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

Patvardhan, C., Bansal, Sulabh and Srivastav, Anand (2014) Solution of “Hard” Knapsack Instances Using Quantum Inspired Evolutionary Algorithm. International Journal of Applied Evolutionary Computation, 5 (1). pp. 52-68. DOI 10.4018/ijaec.2014010104.

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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.

Document Type: Article
Research affiliation: Kiel University
Kiel University > Kiel Marine Science
OceanRep > The Future Ocean - Cluster of Excellence
Refereed: Yes
Open Access Journal?: No
Publisher: IGI Global
Projects: Future Ocean
Date Deposited: 29 Mar 2018 10:06
Last Modified: 23 Sep 2019 21:14
URI: https://oceanrep.geomar.de/id/eprint/42514

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