Differential evolution for sequencing and scheduling optimization
This paper presents a stochastic method based on the differential evolution (DE) algorithm to address a wide range of sequencing and scheduling optimization problems. DE is a simple yet effective adaptive scheme developed for global optimization over continuous spaces. In spite of its simplicity and...
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| Veröffentlicht in: | Journal of heuristics 2006-12, Vol.12 (6), p.395-411 |
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| container_title | Journal of heuristics |
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| creator | Nearchou, Andreas C. Omirou, Sotiris L. |
| description | This paper presents a stochastic method based on the differential evolution (DE) algorithm to address a wide range of sequencing and scheduling optimization problems. DE is a simple yet effective adaptive scheme developed for global optimization over continuous spaces. In spite of its simplicity and effectiveness the application of DE on combinatorial optimization problems with discrete decision variables is still unusual. A novel solution encoding mechanism is introduced for handling discrete variables in the context of DE and its performance is evaluated over a plethora of public benchmarks problems for three well-known NP-hard scheduling problems. Extended comparisons with the well-known random-keys encoding scheme showed a substantially higher performance for the proposed. Furthermore, a simple slight modification in the acceptance rule of the original DE algorithm is introduced resulting to a more robust optimizer over discrete spaces than the original DE. [PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1007/10732-006-3750-x |
| format | Article |
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| ispartof | Journal of heuristics, 2006-12, Vol.12 (6), p.395-411 |
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| language | eng |
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| subjects | Algorithms Benchmarks Heuristic Mutation Optimization Optimization algorithms Population Scheduling Sequential scheduling Stochastic models Studies Traveling salesman problem Variables |
| title | Differential evolution for sequencing and scheduling optimization |
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