Journal Article

Adaptive multifactorial particle swarm optimisation

Gong, Maoguo, Tang, Zedong

2019

Overview

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.

• Publisher: The Institution of Engineering and Technology,John Wiley & Sons, Inc,Wiley
• Subject: Adaptive algorithms / adaptive multifactorial particle swarm optimisation / additional searching experiences / B0260 Optimisation techniques / benchmark problems / broad search space / C1180 Optimisation techniques / C6170K Knowledge engineering techniques / Cultural transmission / different complementarity / different stages / evolution process / Evolutionary algorithms / fixed inter-task learning probability / Genetic algorithms / inter-task crossover / inter-task knowledge / inter-task learning mechanism / inter-task learning-based information transferring mechanism / inter-task particles / Knowledge / Knowledge management / learning (artificial intelligence) / Machine learning / MFPSO / Multitasking / particle swarm optimisation / Particle swarm optimization / Performance evaluation / probability / problem dependent / Problem solving / relatively narrow area / Research Article / Searching / searching step / self-adaption strategy / state-of-the-art algorithms