A Modified Quantum-Behaved Particle Swarm Optimization for Constrained Optimization

Abstract

The method to handle the constraints is the key factor to success when we are trying to solve constrained optimization problems by quantum-behaved particle swarm optimization. In this paper, a modified quantum-behaved particle swarm optimization is proposed for constrained optimization. Double fitness values are defined for every particle. Whether the particle is better or not will be decided by its two fitness values. An adaptive strategy to keep a fixed proportion of infeasible particles is used in this method. Experimental results show that the modified algorithm is feasible and better on precision and convergence than quantum-behaved particle swarm optimization using a penalty function and other optimization algorithms.