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

Li, Hui; Landa-Silva, Dario; Gandibleux, Xavier

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.

Citation

Li, H., Landa-Silva, D., & Gandibleux, X. (2010). Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails.

• Conference Name: Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010)
• End Date: Jul 23, 2010
• Acceptance Date: May 18, 2010
• Publication Date: Jul 18, 2010
• Deposit Date: Aug 1, 2016
• Publicly Available Date: Aug 1, 2016
• Peer Reviewed: Peer Reviewed
• Keywords: multiobjective optimization, quantum computing, adaptive algorithms, encoding schemes, travelling salesman problem
• Public URL: https://nottingham-repository.worktribe.com/output/706465
• Publisher URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5585998
• 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
• Contract Date: Aug 1, 2016