Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Parallel Hybrid Multi-Objective Island Model in Peer-to-Peer Environment
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Expert Systems with Applications: An International Journal
Solving a bi-objective flowshop scheduling problem by pareto-ant colony optimization
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Path relinking in pareto multi-objective genetic algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Memetic algorithm based task scheduling using probabilistic local search
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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The resolution of workshop problems such as the Flow Shop or the Job Shop has a great importance in many industrial areas. The criteria to optimize are generally the minimization of the makespan or the tardiness. However, few are the resolution approaches that take into account those different criteria simultaneously. This paper presents an approach based on hybrid genetic algorithms adapted to the multicriteria case. Several strategies of selection and diversity maintaining are presented. Their performances are evaluated and compared using different benchmarks. A parallel model is also proposed and implemented for the hybrid metaheuristic. It allows to increase the population size and the number of generations, and then leads to better results.