Open AccessResearch on quorum sensing particle swarm optimization based on chaos
Author(s) -
Benyan Yu,
Hongwei Kang,
Yong Shen,
Xingping Sun,
Qingyi Chen
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/7/072027
Subject(s) - particle swarm optimization , multi swarm optimization , benchmark (surveying) , mathematical optimization , chaotic , chaos (operating system) , metaheuristic , computer science , swarm behaviour , derivative free optimization , meta optimization , test functions for optimization , optimization problem , algorithm , mathematics , artificial intelligence , computer security , geodesy , geography
particle swarm optimization (PSO) is a kind of swarm optimization algorithm with fast search speed, high efficiency and suitable for practical optimization problems. Once it was put forward, it has attracted extensive attention of scholars in various fields. A quorum sensing particle swarm optimization (CPSOQS) algorithm based on chaos is proposed to solve the shortcomings of traditional PSO algorithms, such as poor handling of discrete optimization problems and the loss of searching ability due to the fast particle velocity decline in the later stage. Firstly, the search ability of the algorithm is improved by generating chaotic sequences and mapping them to the solution space of the definition domain; Secondly, the quorum sensing mechanism of bacteria is introduced into PSO. Chaos search was used twice in the algorithm, which improved the global search ability. CEC 2005 benchmark is used to test the performance of the algorithm. The experimental results show that among the 14 selected test functions, 11 test functions have better experimental results than the comparison algorithm.