Quantum Particle Swarm Optimization And Its Applications

Optimization problem is often encountered in industrial design problems, in order to solve a wide range of optimization problems, we has developed a lot of optimization algorithms, such as Ant colony algorithm, genetic algorithm and so on. The particle swarm optimization (PSO) originally developed by Kennedy and Eberhart in 1995. The algorithm is starting from a random solution, and through the fitness to evaluate the solutions. This algorithm with its less parame-ters, simple form, high precision and fast convergence advantages attracted more and more academic attention, and it demonstrate its superiority in solving prac-tical problems to. In order to improve the convergence, the Quantum-behaved particle swarm optimization(QPSO) developed in 2004 by Sun and others, which based on the standard pso. In the quantum space, particles can be search in the whole feasible solution space, thereby we can obtain the global optimal solution. Therefore, QPSO algorithm is a global guaranteed algorithm, which outperforms original PSO algorithm in search capabilities.This paper introduces the algorithm idea, algorithmic process, algorithmic parameters of the Particle Swarm and Quantum Particle Swarm Optimization algorithm. Then we do the mathematical analysis to the algorithms and improve the algorithms. After that we improved the QPSO algorithm use the Cauchy mutation to replace random number of the QPSO. As the Cauchy distribution features make the algorithm can be faster out of local minima. Finally, we can make use of the QPSO to solve some optimization problems. Especially, we can solve some of complex programming problems. Numerical experiments illustrate the superiority of the QPSO.Finally, the full text of the article summarizes are given with looked to the future.