Evolutionary Multiparty Distance Minimization

TL;DR

This paper introduces a new evolutionary algorithm, OptMPNDS3, designed for multiparty multiobjective optimization problems involving multiple decision makers, and demonstrates its effectiveness through comparative experiments.

Contribution

It proposes a novel algorithm tailored for MPMOPs, addressing the gap in evolutionary computation for problems with multiple decision makers.

Findings

OptMPNDS3 performs strongly compared to existing algorithms.

The algorithm effectively visualizes Pareto optimal solutions.

The method handles complex multiparty objectives efficiently.

Abstract

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