Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification
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 stochas...
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| Published in | Applied mathematics and computation Vol. 218; no. 8; pp. 4365 - 4383 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier Inc
15.12.2011
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0096-3003 1873-5649 |
| DOI | 10.1016/j.amc.2011.10.012 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Shieh, Horng-Lin Chiang, Chin-Ming Kuo, Cheng-Chien |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification |
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