模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2014年
6期
569-576
,共8页
多目标进化算法(MOEA)%多目标优化%支配关系%偏好
多目標進化算法(MOEA)%多目標優化%支配關繫%偏好
다목표진화산법(MOEA)%다목표우화%지배관계%편호
Multi-objective Evolutionary Algorithm ( MOEA )%Multi-objective Optimization%Dominance Relation%Preference
利用参考点及角度值引入决策者的偏好信息,采用角度偏好区域设定方法将目标空间划分为偏好区域和非偏好区域,提出一种能区分偏好区域和非偏好区域中非支配解的支配策略---角度偏好的ε-Pareto 支配策略.为验证所提出的支配策略的有效性,将其融入基于ε支配的多目标进化算法(ε-MOEA)中,形成 AP-ε-MOEA.通过与融入 G 支配的 G-NSGA-II 和融入 R 支配的 R-NSGA-II 的性能对比实验表明,AP-ε-MOEA 在以较快速度收敛到Pareto 最优边界的同时,能较好满足决策者偏好.
利用參攷點及角度值引入決策者的偏好信息,採用角度偏好區域設定方法將目標空間劃分為偏好區域和非偏好區域,提齣一種能區分偏好區域和非偏好區域中非支配解的支配策略---角度偏好的ε-Pareto 支配策略.為驗證所提齣的支配策略的有效性,將其融入基于ε支配的多目標進化算法(ε-MOEA)中,形成 AP-ε-MOEA.通過與融入 G 支配的 G-NSGA-II 和融入 R 支配的 R-NSGA-II 的性能對比實驗錶明,AP-ε-MOEA 在以較快速度收斂到Pareto 最優邊界的同時,能較好滿足決策者偏好.
이용삼고점급각도치인입결책자적편호신식,채용각도편호구역설정방법장목표공간화분위편호구역화비편호구역,제출일충능구분편호구역화비편호구역중비지배해적지배책략---각도편호적ε-Pareto 지배책략.위험증소제출적지배책략적유효성,장기융입기우ε지배적다목표진화산법(ε-MOEA)중,형성 AP-ε-MOEA.통과여융입 G 지배적 G-NSGA-II 화융입 R 지배적 R-NSGA-II 적성능대비실험표명,AP-ε-MOEA 재이교쾌속도수렴도Pareto 최우변계적동시,능교호만족결책자편호.
By using reference points and angle values, decision maker ' s preferences are introduced intoε-multi-objective evolutionary algorithm(ε-MOEA). The objective space is divided into preference area and non-preference area by the preferences. Moreover, an angle preference based ε-Pareto dominance strategy is presented. It establishes a strict partial order relation to distinguish the preference solutions and non-preference solutions among non-dominated solutions. To demonstrate the effectiveness of the proposed strategy, it is integrated into ε-MOEA,and thus ε-Pareto dominance strategy based on angle preference in MOEA ( AP-ε-MOEA) is put forward . The comparative experiments of AP-ε-MOEA, g-dominance and r-dominance show that AP-ε-MOEA can converge to Pareto optimal front with a higher speed and meanwhile meet the decision maker′s preferences.
