A new hybrid particle swarm and simulated annealing stochastic optimization method

• Published in: Applied soft computing Vol. 60; pp. 634 - 654
• Main Authors: Javidrad, F., Nazari, M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-11-2017
• Subjects: Global optimization; Hybridization; Laminated composites; Particle swarm optimization (PSO); Simulated annealing (SA); Laminated composites; Simulated annealing (SA); Global optimization; Hybridization; Particle swarm optimization (PSO)
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.07.023

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•Development of a new hybrid PSO-SA optimization method.•Numerical validation of the proposed method using a number of benchmark functions.•Using three criteria for comparative work.•Finding near optimum parameters of the proposed method.•Application of the proposed algorithm in two engineering problems.
A novel hybrid particle swarm and simulated annealing stochastic optimization method is proposed. The proposed hybrid method uses both PSO and SA in sequence and integrates the merits of good exploration capability of PSO and good local search properties of SA. Numerical simulation has been performed for selection of near optimum parameters of the method. The performance of this hybrid optimization technique was evaluated by comparing optimization results of thirty benchmark functions of different dimensions with those obtained by other numerical methods considering three criteria. These criteria were stability, average trial function evaluations for successful runs and the total average trial function evaluations considering both successful and failed runs. Design of laminated composite materials with required effective stiffness properties and minimum weight design of a three-bar truss are addressed as typical applications of the proposed algorithm in various types of optimization problems. In general, the proposed hybrid PSO-SA algorithm demonstrates improved performance in solution of these problems compared to other evolutionary methods The results of this research show that the proposed algorithm can reliably and effectively be used for various optimization problems.