Multi-Objective QPSO Algorithms to Solve an Electromagnetic Benchmark Problem

Abstract

The paper proposes a new set of Quantum-behaved Particle Swarm Optimization (QPSO) multi-objective algorithms with the final goal to use them for the optimization of a two objective electromagnetic benchmark inspired by a real world application. Starting from some various flavors of single-objective QPSO algorithms, known as: classic, with Gaussian attractor and with random mean, the new multi-objective QPSO implementations integrate principles inspired from notorious algorithms such as NSGA II, OMOPSO and ε-MOEA. The proposed algorithms are tested on benchmark problems proposed by scientific communities working in Evolutionary Computation and Computational Electromagnetics.