An adaptive evolutionary multi-objective approach based on simulated annealing
A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization...
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| Format: | Article |
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MIT Press
2011
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| Online Access: | https://eprints.nottingham.ac.uk/32605/ |
| _version_ | 1848794447709470720 |
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| author | Li, Hui Landa-Silva, Dario |
| author_facet | Li, Hui Landa-Silva, Dario |
| author_sort | Li, Hui |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search towards the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well-established multi-objective metaheuristic algorithms on both the (constrained)multi-objective knapsack problemand the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated. |
| first_indexed | 2025-11-14T19:16:21Z |
| format | Article |
| id | nottingham-32605 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:16:21Z |
| publishDate | 2011 |
| publisher | MIT Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-326052020-05-04T20:23:59Z https://eprints.nottingham.ac.uk/32605/ An adaptive evolutionary multi-objective approach based on simulated annealing Li, Hui Landa-Silva, Dario A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search towards the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well-established multi-objective metaheuristic algorithms on both the (constrained)multi-objective knapsack problemand the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated. MIT Press 2011 Article PeerReviewed Li, Hui and Landa-Silva, Dario (2011) An adaptive evolutionary multi-objective approach based on simulated annealing. Evolutionary Computation, 19 (4). pp. 561-595. ISSN 1530-9304 multiobjective optimization simulated annealing local search combinatorial optimization http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00038#.Vv_I3vkrJpg doi:10.1162/EVCO_a_00038 doi:10.1162/EVCO_a_00038 |
| spellingShingle | multiobjective optimization simulated annealing local search combinatorial optimization Li, Hui Landa-Silva, Dario An adaptive evolutionary multi-objective approach based on simulated annealing |
| title | An adaptive evolutionary multi-objective approach based on simulated annealing |
| title_full | An adaptive evolutionary multi-objective approach based on simulated annealing |
| title_fullStr | An adaptive evolutionary multi-objective approach based on simulated annealing |
| title_full_unstemmed | An adaptive evolutionary multi-objective approach based on simulated annealing |
| title_short | An adaptive evolutionary multi-objective approach based on simulated annealing |
| title_sort | adaptive evolutionary multi-objective approach based on simulated annealing |
| topic | multiobjective optimization simulated annealing local search combinatorial optimization |
| url | https://eprints.nottingham.ac.uk/32605/ https://eprints.nottingham.ac.uk/32605/ https://eprints.nottingham.ac.uk/32605/ |