Hybrid Genetic Algorithms to Solve the Multidimensional Knapsack Problem

2018 ◽  
pp. 235-250 ◽  
Author(s):  
Abdellah Rezoug ◽  
Mohamed Bader-El-Den ◽  
Dalila Boughaci
Keyword(s):  
Genetic Algorithms ◽  
Knapsack Problem ◽  
Multidimensional Knapsack Problem ◽  
Hybrid Genetic Algorithms ◽  
Multidimensional Knapsack

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

2014 ◽  
Vol 146 ◽  
pp. 17-29 ◽  
Author(s):  
Jean P. Martins ◽  
Carlos M. Fonseca ◽  
Alexandre C.B. Delbem
Keyword(s):  
Genetic Algorithms ◽  
Knapsack Problem ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack

scholarly journals Neural, Genetic, And Neurogenetic Approaches For Solving The 0-1 Multidimensional Knapsack Problem

2012 ◽  
Vol 17 (1) ◽  
pp. 43 ◽  
Author(s):  
Jason Deane ◽  
Anurag Agarwal
Keyword(s):  
Genetic Algorithms ◽  
Knapsack Problem ◽  
Search Algorithm ◽  
Benchmark Problems ◽  
Initial Population ◽  
Local Search Algorithm ◽  
Genetic Approach ◽  
Multidimensional Knapsack Problem ◽  
Problem Space ◽  
Multidimensional Knapsack

The multi-dimensional knapsack problem (MDKP) is a well-studied problem in Decision Sciences. The problems NP-Hard nature prevents the successful application of exact procedures such as branch and bound, implicit enumeration and dynamic programming for larger problems. As a result, various approximate solution approaches, such as the relaxation approaches, heuristic and metaheuristic approaches have been developed and applied effectively to this problem. In this study, we propose a Neural approach, a Genetic Algorithms approach and a Neurogenetic approach, which is a hybrid of the Neural and the Genetic Algorithms approach. The Neural approach is essentially a problem-space based non-deterministic local-search algorithm. In the Genetic Algorithms approach we propose a new way of generating initial population. In the Neurogenetic approach, we show that the Neural and Genetic iterations, when interleaved appropriately, can complement each other and provide better solutions than either the Neural or the Genetic approach alone. Within the overall search, the Genetic approach provides diversification while the Neural provides intensification. We demonstrate the effectiveness of our proposed approaches through an empirical study performed on several sets of benchmark problems commonly used in the literature.

A Review and Comparison of Genetic Algorithms for the 0-1 Multidimensional Knapsack Problem

2015 ◽  
Vol 6 (2) ◽  
pp. 21-31 ◽  
Author(s):  
Bernhard Lienland ◽  
Li Zeng
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Real Life ◽  
Combinatorial Optimization Problem ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
Problem Types

The 0-1 multidimensional knapsack problem (MKP) is a well-known combinatorial optimization problem with several real-life applications, for example, in project selection. Genetic algorithms (GA) are effective heuristics for solving the 0-1 MKP. Multiple individual GAs with specific characteristics have been proposed in literature. However, so far, these approaches have only been partially compared in multiple studies with unequal conditions. Therefore, to identify the “best” genetic algorithm, this article reviews and compares 11 existing GAs. The authors' tests provide detailed information on the GAs themselves as well as their performance. The authors validated fitness values and required computation times in varying problem types and environments. Results demonstrate the superiority of one GA.

scholarly journals Optimization Method to Address Psychosocial Risks through Adaptation of the Multidimensional Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1126
Author(s):  
Marta Lilia Eraña-Díaz ◽  
Marco Antonio Cruz-Chávez ◽  
Fredy Juárez-Pérez ◽  
Juana Enriquez-Urbano ◽  
Rafael Rivera-López ◽  
...  
Keyword(s):  
Risk Factors ◽  
Simulated Annealing ◽  
Knapsack Problem ◽  
Simulated Annealing Algorithm ◽  
Psychosocial Risk Factors ◽  
Psychosocial Risk ◽  
Multidimensional Knapsack Problem ◽  
Optimal Subset ◽  
Multidimensional Knapsack ◽  
A Company

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.

scholarly journals Multidimensional knapsack problem optimization using a binary particle swarm model with genetic operations

2017 ◽  
Vol 22 (8) ◽  
pp. 2567-2582 ◽  
Author(s):  
Luis Fernando Mingo López ◽  
Nuria Gómez Blas ◽  
Alberto Arteta Albert
Keyword(s):  
Knapsack Problem ◽  
Particle Swarm ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack
Sign in / Sign up

Export Citation Format

Share Document