Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions

By Yulian Cao, Hanxun Zhang, Wenfeng Li, Mengchu Zhou, Yu Zhang, W. Chaovalitwongse. IEEE Transactions on Evolutionary Computation, 2019

341 citations recorded by the source corpus.

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO’s strong global search capability and LS’s fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/4a8a8be1cdc2aba2f60f29c228aaff7e0285d988

Citations in the corpus

• Slime mould algorithm: A new method for stochastic optimization (2020, neutral): “[53], PBIL [54], DE [55] and advanced MAs: AGA[56], BLPSO [57], CLPSO [58], CBA [59], RCBA [60], CDLOBA [61], m_SCA [62], IWOA [63], LWOA [64], and CSSA [65].”