Adaptive multifactorial particle swarm optimisation. Issue 1 (20th February 2019)

• Record Type: Journal Article
• Authors: Tang, Zedong; Gong, Maoguo
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 1(2019)
• Page Start: 37
• Page End: 46
• Publication Date: 2019-02-20
• Subjects: particle swarm optimisation -- learning (artificial intelligence) -- probability; additional searching experiences -- different complementarity -- self‐adaption strategy -- state‐of‐the‐art algorithms -- benchmark problems -- inter‐task knowledge -- adaptive multifactorial particle swarm optimisation -- MFPSO -- relatively narrow area -- inter‐task particles -- fixed inter‐task learning probability -- evolution process -- problem dependent -- different stages -- inter‐task learning‐based information transferring mechanism -- inter‐task learning mechanism -- searching step -- inter‐task crossover -- broad search space; B0260 Optimisation techniques -- C1180 Optimisation techniques -- C6170K Knowledge engineering techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.1090
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Existing multifactorial particle swarm optimisation (MFPSO) algorithms only explore a relatively narrow area between the inter‐task particles. Meanwhile, these algorithms use a fixed inter‐task learning probability throughout the evolution process. However, the parameter is problem dependent and can be various at different stages of the evolution. In this work, the authors devise an inter‐task learning‐based information transferring mechanism to replace the corresponding part in MFPSO. This inter‐task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter‐task crossover. By this mean, the particles can explore a broad search space when utilising the additional searching experiences of other tasks. In addition, to enhance the performance on problems with different complementarity, they design a self‐adaption strategy to adjust the inter‐task learning probability according to the performance feedback. They compared the proposed algorithm with the state‐of‐the‐art algorithms on various benchmark problems. Experimental results demonstrate that the proposed algorithm can transfer inter‐task knowledge efficiently and perform well on the problems with different complementarity.