Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Natural Computing: an international journal
Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem
Natural Computing: an international journal
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A bi-objective vehicle routing problem with time windows: A real case in Tenerife
Applied Soft Computing
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There is a variety of methods for ranking objectives in multiobjective optimization and some are difficult to define because they require information a priori (e.g. establishing weights in a weighted approach or setting the ordering in a lexicographic approach). In manyobjective optimization problems, those methods may exhibit poor diversification and intensification performance. We propose the Dynamic Lexicographic Approach (DLA). In this ranking method, the priorities are not fixed, but they change throughout the search process. As a result, the search process is less liable to get stuck in local optima and therefore, DLA offers a wider exploration in the objective space. In this work, DLA is compared to Pareto dominance and lexicographic ordering as ranking methods within a Discrete Particle Swarm Optimization algorithm tackling the Vehicle Routing Problem with Time Windows.