Title :
Chaos Quantum-behaved Particle Swarm Optimization Algorithm with Hybrid Discrete Variables
Author :
Luo, Youxin ; Li, Lingfang
Author_Institution :
Coll. of Mech. Eng., Hunan Univ. of Arts & Sci., Changde, China
Abstract :
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.
Keywords :
chaos; convergence; particle swarm optimisation; quantum computing; CQPSO method; chaos quantum-behaved particle swarm optimization algorithm; dynamic penalty function; hybrid discrete variables; local convergence sensitivity; low convergence speed; population fitness variance; program DCQPSO1.0; Algorithm design and analysis; Art; Chaos; Convergence; Design optimization; Educational institutions; Gears; Mechanical engineering; Particle swarm optimization; Quantum computing; PSO; QPSO; chaos searching; hybrid discrete variables; mechanical optimization;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.58