An Immune Particle Swarm Optimization Algorithm for Solving Permutation Flowshop Problem

Chang Hua Qiu; Can Wang

doi:10.4028/www.scientific.net/KEM.419-420.133

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Research on Extrusion Velocity in Freeform Extrusion Fabrication of Aqueous Alumina Paste
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Research on Loading Method of High-Speed Closed Power Flow Test System
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An Immune Particle Swarm Optimization Algorithm for Solving Permutation Flowshop Problem
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A Subjective Evaluation Method for Man-Machine Interface Based on Grey Interval Relational Analysis
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Simulation Research on Temperature Field of Circular Cavity Hydrostatic Thrust Bearing
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A Contrast of Generation Sinusoidal Fringe Pattern Method Based on Phase Measurement Profilometry
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Fault Diagnosis and Analysis for Marine Planetary Gearbox
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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.