Hybrid particle swarm optimisation with adaptively coordinated local searches for multimodal optimisation Online publication date: Thu, 28-May-2015
by Gang Xu; Hao Liu
International Journal of Computing Science and Mathematics (IJCSM), Vol. 6, No. 3, 2015
Abstract: Particle swarm optimisation (PSO) is a population-based stochastic search algorithm. Two common criticisms exist. First, PSO suffers premature convergence. Second, several existing PSO variants are designed for a specific search space thus an algorithm performing well on a diverse set of problems is lacking. In this paper, we propose a hybrid particle swarm optimisation with adaptively coordinated local searches, called NMRM-PSO, to make up the above demerits. These local search algorithms are the Nelder mead algorithm and the Rosenbrock method. NMRM-PSO has two alternative phases: the exploration phase realised by PSO and the exploitation phase completed by two adaptively coordinated local searches. Experiment results show that NMRM-PSO outperforms all of the tested PSO algorithms on most of multimodal functions in terms of solution quality, convergence speed and success rate.
Online publication date: Thu, 28-May-2015
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org