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.
Highlights
The 21st century has witnessed an increased amount and the quality of research output in order to meet up with the numerous and diverse real-world challenges confronting us today
This paper presents the successful implementation of a novel tripartite optimisation approach
This novel approach uses the best features of the combination of particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)
Summary
The 21st century has witnessed an increased amount and the quality of research output in order to meet up with the numerous and diverse real-world challenges confronting us today. Nesmachnow (2014) provided in his work an accurate overview of many cutting-edge approaches, elaborated on efficient available metaheuristic optimisation methods for solving difficult practical and theoretical problems and detailed the current and future research in the field. Taking into account the various and numerous research already carried out in the area of metaheuristics optimisation, literature reveals that many approaches have been implemented using individual metaheuristics approaches; only a few works exist in the area of hybrid metaheuristics optimisation such as the PSO, GA and SA approaches This proposed approach is designed to operate with high intelligence in order to explore diverse solution space, exploits good features of the individual methods in order to arrive at an optimum solution.
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