Paper Title:
A Modified Particle Swarm Optimization Algorithm with Cases Studies
  Abstract

s paper presents a hybrid algorithm, the “particle swarm optimization with simulated annealing behavior (SA-PSO)” algorithm, which combines the advantages of good solution quality in simulated annealing and fast calculation in particle swarm optimization. As stochastic optimization algorithms are sensitive to its parameters, this paper introduces criteria in selecting parameters to improve solution quality. To prove the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimized functions of different dimensions. The results made from different algorithms are then compared between the quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the simulation results, SA-PSO obtained higher efficiency, better quality and faster convergence speed than other compared algorithms.

  Info
Periodical
Advanced Materials Research (Volumes 268-270)
Edited by
Feng Xiong
Pages
823-828
DOI
10.4028/www.scientific.net/AMR.268-270.823
Citation
C. C. Kuo, H. C. Chen, T. F. Taso, C. M. Chiang, "A Modified Particle Swarm Optimization Algorithm with Cases Studies", Advanced Materials Research, Vols. 268-270, pp. 823-828, 2011
Online since
July 2011
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Price
$32.00
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