Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails

Li, Hui and Landa-Silva, Dario and Gandibleux, Xavier (2010) Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails. In: Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010), 18-23 July 2010, Barcelona, Spain.

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Abstract

Recently, the research on quantum-inspired evolutionary algorithms (QEA) has attracted some attention in the area of evolutionary computation. QEA use a probabilistic representation, called Q-bit, to encode individuals in population. Unlike standard evolutionary algorithms, each Q-bit individual is a probability model, which can represent multiple solutions. Since probability models store global statistical information of good solutions found previously in the search, QEA have good potential to deal with hard optimization problems with many local optimal solutions. So far, not much work has been done on evolutionary multi-objective (EMO) algorithms with probabilistic representation. In this paper, we investigate the performance of two state-of-the-art EMO algorithms - MOEA/D and NSGA-II, with probabilistic representation based on pheromone trails, on the multi-objective travelling salesman problem. Our experimental results show that MOEA/D and NSGA-II with probabilistic presentation are very promising in sampling high-quality offspring solutions and in diversifying the search along the Pareto fronts.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published in: 2010 IEEE Congress on Evolutionary Computation (CEC 2010). ISBN 9781424469093, doi:10.1109/CEC.2010.5585998
Keywords: multiobjective optimization, quantum computing, adaptive algorithms, encoding schemes, travelling salesman problem
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Depositing User: Landa-Silva, Dario
Date Deposited: 01 Aug 2016 08:56
Last Modified: 25 Sep 2016 13:38
URI: http://eprints.nottingham.ac.uk/id/eprint/35592

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