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HC-PSOGWO: Hybrid Crossover Oriented PSO and GWO based Co-Evolution for Global optimization
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 162 - 167
In order to introduce and maintain an optimum balance between exploration and exploitation in the search space, a hybrid crossover oriented Particle Swarm optimization(PSO) and Grey Wolf optimization(GWO) based co-evolution structure, abbreviated as HC-PSOGWO is proposed in this paper. The proposed HC-PSOGWO mainly focuses on better generalization, search procedure and diversification. Here both particles and wolves, as search agents, concurrently and independently explore the entire search space for optimal results. The learning procedure of GWO is also modified. Once, the exploration process is over, the evolved search agents from both the optimization techniques are crossed, they communicate among themselves, better agents are identified in the process and they are again allowed to modify the swarms targeted by the individual technique. The performance of the proposed technique is tested on 23 well known classical benchmark functions, one real world optimization problem and compared with several established optimization techniques. The experimental results and related analysis show that the proposed HC-PSOGWO is significantly better than other existing techniques. © 2019 IEEE.
About the journal
JournalData powered by TypesetProceedings of 2019 IEEE Region 10 Symposium, TENSYMP 2019
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.