Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails
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...
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| Format: | Conference or Workshop Item |
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IEEE press
2010
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| Online Access: | https://eprints.nottingham.ac.uk/35592/ |
| _version_ | 1848795116095930368 |
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| author | Li, Hui Landa-Silva, Dario Gandibleux, Xavier |
| author_facet | Li, Hui Landa-Silva, Dario Gandibleux, Xavier |
| author_sort | Li, Hui |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:26:58Z |
| format | Conference or Workshop Item |
| id | nottingham-35592 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:26:58Z |
| publishDate | 2010 |
| publisher | IEEE press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-355922020-05-04T16:29:27Z https://eprints.nottingham.ac.uk/35592/ Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails Li, Hui Landa-Silva, Dario Gandibleux, Xavier 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. IEEE press 2010-07-18 Conference or Workshop Item PeerReviewed Li, Hui, 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. multiobjective optimization quantum computing adaptive algorithms encoding schemes travelling salesman problem http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5585998 |
| spellingShingle | multiobjective optimization quantum computing adaptive algorithms encoding schemes travelling salesman problem Li, Hui Landa-Silva, Dario Gandibleux, Xavier Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails |
| title | Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails |
| title_full | Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails |
| title_fullStr | Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails |
| title_full_unstemmed | Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails |
| title_short | Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails |
| title_sort | evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails |
| topic | multiobjective optimization quantum computing adaptive algorithms encoding schemes travelling salesman problem |
| url | https://eprints.nottingham.ac.uk/35592/ https://eprints.nottingham.ac.uk/35592/ |