Improved dynamic lexicographic ordering for multi-objective optimisation

Castro-Gutierrez, Juan; Landa-Silva, Dario; Moreno-Perez, Jose A.

Abstract

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.

Citation

Castro-Gutierrez, J., Landa-Silva, D., & Moreno-Perez, J. A. Improved dynamic lexicographic ordering for multi-objective optimisation. Presented at Parallel Problem Solving from Nature - PPSN XI, Lecture Notes in Computer Science, Vol. 6239

• Conference Name: Parallel Problem Solving from Nature - PPSN XI, Lecture Notes in Computer Science, Vol. 6239
• End Date: Sep 15, 2010
• Acceptance Date: Jul 11, 2010
• Publication Date: Sep 11, 2010
• Deposit Date: Aug 1, 2016
• Publicly Available Date: Aug 1, 2016
• Peer Reviewed: Peer Reviewed
• Keywords: multiobjective optimization, vehicle routing, swarm optimization
• Public URL: https://nottingham-repository.worktribe.com/output/706718
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-642-15871-1_4
• Additional Information: Note: Presented at the 11th International Conference on Parallel Problem Solving From Nature (PPSN 2010), Krakow Poland, September 2010. doi: 10.1007/978-3-642-15871-1_4
• Contract Date: Aug 1, 2016