Quantum Evolutionary Algorithm based on Particle Swarm theory in multiobjective problems

Abstract

Quantum Evolutionary Algorithm (QEA) is an optimization algorithm based on the concept of quantum computing and Particle Swarm Optimization (PSO) algorithm is a population based intelligent search technique. Both these techniques have good performance to solve optimization problems. PSEQEA combines the PSO with QEA to improve the performance of QEA and it can solve single objective optimization problem efficiently and effectively. In this paper, PSEQEA is studied to solve multi-objective Optimization (MO) problems. Some well-known non-trivial functions are used to observe the performance of PSEQEA to detect the Pareto optimal points and the shape of the Pareto front using both Fixed Weighted Aggregation method and Adaptive Weighted Aggregation method. Moreover, Vector Evaluated PSEQEA (VEPSEQEA) borrows concept from Schaffer's Vector Evaluated Genetic Algorithm (VEGA) that can also cope with MO problems. Simulation results show that PSEQEA and VEPSEQEA perform better than PSO and VEPSO to discover the Pareto frontier.