IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Solution of “Hard” Knapsack Instances Using Quantum Inspired Evolutionary Algorithm

Solution of “Hard” Knapsack Instances Using Quantum Inspired Evolutionary Algorithm
View Sample PDF
Author(s): C. Patvardhan (Dayalbagh Educational Institute, India), Sulabh Bansal (Dayalbagh Educational Institute, India)and Anand Srivastav (Institut für Informatik, Universität zu Kiel, Germany)
Copyright: 2015
Pages: 17
Source title: Research Methods: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-7456-1.ch048

Purchase

View Solution of “Hard” Knapsack Instances Using Quantum Inspired Evolutionary Algorithm on the publisher's website for pricing and purchasing information.

Abstract

Knapsack Problem (KP) is a popular combinatorial optimization problem having application in many technical and economic areas. Several attempts have been made in past to solve the problem. Various exact and non-exact approaches exist to solve KP. Exact algorithms for KP are based on either branch and bound or dynamic programming technique. Heuristics exist which solve KP non-exactly in lesser time. Heuristic approaches do not provide any guarantee regarding the quality of solution whereas exact approaches have high worst case complexities. Quantum-inspired Evolutionary Algorithm (QEA) is a subclass of Evolutionary Algorithm, a naturally inspired population based search technique. QEA uses concepts of quantum computing. An engineered Quantum-inspired Evolutionary Algorithm (QEA-E), an improved version of QEA, is presented which quickly solves extremely large spanner problem instances (e.g. 290,000 items) that are very difficult for the state of the art exact algorithm as well as the original QEA.

Related Content

Tutita M. Casa, Fabiana Cardetti, Madelyn W. Colonnese. © 2024. 14 pages.
R. Alex Smith, Madeline Day Price, Tessa L. Arsenault, Sarah R. Powell, Erin Smith, Michael Hebert. © 2024. 19 pages.
Marta T. Magiera, Mohammad Al-younes. © 2024. 27 pages.
Christopher Dennis Nazelli, S. Asli Özgün-Koca, Deborah Zopf. © 2024. 31 pages.
Ethan P. Smith. © 2024. 22 pages.
James P. Bywater, Sarah Lilly, Jennifer L. Chiu. © 2024. 20 pages.
Ian Jones, Jodie Hunter. © 2024. 20 pages.
Body Bottom