Fairly Constricted Multi-Objective Particle Swarm Optimization

TL;DR

This paper enhances a multi-objective particle swarm optimization algorithm by integrating exponentially-averaged momentum, leading to improved convergence and performance on standard benchmark problems.

Contribution

It introduces a novel formalism of constriction fairness and extends SMPSO with EM, demonstrating competitive or superior results.

Findings

Matches SMPSO performance on benchmark suites

Outperforms SMPSO in certain problem instances

Provides a new mathematical formalism of constriction fairness

Abstract

It has been well documented that the use of exponentially-averaged momentum (EM) in particle swarm optimization (PSO) is advantageous over the vanilla PSO algorithm. In the single-objective setting, it leads to faster convergence and avoidance of local minima. Naturally, one would expect that the same advantages of EM carry over to the multi-objective setting. Hence, we extend the state of the art Multi-objective optimization (MOO) solver, SMPSO, by incorporating EM in it. As a consequence, we develop the mathematical formalism of constriction fairness which is at the core of extended SMPSO algorithm. The proposed solver matches the performance of SMPSO across the ZDT, DTLZ and WFG problem suites and even outperforms it in certain instances.