Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification

• Published in: Applied mathematics and computation Vol. 218; no. 8; pp. 4365 - 4383
• Main Authors: Shieh, Horng-Lin, Kuo, Cheng-Chien, Chiang, Chin-Ming
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 15.12.2011; Elsevier
• Subjects: Algorithms; Calculus of variations and optimal control; Computational efficiency; Computing time; Convergence; Elite reserve; Exact sciences and technology; Heuristic search; Mathematical analysis; Mathematical models; Mathematics; Metropolis process; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in mathematical programming, optimization and calculus of variations; Numerical methods in optimization and calculus of variations; Optimization; Particle swarm optimization; Sciences and techniques of general use; Searching; Simulated annealing; Heuristic search; Particle swarm optimization; Simulated annealing; Metropolis process; Elite reserve; Optimization method; Algorithm; Convergence; Stochastic programming; Convergence speed; Mathematical function; Numerical analysis; Algorithm performance; Applied mathematics; Variational calculus; Mathematical programming
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2011.10.012

The hybrid algorithm that combined particle swarm optimization with simulated annealing behavior (SA-PSO) is proposed in this paper. The SA-PSO algorithm takes both of the advantages of good solution quality in simulated annealing and fast searching ability in particle swarm optimization. As stochastic optimization algorithms are sensitive to their parameters, proper procedure for parameters selection is introduced in this paper to improve solution quality. To verify the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimization functions with different dimensions. The comparative works have also been conducted among different algorithms under the criteria of quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the results, the SA-PSO could have higher efficiency, better quality and faster convergence speed than compared algorithms.