PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Hierarchical parallel approach for GSM mobile network design
Journal of Parallel and Distributed Computing
Metaheuristics and cooperative approaches for the Bi-objective Ring Star Problem
Computers and Operations Research
Experimental genetic operators analysis for the multi-objective permutation flowshop
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
ParadisEO-MOEO: a framework for evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Computers and Industrial Engineering
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
A parallel and distributed meta-heuristic framework based on partially ordered knowledge sharing
Journal of Parallel and Distributed Computing
Solving bi-objective flow shop problem with hybrid path relinking algorithm
Applied Soft Computing
A system-level infrastructure for multidimensional MP-SoC design space co-exploration
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on ESTIMedia'10
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Multi-objective optimization using evolutionary algorithms has been extensively studied in the literature. We propose formal methods to solve problems appearing frequently in the design of such algorithms. To evaluate the effectiveness of the introduced mechanisms, we apply them to the flow-shop scheduling problem. We propose a dynamic mutation Pareto genetic algorithm (GA) in which different genetic operators are used simultaneously in an adaptive manner, taking into account the history of the search. We present a diversification mechanism which combines sharing in the objective space as well as in the decision space, in which the size of the niche is automatically calculated. We also propose a hybrid approach which combines the Pareto GA with local search. Finally, we propose two performance indicators to evaluate the effectiveness of the introduced mechanisms.