Adaptive multi-context cooperatively coevolving particle swarm optimization for large-scale problems

Adaptive multi-context cooperatively coevolving particle swarm optimization for large-scale problems A novel adaptive multi-context cooperatively coevolving particle swarm optimization (AM-CCPSO) algorithm is proposed in an attempt to improve the performance on solving large-scale optimization problems (LSOP). Due to the curse of dimensionality, most optimization algorithms show their weaknesses on LSOP, and the cooperative co-evolution (CC) is often utilized to overcome such weaknesses. The basic CC framework employs one context vector for cooperatively, but greedily coevolving different subcomponents, which sometimes fails to find global optimum, especially on some complex non-separable LSOP. In the AM-CCPSO, more than one context vectors are employed to provide robust and effective co-evolution. These vectors are selected with respect to each particle of each subcomponent according to their own adaptive probabilities. In the AM-CCPSO, a new PSO updating rule is also proposed to exploit “four best positions” via Gaussian sampling. On a comprehensive set of benchmarks (up to 1000 real-valued variables), as well as on a real world application, the performance of AM-CCPSO can rival several state-of-the-art evolutionary algorithms. Experimental results indicate that the novel adaptive multi-context CC framework is effective to improve the performance of PSO on solving LSOP and can be generally extended in other evolutionary algorithms. Soft Computing Springer Journals

Adaptive multi-context cooperatively coevolving particle swarm optimization for large-scale problems

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Springer Berlin Heidelberg
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
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