Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/23119

Title: An intelligent novel tripartite - (PSO-GA-SA) optimization strategy
Authors: Owa, Kayode
Jackson, Lisa M.
Jackson, Thomas
Keywords: Genetic algorithms (GA)
Particle swarm optimization (PSO)
Simulated annealing (SA)
Nonlinear programming (NLP)
Non-deterministic polynomial time hard (NP-hard)
Issue Date: 2016
Publisher: Inderscience Enterprises Limited
Citation: OWA, K., JACKSON, L.M. and JACKSON, T., 2016. An intelligent novel tripartite - (PSO-GA-SA) optimization strategy. International Journal of Metaheuristics, 6 (3), pp. 210-233.
Abstract: A solution approach for many challenging and non-differentiable optimization tasks in industries is the use of non-deterministic meta-heuristic methods. Some of these approaches include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). However, with the implementation usage of these robust and stochastic optimization approaches, there are still some predominant issues such as the problem of the potential solution being trapped in a local minima solution space. Other challenges include the untimely convergence and the slow rate of arriving at optimal solutions. In this research study, a tripartite version (PSO-GA-SA) is proposed to address these deficiencies. This algorithm is designed with the full exploration of all the capabilities of PSO, GA and SA functioning simultaneously with a high level of intelligent system techniques to exploit and exchange relevant population traits in real time without compromising the computational time. The design algorithm further incorporates a variable velocity component that introduces random intelligence depending on the fitness performance from one generation to the other. The robust design is validated with known mathematical test function models. There are substantial performance improvements when the novel PSO-GA-SA approach is subjected to three test functions used as case studies. The results obtained indicate that the new approach performs better than the individual methods from the fitness function deviation point of view and in terms of the total simulation time whilst operating with both a reduced number of generations and populations. Moreover, the new novel approach offers more beneficial trade-off between exploration and exploitation of PSO, GA and SA. This novel design is implemented using an object oriented programming approach and it is expected to be compatible with a variety of practical problems with specified input-output pairs coupled with constraints and limitations on the available resources.
Description: This is an Open Access Article. It is published by Inderscience under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: This work is supported financially by the Engineering and Physical Sciences Research Council (EPSRC) under the project titled ‘Adaptive Informatics for Intelligent Manufacturing (AI2M)’- (EPSRC Reference number is EP/K014137/1).
Version: Published
DOI: 10.1504/IJMHEUR.2017.10002057
URI: https://dspace.lboro.ac.uk/2134/23119
Publisher Link: http://dx.doi.org/10.1504/IJMHEUR.2017.10002057
ISSN: 1755-2184
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

Files associated with this item:

File Description SizeFormat
1954678310210001000.pdfPublished version1.05 MBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.