Abstract
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 effiective 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.
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Jozefowiez, N., Semet, F., Talbi, EG. (2002). Parallel and Hybrid Models for Multi-objective Optimization: Application to the Vehicle Routing Problem. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_26
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DOI: https://doi.org/10.1007/3-540-45712-7_26
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