Abstract
This paper presents a parallel hybrid exact multi-objective approach which combines two metaheuristics – a genetic algorithm (GA) and a memetic algorithm (MA), with an exact method – a branch and bound (B&B) algorithm. Such approach profits from both the exploration power of the GA, the intensification capability of the MA and the ability of the B&B to provide optimal solutions with proof of optimality. To fully exploit the resources of a computational grid, the hybrid method is parallelized according to three well-known parallel models – the island model for the GA, the multi-start model for the MA and the parallel tree exploration model for the B&B. The obtained method has been experimented and validated on a bi-objective flow-shop scheduling problem. The approach allowed to solve exactly for the first time an instance of the problem – 50 jobs on 5 machines. More than 400 processors belonging to 4 different administrative domains have contributed to the resolution process during more than 6 days.
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Mezmaz, M., Melab, N. & Talbi, EG. Combining Metaheuristics and Exact Methods for Solving Exactly Multi-objective Problems on the Grid. J Math Model Algor 6, 393–409 (2007). https://doi.org/10.1007/s10852-007-9063-8
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DOI: https://doi.org/10.1007/s10852-007-9063-8
Keywords
- Multi-objective optimization
- Hybridization
- Parallel computing
- Genetic/memetic algorithm
- Branch and bound
- Flow-shop