Multi-agent list-based threshold-accepting algorithm for numerical optimisation Online publication date: Tue, 10-Nov-2015
by Juan Lin; Yiwen Zhong
International Journal of Computing Science and Mathematics (IJCSM), Vol. 6, No. 5, 2015
Abstract: Traditional list-based threshold-accepting (LBTA) algorithm is similar with simulated annealing (SA) algorithm, depends on an intense local search method, and utilises a list filling procedure with threshold values to search the solution space effectively. Inspired by the learning ability of particle swarm optimisation (PSO), multi-agent LBTA (MLBTA) involves the learning knowledge to guide its sampling, explores the solution space in a co-evolution mode. Compare with multi-agent SA (MSA) algorithm adapting the same local search version, MLBTA incorporates a dynamic list of threshold values which is adapted according to the topology of the solution space and tunes only one parameter. Dispense with sophisticated parameters as MSA, MLBTA balances the intensification and diversification iteratively. Computational results on functions optimisation and protein structure prediction (PSP) problems show that MLBTA algorithm achieves better or comparable performances with MSA.
Online publication date: Tue, 10-Nov-2015
Go to Inderscience Online Journals to access the Full Text of this article.
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