An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem

As same with many evolutional algorithms, performance of simple PSO depends on its parameters, and it often suffers the problem of being trapped in local optima so as to cause premature convergence. In this paper, an improved particle swarm optimization with decline disturbance index (DDPSO), is pro...

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Vydané v:	Computers & operations research Ročník 45; s. 38 - 50
Hlavní autori:	Zhao, Fuqing, Tang, Jianxin, Wang, Junbiao, Jonrinaldi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.05.2014 Elsevier Pergamon Press Inc
Predmet:	Adaptive meta-Lamarckian strategy Algorithms Applied sciences Computer simulation Decision theory. Utility theory Decline disturbance Disturbances Effectiveness studies Exact sciences and technology Expanded job shop scheduling problem Genetic algorithms Job shops Local optimization Markov analysis Mathematical models Mathematical problems Multi-objective job shop scheduling problem Operational research and scientific management Operational research. Management science Optimization Optimization algorithms Particle swarm optimization Performance indices Production scheduling Scheduling, sequencing Searching Stochastic models Swarm intelligence Adaptive meta-Lamarckian strategy Multi-objective job shop scheduling problem Particle swarm optimization Expanded job shop scheduling problem Decline disturbance Markov process Local search Search strategy Probabilistic approach Pareto optimum Job shop Multiobjective programming Task scheduling Global optimum Scheduling Stochastic process Adaptive method Global solution Workload Global local method Trapping Neighborhood search Swarm intelligence Robustness
ISSN:	0305-0548, 1873-765X, 0305-0548
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Shrnutí:	As same with many evolutional algorithms, performance of simple PSO depends on its parameters, and it often suffers the problem of being trapped in local optima so as to cause premature convergence. In this paper, an improved particle swarm optimization with decline disturbance index (DDPSO), is proposed to improve the ability of particles to explore the global and local optimization solutions, and to reduce the probability of being trapped into the local optima. The correctness of the modification, which incorporated a decline disturbance index, was proved. The key question why the proposed method can reduce the probability of being trapped in local optima was answered. 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. The multi-objective combination problems with DDPSO for finding the pareto front was presented under certain performance index. Simulation results and comparisons with typical algorithms show the effectiveness and robustness of the proposed DDPSO.
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0305-0548 1873-765X 0305-0548
DOI:	10.1016/j.cor.2013.11.019