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Information 2019, 10(1), 22; https://doi.org/10.3390/info10010022

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

School of Mathematics and Finance, Chuzhou University, Chuzhou 239000, China
Received: 26 October 2018 / Revised: 19 December 2018 / Accepted: 4 January 2019 / Published: 10 January 2019
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Abstract

Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate. View Full-Text
Keywords: quantum-behaved particle swarm optimization; weighted mean personal best position; adaptive local attractor quantum-behaved particle swarm optimization; weighted mean personal best position; adaptive local attractor
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Chen, S. Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor. Information 2019, 10, 22.

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