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1 2012

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An Effective Hybrid Particle Swarm Optimization with Decline Disturbance Index for Expanded Job-shop Scheduling Problem

Zhao F., Tang J., Zou J., Di J.

Przegląd Elektrotechniczny

2012

R. 88, nr 1b

34-38

In this paper, an improved particle swarm optimization with decline disturbance index (DDPSO), is proposed for expanded job-shop scheduling problem (EJSSP). To balance the exploration and exploitation abilities of DDPSO, both PSO-based global search and an adaptive local search are designed and applied simultaneously in the proposed DDPSO. An index was added when the velocity of the particle is prone to stagnation in the middle and later evolution periods. The modification improves the ability of particles to explore the global and local optimization solutions, and reduces the probability of being trapped into the local optima. Theoretical analysis, which is based on stochastic processes, proves that the trajectory of particle is a Markov processes and DDPSO algorithm converges to the global optimal solution with mean square merit. After the exploration based on DDPSO, neighborhood search strategy is used in a local search and an adaptive Meta-Lamarckian strategy is employed to dynamically decide which neighborhood should be selected to stress exploitation in each generation. Simulation results and comparisons with typical algorithms show the effectiveness and robustness of the proposed DDPSO.

Opisano ulepszony al;gorytm mrówkowy z indeksem DDPSO do rozwiązywania problemu EJSSP (expanded job-shop scheduling problem). Dodano indeks kiedy szybkość cząstki jest podatna na stagnację w środku i końcowej części procesu ewolucji. Analiza teoretyczna bazująca na ;procesach stochastycznych dowodzi że trajektoria cząstki jest procesem Markova i algorytm DDPSO pokrywa się z rozwiązaniem globalnego optimum metodą średniokwadratową.