An intelligent novel tripartite - (PSO-GA-SA) optimisation strategy. (2017)

• Record Type: Journal Article
• Authors: Owa, Kayode; Jackson, Lisa; Jackson, Tom
• Is Part Of: International journal of metaheuristics. Volume 6:Number 3(2017)
• Page Start: 210
• Page End: 233
• Publication Date: 2017
• Subjects: 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; Heuristic algorithms -- Periodicals; 006.3105
• Languages: English
• ISSNs: 1755-2176

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.