Journal of System Simulation
Abstract
Abstract: To address the problems of population diversity loss and the tendency to fall into local optimality in the PSO (particle swarm optimization)algorithm in dealing with unrelated parallel batch scheduling problems, an improved scheduling optimization algorithm for PSO is proposed for minimizing the maximum completion time solution. A real number encoding based on the sequence of artifacts is used for the encoding operation. A new strategy based on J_B local search is designed based on the mixed integer programming model of the problem. The Metropolis criterion of the simulated annealing algorithm isintroduced into the individual extreme value search of the population particles.The performance of the algorithm is tested with randomly generated small,medium and large instances and compared with proposed metaheuristic for this scheduling problem and three other metaheuristics. The experimental results and statistical tests shows that the algorithm performs significantly better than the comparison algorithm.
Recommended Citation
Du, Lizhen; Ye, Tao; Wang, Yuhao; and Zhang, Yajun
(2023)
"Improved Particle Swarm Algorithm of Unrelated Parallel Batch Scheduling Optimization,"
Journal of System Simulation: Vol. 35:
Iss.
7, Article 12.
DOI: 10.16182/j.issn1004731x.joss.22-0367
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss7/12
First Page
1549
Last Page
1561
CLC
TP391.9
Recommended Citation
Du Lizhen, Ye Tao, Wang Yuhao, et al. Improved Particle Swarm Algorithm of Unrelated Parallel Batch Scheduling Optimization[J]. Journal of System Simulation, 2023, 35(7): 1549-1561.
DOI
10.16182/j.issn1004731x.joss.22-0367
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