An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem
A flexible flow-shop scheduling (FFS) with nonidentical parallel machines for minimizing the maximum completion time or makespan is a well-known combinational problem. Since the problem is known to be strongly NP-hard, optimization can either be the subject of optimization approaches or be implement...
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| Published in | Advances in Mechanical Engineering Vol. 2013; no. 2013; pp. 1 - 13 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Cairo, Egypt
Hindawi Puplishing Corporation
2013
SAGE Publications Sage Publications Ltd. (UK) SAGE Publishing |
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| Abstract | A flexible flow-shop scheduling (FFS) with nonidentical parallel machines for minimizing the maximum completion time or makespan is a well-known combinational problem. Since the problem is known to be strongly NP-hard, optimization can either be the subject of optimization approaches or be implemented for some approximated cases. In this paper, an improved genetic-simulated annealing algorithm (IGAA), which combines genetic algorithm (GA) based on an encoding matrix with simulated annealing algorithm (SAA) based on a hormone modulation mechanism, is proposed to achieve the optimal or near-optimal solution. The novel hybrid algorithm tries to overcome the local optimum and further to explore the solution space. To evaluate the performance of IGAA, computational experiments are conducted and compared with results generated by different algorithms. Experimental results clearly demonstrate that the improved metaheuristic algorithm performs considerably well in terms of solution quality, and it outperforms several other algorithms. |
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| AbstractList | A flexible flow-shop scheduling (FFS) with nonidentical parallel machines for minimizing the maximum completion time or makespan is a well-known combinational problem. Since the problem is known to be strongly NP-hard, optimization can either be the subject of optimization approaches or be implemented for some approximated cases. In this paper, an improved genetic-simulated annealing algorithm (IGAA), which combines genetic algorithm (GA) based on an encoding matrix with simulated annealing algorithm (SAA) based on a hormone modulation mechanism, is proposed to achieve the optimal or near-optimal solution. The novel hybrid algorithm tries to overcome the local optimum and further to explore the solution space. To evaluate the performance of IGAA, computational experiments are conducted and compared with results generated by different algorithms. Experimental results clearly demonstrate that the improved metaheuristic algorithm performs considerably well in terms of solution quality, and it outperforms several other algorithms. A flexible flow-shop scheduling (FFS) with nonidentical parallel machines for minimizing the maximum completion time or makespan is a well-known combinational problem. Since the problem is known to be strongly NP-hard, optimization can either be the subject of optimization approaches or be implemented for some approximated cases. In this paper, an improved geneticsimulated annealing algorithm (IGAA), which combines genetic algorithm (GA) based on an encoding matrix with simulated annealing algorithm (SAA) based on a hormone modulation mechanism, is proposed to achieve the optimal or near-optimal solution. The novel hybrid algorithm tries to overcome the local optimum and further to explore the solution space. To evaluate the performance of IGAA, computational experiments are conducted and compared with results generated by different algorithms. Experimental results clearly demonstrate that the improved metaheuristic algorithm performs considerably well in terms of solution quality, and it outperforms several other algorithms. |
| Audience | Academic |
| Author | Zheng, Kun Tang, Dunbing Cai, Qixiang Dai, Min |
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| Title | An Improved Genetic-Simulated Annealing Algorithm Based on a Hormone Modulation Mechanism for a Flexible Flow-Shop Scheduling Problem |
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