An improved quantum-behaved particle swarm optimization algorithm based on linear interpolation

Jiang, Shouyong and Yang, S. (2014) An improved quantum-behaved particle swarm optimization algorithm based on linear interpolation. In: 2014 IEEE Congress on Evolutionary Computation (CEC), 6-11th July 2014, Beijing, China.

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Quantum-behaved particle swarm optimization (QPSO) has shown to be an effective algorithm for solving global optimization problems that are of high complexity. This paper presents a new QPSO algorithm, denoted LI-QPSO, which employs a model-based linear interpolation method to strengthen the local search ability and improve the precision and convergence performance of the QPSO algorithm. In LI-QPSO, linear interpolation is used to approximate the objective function around a pre-chosen point with high quality in the search space. Then, local search is used to generate a promising trial point around this pre-chosen point, which is then used to update the worst personal best point in the swarm. Experimental results show that the proposed algorithm provides some significant improvements in performance on the tested problems. © 2014 IEEE.

Additional Information:cited By 7; Conference of 2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference Date: 6 July 2014 Through 11 July 2014; Conference Code:114617
Keywords:Global optimization, Particle swarm optimization (PSO), Convergence performance, Effective algorithms, Global optimization problems, Linear Interpolation, Objective functions, QPSO algorithms, Quantum-behaved particle swarm optimization, Quantum-behaved Particle Swarm Optimization algorithms, Interpolation
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35673
Deposited On:01 May 2019 14:10

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