Treffer: An efficient optimizer for the 0/1 knapsack problem using group counseling

Title:
An efficient optimizer for the 0/1 knapsack problem using group counseling
Authors:
Yazeed Yasin Ghadi, Tamara Al Shloul, Zahid Iqbal Nezami, Hamid Ali, Muhammad Asif, Hanan Aljuaid, Shahbaz Ahmad
Source:
PeerJ Comput Sci
PeerJ Computer Science, Vol 9, p e1315 (2023)
Publisher Information:
PeerJ, 2023.
Publication Year:
2023
Subject Terms:
Optimization, Combinatorial optimization, Adaptive and Self-Organizing Systems, Evolutionary biology, Dynamic programming, Knapsack, Multi-Objective Transportation Problem Optimization, Evolutionary algorithmx, Engineering, Change-making problem, Artificial Intelligence, Generalized assignment problem, Evolutionary algorithm, FOS: Mathematics, Cutting stock problem, Swarm Intelligence Optimization Algorithms, Optimization problem, Constraint Handling, Biology, Polynomial-time approximation scheme, Global Optimization, Multi-Objective Optimization, Optimization Applications, Mathematical optimization, QA75.5-76.95, Computer science, GCO, Knapsack problem, Combinatorial, Control and Systems Engineering, Particle Swarm Optimization, Function (biology), Electronic computers. Computer science, Application of Genetic Programming in Machine Learning, Computer Science, Physical Sciences, Continuous knapsack problem, Mathematics
Document Type:
Fachzeitschrift Article<br />Other literature type
Language:
English
ISSN:
2376-5992
DOI:
10.7717/peerj-cs.1315
DOI:
10.60692/mp3be-r0c82
DOI:
10.60692/vw2g9-bna31
Access URL:
https://pubmed.ncbi.nlm.nih.gov/37346609
https://doaj.org/article/cf675713e0cc4505962511edbf55239f
Rights:
CC BY
URL: http://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
Accession Number:
edsair.doi.dedup.....247af25e1e86e36e4bd135d2182d93a8
Database:
OpenAIRE

Weitere Informationen

The field of optimization is concerned with determining the optimal solution to a problem. It refers to the mathematical loss or gain of a given objective function. Optimization must reduce the given problem’s losses and disadvantages while maximizing its earnings and benefits. We all want optimal or, at the very least, suboptimal answers because we all want to live a better life. Group counseling optimizer (GCO) is an emerging evolutionary algorithm that simulates the human behavior of counseling within a group for solving problems. GCO has been successfully applied to single and multi-objective optimization problems. The 0/1 knapsack problem is also a combinatorial problem in which we can select an item entirely or drop it to fill a knapsack so that the total weight of selected items is less than or equal to the knapsack size and the value of all items is as significant as possible. Dynamic programming solves the 0/1 knapsack problem optimally, but the time complexity of dynamic programming is O(n3). In this article, we provide a feature analysis of GCO parameters and use it to solve the 0/1 knapsack problem (KP) using GCO. The results show that the GCO-based approach efficiently solves the 0/1 knapsack problem; therefore, it is a viable alternative to solving the 0/1 knapsack problem.