Abstract
Hybridization is an integrated framework that combines the merits of algorithms to improve the performance of an optimizer. In this chapter, the synergism of the improved version of particle swarm optimization (PSO) and differential evolution (DE) algorithms are invoked to construct a hybrid algorithm. The proposed method is executed in an interleaved fashion for balancing exploration and exploitation dilemma in the evolution process. The results are tested on ten real protein instances, taken from the protein data bank. The effectiveness of the proposed algorithm is evaluated through qualitative and quantitative comparisons with other hybridization of PSO and DE; and comprehensive learning PSO algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
