A HYBRIDIZED MULTI-OBJECTIVE MEMETIC ALGORITHM FOR THE MULTI-OBJECTIVE STOCHASTIC QUADRATIC KNAPSACK PROBLEM

• Autor(a) principal: Guerrouma,Amina
• Data de Publicação: 2022
• Outros Autores: Aïder,Méziane
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Pesquisa operacional (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382022000100214
• topic: non-dominated sort algorithm; crowding-distance; gradient algorithm; memetic algorithm with selection neighborhood pareto local search

ABSTRACT 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.