A Novel Evolutionary Algorithm for Multidimensional Knapsack Problem

2015 ◽  
Vol 6 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Sara Sabba ◽  
Salim Chikhi
Keyword(s):  
Knapsack Problem ◽  
Optimization Problems ◽  
Population Based ◽  
Multidimensional Knapsack Problem ◽  
Great Tendency ◽  
Binary Optimization ◽  
Local Optima ◽  
Multidimensional Knapsack ◽  
Hard Problems ◽  
Complex Optimization

Binary optimization problems are in the most case the NP-hard problems that call to satisfy an objective function with or without constraints. Various optimization problems can be formulated in binary expression whither they can be resolved in easier way. Optimization literature supplies a large number of approaches to find solutions to binary hard problems. However, most population-based algorithms have a great tendency to be trapped in local optima particularly when solving complex optimization problems. In this paper, the authors introduce a new efficient population-based technique for binary optimization problems (that we called EABOP). The proposed algorithm can provide an effective search through a new proposed binary mutation operator. The performance of our approach was tested on hard instances of the multidimensional knapsack problem. The obtained results show that the new algorithm is able of quickly obtaining high-quality solutions for most hard instances of the problem.

scholarly journals A Modified Binary Pigeon-Inspired Algorithm for Solving the Multi-dimensional Knapsack Problem

2020 ◽  
Vol 30 (1) ◽  
pp. 90-103
Author(s):  
Asaju La’aro Bolaji ◽  
Friday Zinzendoff Okwonu ◽  
Peter Bamidele Shola ◽  
Babatunde Sulaiman Balogun ◽  
Obinna Damian Adubisi
Keyword(s):  
Knapsack Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Solution Space ◽  
Population Based ◽  
Solution Technique ◽  
Multidimensional Knapsack Problem ◽  
Practical Applications ◽  
Multidimensional Knapsack ◽  
Swarm Intelligence Based Algorithm

AbstractThe pigeon-inspired optimization algorithm is a category of a newly proposed swarm intelligence-based algorithm that belongs to the population-based solution technique. The MKP is a class of complex optimization problems that have many practical applications in the fields of engineering and sciences. Due to the practical applications of MKP, numerous algorithmic-based methods like local search and population-based search algorithms have been proposed to solve the MKP in the past few decades. This paper proposes a modified binary pigeon-inspired optimization algorithm named (Modified-BPIO) for the 0 - 1 multidimensional knapsack problem (MKP). The utilization of the binary pigeon-inspired optimization (BPIO) for solving the multidimensional knapsack problem came with huge success. However, it can be observed that the BPIO converges prematurely due to lost diversity during the search activities. Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space. The MKP benchmarks from the Operations Research (OR) library are utilized to test the performance of the proposed binary method. Experimentally, it is concluded that the proposed Modified-BPIO has a better performance when compared with the BPIO and existing state-of-the-arts that worked on the same MKP benchmarks.

Solution to the 0-1 Multidimensional Knapsack Problem Based on DNA Computation

2011 ◽  
Vol 58-60 ◽  
pp. 1767-1772
Author(s):  
Kee Rong Wu ◽  
Chung Wei Yeh
Keyword(s):  
Knapsack Problem ◽  
Optimization Problem ◽  
Deoxyribonucleic Acid ◽  
Np Hard ◽  
Multidimensional Knapsack Problem ◽  
Dna Computation ◽  
Multidimensional Knapsack ◽  
Operational Problem ◽  
Hard Problems ◽  
Computation Scheme

We proposed a two-layer scheme of Deoxyribonucleic acid (DNA) based computation, DNA-01MKP, to solve the typical NP-hard combinatorial optimization problem, 0-1 multidimensional knapsack problem (0-1 MKP). DNA-01MKP consists of two layers of procedures: (1) translation of the problem equations to strands and (2) solution of problems. For layer 1, we designed flexible well-formatted strands to represent the problem equations; for layer 2, we constructed the DNA algorithms to solve the 0-1 MKP. Our results revealed that this molecular computation scheme is able to solve the complicated operational problem with a reasonable time complexity of O(n×k), though it needs further experimental verification in the future. By adjusting the DNA-based procedures, the scheme may be used to resolve different NP-hard problems.

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