Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

Na Tian,Zhicheng Ji

doi:10.1155/2015/940592

Abstract

A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO) for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection) is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

Highlights

Most real-world optimization problems have more than one objective, with at least two objectives conflicting with each other
The results demonstrate that quantum-behaved particle swarm optimization (QPSO) with preference order has comparative performance with sigma value according to different number of objectives
It is easy to note that QPSO with preference order has comparative performance with sigma value

Summary

Introduction

Most real-world optimization problems have more than one objective, with at least two objectives conflicting with each other. The conflicting objectives lead to a problem where a single solution does not exist. A set of optimal trade-off solutions exists, which are referred to as the paretooptimal front or pareto front. This kind of optimization problems is referred to as multiobjective optimization problems. Deb et al [1] gave rise to a fast and elitist multiobjective genetic algorithm (NSGA-II) to reduce the computational complexity and adopted an elitism strategy. A particle swarm optimization (PSO) with pareto dominance was proposed in [3], in which a secondary repository and a special mutation operator were incorporated. The great difference between genetic algorithm (GA) and PSO exists for solving the multiobjective optimization problems.

Methods

Results

Conclusion