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Information 2015, 6(4), 633-649; doi:10.3390/info6040633

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 14 July 2015 / Revised: 4 October 2015 / Accepted: 6 October 2015 / Published: 13 October 2015
(This article belongs to the Section Information Theory and Methodology)
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Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the number of iterations is limited. A novel way of computing the local attractor for QPSO is proposed to improve QPSO’s performance in global searching, and this novel QPSO is denoted as EQPSO during which we can guarantee the particles are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iteration. We also discuss this way of computing the local attractor in mathematics. The results of test functions are compared between EQPSO and other optimization techniques (including six different PSO and seven different optimization algorithms), and the results found by the EQPSO are better than other considered methods. View Full-Text
Keywords: QPSO; optimization algorithm; local attractor; global optimum QPSO; optimization algorithm; local attractor; global optimum

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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. (CC BY 4.0).

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Jia, P.; Duan , S.; Yan, J. An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor. Information 2015, 6, 633-649.

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