A HYBRIDIZED MULTI-OBJECTIVE MEMETIC ALGORITHM FOR THE MULTI-OBJECTIVE STOCHASTIC QUADRATIC KNAPSACK PROBLEM
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| Název: | A HYBRIDIZED MULTI-OBJECTIVE MEMETIC ALGORITHM FOR THE MULTI-OBJECTIVE STOCHASTIC QUADRATIC KNAPSACK PROBLEM |
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| Autoři: | Amina Guerrouma, Méziane Aïder |
| Zdroj: | Pesquisa Operacional v.42 2022 Pesquisa operacional Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO Pesquisa Operacional, Volume: 42, Article number: e257386, Published: 18 JUL 2022 |
| Informace o vydavateli: | FapUNIFESP (SciELO), 2022. |
| Rok vydání: | 2022 |
| Témata: | non-dominated sort algorithm, crowding-distance, gradient algorithm, memetic algorithm with selection neighborhood pareto local search, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology |
| Popis: | The knapsack problem is basic in combinatorial optimization and possesses several variants and expansions. In this paper, we focus on the multi-objective stochastic quadratic knapsack problem with random weights. We propose a Multi-Objective Memetic Algorithm With Selection Neighborhood Pareto Local Search (MASNPL). At each iteration of this algorithm, crossover, mutation, and local search are applied to a population of solutions to generate new solutions that would constitute an offspring population. Then, we use a selection operator for the best solutions to the combined parent and offspring populations. The principle of the selection operation relies on the termination of the non-domination rank and the crowding distance obtained respectively by the Non-dominated Sort Algorithm and the Crowding-Distance Computation Algorithm. To evaluate the performance of our algorithm, we compare it with both an exact algorithm and the NSGA-II algorithm. Our experimental results show that the MASNPL algorithm leads to significant efficiency. |
| Druh dokumentu: | Article |
| Popis souboru: | text/html |
| ISSN: | 1678-5142 0101-7438 |
| DOI: | 10.1590/0101-7438.2022.042.00257386 |
| Přístupová URL adresa: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382022000100214 http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382022000100214&lng=en&tlng=en |
| Rights: | CC BY |
| Přístupové číslo: | edsair.doi.dedup.....d2e48e74c7d5e2f6a53bb4a54ed72f0e |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | The knapsack problem is basic in combinatorial optimization and possesses several variants and expansions. In this paper, we focus on the multi-objective stochastic quadratic knapsack problem with random weights. We propose a Multi-Objective Memetic Algorithm With Selection Neighborhood Pareto Local Search (MASNPL). At each iteration of this algorithm, crossover, mutation, and local search are applied to a population of solutions to generate new solutions that would constitute an offspring population. Then, we use a selection operator for the best solutions to the combined parent and offspring populations. The principle of the selection operation relies on the termination of the non-domination rank and the crowding distance obtained respectively by the Non-dominated Sort Algorithm and the Crowding-Distance Computation Algorithm. To evaluate the performance of our algorithm, we compare it with both an exact algorithm and the NSGA-II algorithm. Our experimental results show that the MASNPL algorithm leads to significant efficiency. |
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| ISSN: | 16785142 01017438 |
| DOI: | 10.1590/0101-7438.2022.042.00257386 |