Open Access Article

Title: An intelligent novel tripartite - (PSO-GA-SA) optimisation strategy

Authors: Kayode Owa; Lisa Jackson; Tom Jackson

Addresses: Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire LE11 3TU, UK ' Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire LE11 3TU, UK ' Centre for Information Management, The School of Business and Economics, Loughborough University, Leicestershire LE11 3TU, UK

Abstract: This paper presents a tripartite version of particle swarm optimisation, genetic algorithm, and simulated annealing (PSO-GA-SA) optimisation strategy addressing some predominant issues such as the problem of the potential solution being trapped in a local minima solution space, the untimely convergence and the slow rate of arriving at optimal solutions. This strategy is designed with an intelligence beneficiary trade-off between exploration and exploitation of the full potential of all the capabilities of PSO, GA, and SA functioning simultaneously. The design algorithm further incorporates a variable velocity component that introduces random intelligence. There are substantial performance improvements when the novel robust design is first validated with three test functions for the initial case studies. To demonstrate the capabilities to handle complexities and establish scalability in the implementation of the proposed approach, the optimisation strategy is further applied to a high-integrity protection system (HIPS) which is a real-life safety system design optimisation problem with increased number of input variables, constraints, and limitations on the available resources. The novel design performs better than their individual methods using the number of fitness evaluations, as the performance metrics, whilst operating with both a reduced number of generations and initial number of starting potential solutions.

Keywords: EAs; evolutionary algorithms; GA; genetic algorithms; HIPS; high integrity protection system; metaheuristic optimisation; NLP; non-linear programming; NP-hard; non-deterministic polynomial time hard; optimisation test functions; PSO; particle swarm optimisation; safety systems; SA; simulated annealing.

DOI: 10.1504/IJMHEUR.2017.085125

International Journal of Metaheuristics, 2017 Vol.6 No.3, pp.210 - 233

Received: 10 Dec 2015
Accepted: 18 Oct 2016

Published online: 25 Jun 2017 *