View in EDS

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

Saved in:
Bibliographic Details
Title: A HYBRIDIZED MULTI-OBJECTIVE MEMETIC ALGORITHM FOR THE MULTI-OBJECTIVE STOCHASTIC QUADRATIC KNAPSACK PROBLEM
Authors: Amina Guerrouma, Méziane Aïder
Source: 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
Publisher Information: FapUNIFESP (SciELO), 2022.
Publication Year: 2022
Subject Terms: 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
Description: 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.
Document Type: Article
File Description: text/html
ISSN: 1678-5142
0101-7438
DOI: 10.1590/0101-7438.2022.042.00257386
Access URL: 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
Accession Number: edsair.doi.dedup.....d2e48e74c7d5e2f6a53bb4a54ed72f0e
Database: OpenAIRE
Description
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.
ISSN:16785142
01017438
DOI:10.1590/0101-7438.2022.042.00257386