Open AccessA modified comprehensive learning particle swarm optimizer and its application in cylindricity error evaluation problem
Author(s) -
Qing Wu,
Chunjiang Zhang,
Mengya Zhang,
FaJun Yang,
Liang Gao
Publication year - 2019
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2019057
Subject(s) - latin hypercube sampling , particle swarm optimization , local search (optimization) , hypercube , metaheuristic , computer science , mathematical optimization , set (abstract data type) , sampling (signal processing) , swarm behaviour , process (computing) , swarm intelligence , algorithm , artificial intelligence , mathematics , monte carlo method , statistics , filter (signal processing) , parallel computing , computer vision , programming language , operating system
Particle swarm optimizer was proposed in 1995, and since then, it has become an extremely popular swarm intelligent algorithm with widespread applications. Many modified versions of it have been developed, in which, comprehensive learning particle swarm optimizer is a very powerful one. In order to enhance its performance further, a local search based on Latin hypercube sampling is combined with it in this work. Due to that a hypercube should become smaller and smaller for better local search ability during the search process, a control method is designed to set the size of the hypercube. Via numerical experiments, it can be observed that the comprehensive learning particle swarm optimizer with the local search based on Latin hypercube sampling has a strong ability on both global and local search. The hybrid algorithm is applied in cylindricity error evaluation problem and it outperforms several other algorithms.