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

Author

Listed:
  • C. Patvardhan

    (Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, India)

  • Sulabh Bansal

    (Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, India)

  • Anand Srivastav

    (Institut für Informatik, Universität zu Kiel, Kiel, Germany)

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.

Suggested Citation

  • C. Patvardhan & Sulabh Bansal & Anand Srivastav, 2014. "Solution of “Hard” Knapsack Instances Using Quantum Inspired Evolutionary Algorithm," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 5(1), pages 52-68, January.
    • Handle: RePEc:igg:jaec00:v:5:y:2014:i:1:p:52-68
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