An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem
This paper proposes an enhanced Multi-objective Go with the Winners (MOGWW) algorithm to solve multi-objective combinatorial optimization problems. The original MOGWW algorithm is equipped with the well known Pareto Local Search (PLS) procedure. In order to assess the performance of the hybridizatio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2011-08-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/2175.pdf |
| _version_ | 1811252309565374464 |
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| author | Everardo Gutierrez Carlos Brizuela |
| author_facet | Everardo Gutierrez Carlos Brizuela |
| author_sort | Everardo Gutierrez |
| collection | DOAJ |
| description | This paper proposes an enhanced Multi-objective Go with the Winners (MOGWW) algorithm to solve multi-objective combinatorial optimization problems. The original MOGWW algorithm is equipped with the well known Pareto Local Search (PLS) procedure. In order to assess the performance of the hybridization, the non-dominated solutions it generates are compared with the ones generated by each of its components. The algorithms are applied to benchmark instances of the bi-objective Quadratic Assignment Problem. Experimental results show that the hybridized version outperforms both its components, i.e. the original MOGWW algorithm and a PLS variant. |
| first_indexed | 2024-04-12T16:32:48Z |
| format | Article |
| id | doaj.art-4ee0dd71affc4fe490045623eaad9588 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-12T16:32:48Z |
| publishDate | 2011-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-4ee0dd71affc4fe490045623eaad95882022-12-22T03:25:05ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832011-08-014410.2991/ijcis.2011.4.4.12An Enhanced MOGWW for the bi-objective Quadratic Assignment ProblemEverardo GutierrezCarlos BrizuelaThis paper proposes an enhanced Multi-objective Go with the Winners (MOGWW) algorithm to solve multi-objective combinatorial optimization problems. The original MOGWW algorithm is equipped with the well known Pareto Local Search (PLS) procedure. In order to assess the performance of the hybridization, the non-dominated solutions it generates are compared with the ones generated by each of its components. The algorithms are applied to benchmark instances of the bi-objective Quadratic Assignment Problem. Experimental results show that the hybridized version outperforms both its components, i.e. the original MOGWW algorithm and a PLS variant.https://www.atlantis-press.com/article/2175.pdfMulti Objective Go With the WinnersBi-objective QAPPareto Local SearchGreedy Nondominated Local Search. |
| spellingShingle | Everardo Gutierrez Carlos Brizuela An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem International Journal of Computational Intelligence Systems Multi Objective Go With the Winners Bi-objective QAP Pareto Local Search Greedy Nondominated Local Search. |
| title | An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem |
| title_full | An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem |
| title_fullStr | An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem |
| title_full_unstemmed | An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem |
| title_short | An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem |
| title_sort | enhanced mogww for the bi objective quadratic assignment problem |
| topic | Multi Objective Go With the Winners Bi-objective QAP Pareto Local Search Greedy Nondominated Local Search. |
| url | https://www.atlantis-press.com/article/2175.pdf |
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