CAJ | 학술논문

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.
목표유수교고적다목표우화문제적난제재우비지배해급극증가,경전산법유우결핍족구적선택압력도치성능급극하강.제출료기우편호등급적면역기억극륭선택우화산법,용우해결목표유수교고적다목표우화문제.이용결책자제공적편호신식래위항체분배편호등급,근거해치비례극륭항체,증대항체적선택압력,가쾌수렴속솔.근거편호신식래축감Pareto전연,병용유한적편호해고계해전연.동시,건립료면역기억충군래보류교호적비지배항체,채용ε지배궤제래보지기억항체충군적다양성.실험결과표명,대우2목표적편호다목표문제이급고체8목표적DTLZ2화DTLZ3문제,해산법취득료일정적실험효과.
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 ε 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.