Improved Quantum Evolutionary Computation Based on Particle Swarm Optimization and Two-Crossovers

A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. Thi...

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Published in:Chinese physics letters 2009-12, Vol.26 (12), p.120304-120304
Main Authors: Duan, Hai-Bin, Xing, Zhi-Hui
Format: Article
Language:English
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Benchmarking
Combinatorial analysis
Convergence
Evolutionary algorithms
Optimization
Strategy
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description A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.
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subjects Benchmarking
Combinatorial analysis
Convergence
Evolutionary algorithms
Optimization
Strategy
title Improved Quantum Evolutionary Computation Based on Particle Swarm Optimization and Two-Crossovers
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