Abstract
This paper proposes a hybrid particle swarm optimization algorithm and for solving Flow Shop Scheduling Problems (FSSP) and Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. By reasonablycombining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). On the other hand, we analyze the convergence of PSO algorithm with an optimum keeping strategy and TS, SA algorithms by Markov chain theory at a different aspect in this paper, and HPTS algorithm is proved to be convergent. This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. Meanwhile, the convergence of HPTS is proved. For example, in 30 and 43 benchmarks, 7 new upper bounds and 6 new upper bounds are obtained by the HPTS algorithm, respectively.
| Original language | English |
|---|---|
| Title of host publication | GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion |
| Pages | 307-314 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - Aug 20 2012 |
| Event | 14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States Duration: Jul 7 2012 → Jul 11 2012 |
Publication series
| Name | GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion |
|---|
Other
| Other | 14th International Conference on Genetic and Evolutionary Computation, GECCO'12 |
|---|---|
| Country | United States |
| City | Philadelphia, PA |
| Period | 7/7/12 → 7/11/12 |
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All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
Cite this
Hybrid Particle Swarm Optimization and convergence analysis for scheduling problems. / Zhang, Xue Feng; Koshimura, Miyuki; Fujita, Hiroshi; Hasegawa, Ryuzo.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion. 2012. p. 307-314 (GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hybrid Particle Swarm Optimization and convergence analysis for scheduling problems
AU - Zhang, Xue Feng
AU - Koshimura, Miyuki
AU - Fujita, Hiroshi
AU - Hasegawa, Ryuzo
PY - 2012/8/20
Y1 - 2012/8/20
N2 - This paper proposes a hybrid particle swarm optimization algorithm and for solving Flow Shop Scheduling Problems (FSSP) and Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. By reasonablycombining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). On the other hand, we analyze the convergence of PSO algorithm with an optimum keeping strategy and TS, SA algorithms by Markov chain theory at a different aspect in this paper, and HPTS algorithm is proved to be convergent. This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. Meanwhile, the convergence of HPTS is proved. For example, in 30 and 43 benchmarks, 7 new upper bounds and 6 new upper bounds are obtained by the HPTS algorithm, respectively.
AB - This paper proposes a hybrid particle swarm optimization algorithm and for solving Flow Shop Scheduling Problems (FSSP) and Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. By reasonablycombining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). On the other hand, we analyze the convergence of PSO algorithm with an optimum keeping strategy and TS, SA algorithms by Markov chain theory at a different aspect in this paper, and HPTS algorithm is proved to be convergent. This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. Meanwhile, the convergence of HPTS is proved. For example, in 30 and 43 benchmarks, 7 new upper bounds and 6 new upper bounds are obtained by the HPTS algorithm, respectively.
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UR - http://www.scopus.com/inward/citedby.url?scp=84865025856&partnerID=8YFLogxK
U2 - 10.1145/2330784.2330829
DO - 10.1145/2330784.2330829
M3 - Conference contribution
AN - SCOPUS:84865025856
SN - 9781450311786
T3 - GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
SP - 307
EP - 314
BT - GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
ER -