Multiparty distance minimization: Problems and an evolutionary approach

Multiparty multiobjective optimization problems (MPMOPs) have been proposed to represent situations in which involves multiple decision makers, each decision maker concerns on a multiobjective optimization problem (MOP) and their MOPs are different. To study multiparty multiobjective evolutionary algorithms in depth, this paper constructs a series of MPMOPs based on distance minimization problems (DMPs). These MPMOPs, called MPDMPs, can easily represent the solutions in the decision space. Thus, the behaviors of evolutionary algorithms performing on MPDMPs can be conveniently studied including the movement of the solutions and the distribution of the final solutions. To address MPDMPs, the new proposed algorithm OptMPNDS3 uses a multiparty initialization method to initialize the population and the JADE2 operator to generate the offspring. OptMPNDS3 is compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The results show that the performance of OptMPNDS3 is strong and comparable to that of other algorithms.