Paper Title:
An Immune Particle Swarm Optimization Algorithm for Solving Permutation Flowshop Problem
  Abstract

To solve the permutation flowshop problem more effectively, a novel artificial immune particle swarm optimization (PSO) algorithm has been proposed. The new algorithm combined the biology immune system theory with particle swarm algorithm by the following phases. Firstly, the scheduling objective and constrain condition were served as antibodies while solutions was served as antigens. Secondly, the particles were encoded as workpiece processing sequence. Furthermore, a concentration selection strategy was adopted to maintain the particle diversity. Finally, comparing with genetic algorithm and PSO, case results showed that immune PSO algorithm not only optimized results and convergence velocity but also had a small fluctuation.

  Info
Periodical
Key Engineering Materials (Volumes 419-420)
Edited by
Daizhong Su, Qingbin Zhang and Shifan Zhu
Pages
133-136
DOI
10.4028/www.scientific.net/KEM.419-420.133
Citation
C. H. Qiu, C. Wang, "An Immune Particle Swarm Optimization Algorithm for Solving Permutation Flowshop Problem", Key Engineering Materials, Vols. 419-420, pp. 133-136, 2010
Online since
October 2009
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