A genetic algorithm to solve the multidimensional Knapsack problem
Loading...
Date
2013
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Scientific Research membranes@mdpi.com
Open Access Color
GOLD
Green Open Access
Yes
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
BIP! Indicators
Impulse
Average
Influence
Average
Popularity
Top 10%
Abstract
In this paper The Multidimensional Knapsack Problem (MKP) which occurs in many different applications is studied and a genetic algorithm to solve the MKP is proposed. Unlike the technique of the classical genetic algorithm initial population is not randomly generated in the proposed algorithm thus the solution space is scanned more efficiently. Moreover the algorithm is written in C programming language and is tested on randomly generated instances. It is seen that the algorithm yields optimal solutions for all instances. © 2020 Elsevier B.V. All rights reserved.
Description
ORCID
Keywords
Evolutionary Algorithms, Genetic Algorithm, Heuristic Approach, Multidimensional Knapsack Problem, C (programming Language), Combinatorial Optimization, Evolutionary Algorithms, Heuristic Algorithms, Heuristic Methods, Heuristic Approach, Initial Population, Multidimensional Knapsack Problems, Optimal Solutions, Solution Space, Genetic Algorithms, C (programming language), Combinatorial optimization, Evolutionary algorithms, Heuristic algorithms, Heuristic methods, Heuristic approach, Initial population, Multidimensional knapsack problems, Optimal solutions, Solution space, Genetic algorithms, Genetic Algorithm, Matematik, Bilgisayar Bilimleri, Teori Ve Metotlar, Evolutionary Algorithms, Heuristic Approach, Multidimensional Knapsack Problem, Matematik, Genetic Algorithm, Heuristic approach, Evolutionary algorithms, Genetic algorithm, Uygulamalı, Multidimensional Knapsack Problem, Evolutionary Algorithms, Heuristic Approach, Multidimensional Knapsack problem
Fields of Science
05 social sciences, 0211 other engineering and technologies, 02 engineering and technology, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering
Citation
WoS Q
Scopus Q
OpenCitations Citation Count
4
Source
Mathematical and Computational Applications
Volume
18
Issue
3
Start Page
486
End Page
494
PlumX Metrics
Citations
CrossRef : 3
Scopus : 10
Captures
Mendeley Readers : 9
Google Scholar™
