Chaos Quantum-behaved Particle Swarm Optimization Algorithm with Hybrid Discrete Variables

To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional quantum-behaved particle swarm optimization (QPSO) to handle complex functions with high-dimension, a novel method of judging the local convergence by the variance of the population's fitness was proposed, dynamic penalty function was constructed and the chaos quantum-behaved particle swarm optimization algorithm (CQPSO) was proposed. The program DCQPSO1.0 with hybrid discrete variables was developed. The proposed CQPSO method can reasonably deal with value adopting problems of hybrid discrete variables in optimization design and enhance searching efficiency. The computing examples of mechanical optimization design show that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.