高的多目标优化问题的难题在于非支配解急剧增加,经典算法由于缺乏足够的选择压力导致性能急剧下降.提出了基于偏好等级的免疫记忆克隆选择优化算法,用于解决目标维数较高的多目标优化问题.利用决策者提供的偏好信息来为抗体分配偏好等级,根据该值比例克隆抗体,增大抗体的选择压力,加快收敛速率.根据偏好信息来缩减Pareto前沿,并用有限的偏好解估计该前沿.同时,建立了免疫记忆种群来保留较好的非支配抗体,采用ε支配机制来保持记忆抗体种群的多样性.实验结果表明,对于2目标的偏好多目标问题以及高达8目标的DTLZ2和DTLZ3问题,该算法取得了一定的实验效果.
The difficulty of current multi-objective optimization community lies in the large number of objectives. Lacking enough selection pressure toward the Pareto front, classical algorithms are greatly restrained. In this paper, preference rank immune memory clone selection algorithm （PISA） is proposed to solve the problem of multi-objective optimization with a large number of objectives. The nondominated antibodies are proportionally cloned by their preference ranks, which are defined by their preference information. It is beneficial to increase selection pressure and speed up convergence to the true Pareto-optimal front. Solutions used to approximate the Pareto front can be reduced by preference information. Because only nondominated antibodies are selected to operate, the time complexity of the algorithm can be reduced. Besides, an immune memory population is kept to preserve the nondominated antibodies and e dominance is employed to maintain the diversity of the immune memory population. Tested in several multi-objective problems with 2 objectives and DTLZ2 and DTLZ3 as high as 8 objectives, PISA is experimentally effective.
出处
《软件学报》
EI
CSCD
北大核心
2010年第1期14-33,共20页
Journal of Software
基金
国家自然科学基金Nos.60703107
60703108
国家高技术研究发展计划(863)No.2009AA12Z210
国家重点基础研究发展计划(973)No.2006CB705707
长江学者和创新团队支持计划No.IRT0645~~
关键词
人工免疫系统
偏好多目标优化
偏好等级
ε支配
artificial immune system
preference multi-objective optimization
preference rank
ε dominance
