CAJ | 학술논문

为了在动态环境中很好地跟踪最优解,考虑动态优化问题的特点,提出一种新的多目标预测遗传算法.首先对 Pareto 前沿面进行聚类以求得解集的质心;其次应用该质心与参考点描述 Pareto 前沿面;再次通过预测方法给出预测点集,使得算法在环境变化后能够有指导地增加种群多样性,以便快速跟踪最优解;最后应用标准动态测试问题进行算法测试,仿真分析结果表明所提出算法能适应动态环境,快速跟踪 Pareto 前沿面.
위료재동태배경중흔호지근종최우해,고필동태우화문제적특점,제출일충신적다목표예측유전산법.수선대 Pareto 전연면진행취류이구득해집적질심;기차응용해질심여삼고점묘술 Pareto 전연면;재차통과예측방법급출예측점집,사득산법재배경변화후능구유지도지증가충군다양성,이편쾌속근종최우해;최후응용표준동태측시문제진행산법측시,방진분석결과표명소제출산법능괄응동태배경,쾌속근종 Pareto 전연면.
Dynamic multiobjective optimization problems require an algorithm to continuously track a changing Pareto optimal solutions over time. Therefore, a new predictive multiobjective genetic algorithm(PMGA) is proposed, in which the centroid of Pareto optimal is soluted by clustering. And Pareto optimal solutions are described by applying the centroid points and reference solutions. Then the prediction set is generated by using the inertia predict and Gauss mutation. After an environment changed, the prediction set is incorporated in the current population to increase the population diversity by guided fashion. Finally, experimental studies on dynamic multiobjective optimization problems are carried out. The simulation results show that PMGA can quickly adapt the dynamic environments and track Pareto optimal solutions.