Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A tabu search heuristic for the vehicle routing problem
Management Science
A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Design of multi-objective evolutionary algorithms: application to the flow-shop scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows
Computational Optimization and Applications
Hierarchical parallel approach for GSM mobile network design
Journal of Parallel and Distributed Computing
Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization
The Consistent Vehicle Routing Problem
Manufacturing & Service Operations Management
A population-based local search for solving a bi-objective vehicle routing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
A multiobjectivization approach for vehicle routing problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
On the interactive resolution of multi-objective vehicle routing problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Analyzing a unified ant system for the VRP and some of its variants
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Solving a bi-objective vehicle routing problem by Pareto-ant colony optimization
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
A parallel heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery
Computers and Operations Research
Enhancements of NSGA II and its application to the vehicle routing problem with route balancing
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Minimal load constrained vehicle routing problems
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
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Solving a multi-objective problem means to find a set of solutions called the Pareto frontier. Since evolutionary algorithms work on a population of solutions, they are well-adapted to multi-objective problems. When they are designed, two purposes are taken into account: they have to reach the Pareto frontier but they also have to find solutions all along the frontier. It is the intensification task and the diversification task. Mechanisms dealing with these goals exist. But with very hard problems or benchmarks of great size, they may not be effective enough. In this paper, we investigate the utilization of parallel and hybrid models to improve the intensification task and the diversification task. First, a new technique inspired by the elitism is used to improve the diversification task. This new method must be implemented by a parallel model to be useful. Second, in order to amplify the diversification task and the intensification task, the parallel model is extended to a more general island model. To help the intensification task, a hybrid model is also used. In this model, a specially defined parallel tabu search is applied to the Pareto frontier reached by an evolutionary algorithm. Finally, those models are implemented and tested on a bi-objective vehicle routing problem.