Fairly Constricted Multi-Objective Particle Swarm Optimization
This is an incremental improvement for multi-objective optimization researchers and practitioners.
The authors extended the multi-objective optimization solver SMPSO by incorporating exponentially-averaged momentum, developing a mathematical formalism of constriction fairness, and found that it matches SMPSO's performance on standard problem suites and outperforms it in some cases.
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.