Research On Adaptive And Cooperative Quantum-behaved Particle Swarm Algorithm

Evolutionary Computation is an important branch of artificial intelligence research that has been given broad attention in recent years; it is also a main part of intelligent information processing as well. As an optimization algorithm based on the theory of biologic evolution, the most outstanding advantage of Evolutionary Computation is its strong global optimizing capability as compared with other optimization algorithms.This paper discusses a novel class of evolutionary computation technique-Swarm Intelligence Algorithm, among which the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a recently proposed approach and is a variant of original Particle Swarm Optimization (PSO). QPSO is global convergent and will be a promising solver for complex optimization problem, which is shown by some previous work. Thus, the research of this paper will be of somewhat significance in evolutionary computation area.In this paper, the traditional evolutionary algorithm is firstly formulated, Genetic Algorithm (GA) in particular and in turn the Swarm Intelligence Algorithm including PSO and QPSO. As we known, the most complained problem about evolutionary computation is Convergent Performance (CP), which is also inevitable in QPSO. Thus this work focuses on how to overcome it and consequently propose two enhanced QPSO: Adaptive QPSO (AQPSO) and Cooperative QPSO (CQPSO). In AQPSO, an approach how to select parameter is proposed to enhance the global search ability of the QPSO algorithm. It can efficiently influence the convergence of the particle in QPSO. In proposed CQPSO, cooperative behavior is employed to significantly improve the performance of the original algorithm. Compared with QPSO algorithm, this new approach can improve the algorithm's chances of finding a good solution as the dimensionality of the problem increases. Application of the AQPSO and CQPSO algorithms on several benchmark optimization problems shows a marked improvement in performance and stability over the PSO and QPSO.The applicability of QPSO is also researched to discrete problems to prove the superiority of QPSO to PSO and GA algorithms. The application of BPSO and BQPSO algorithms is explored to stack filters design. There are vast stack filters, so the main problem of stack filters design is optimization. The stack filters optimization based on PSO and QPSO algorithm make the problem be the Positive Bool Function optimization problem. Then PSO and QPSO algorithms are used to generate a best Positive Bool Function. Experimental results show that the QPSO algorithm can efficiently suppress noise better than PSO and GA algorithms under the same iterations and particle numbers. Therefore, QPSO is the efficient algorithm for stack filters optimization problem.