Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.