In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives. In the real-world applications, there usually exist more than one DM and each DM concerns parts of these objectives. Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict the MOP with multiple decision makers involved, where each party concerns about certain some objectives of all. However, in the evolutionary computation field, there is not much attention paid on MPMOPs. This paper constructs a series of MPMOPs based on distance minimization problems (DMPs), whose Pareto optimal solutions can be vividly visualized. To address MPMOPs, the new proposed algorithm OptMPNDS3 uses the multiparty initializing method to initialize the population and takes JADE2 operator to generate the offsprings. OptMPNDS3 is compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The result shows that OptMPNDS3 is strongly comparable to other algorithms
翻译:在进化多目标优化领域,决策者(DM)涉及相互冲突的目标。在现实世界的应用中,通常存在不止一个DM(DM),每个DM(DM)涉及这些目标的部分。建议多党多目标优化问题(MPMOP)涉及多个决策者,每个当事方都关注某些所有目标。然而,在进化计算领域,对MPMOP没有多少关注。本文根据距离最小化问题(DMPs)构建了一系列MPMO(MPMOS),Pareto最佳解决方案可以生动地视觉化。针对MPMOP(MP),拟议的新算法OptMPNDS3使用多党初始化方法初始化人口,并让JADE2操作员生成后代。OptMPNDS3与问题套件的OptAll、OptMPNDS和OptMPNDS2进行了比较。结果显示,OptMPNDS3与其他算法非常相似。结果显示,OptMPNDS3与其他算法非常相似。