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Computational Intelligence and Neuroscience
Volume 2017, Article ID 2782679, 15 pages
https://doi.org/10.1155/2017/2782679
Research Article

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization

1College of Pipeline and Civil Engineering, China University of Petroleum, Qingdao 266580, China
2Shengli College, China University of Petroleum, Dongying, Shandong 257000, China

Correspondence should be addressed to Ming-hai Xu; nc.ude.cpu@iahgnim

Received 30 January 2017; Revised 10 April 2017; Accepted 20 April 2017; Published 25 May 2017

Academic Editor: Ezequiel López-Rubio

Copyright © 2017 Tao Sun and Ming-hai Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.