Abstract
There is a variety of methods for ranking objectives in multi-objective 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 many-objective 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.
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Castro-Gutierrez, J., Landa-Silva, D., Pérez, J.M. (2010). Improved Dynamic Lexicographic Ordering for Multi-Objective Optimisation. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_4
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DOI: https://doi.org/10.1007/978-3-642-15871-1_4
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