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A Percentile Transition Ranking Algorithm Applied to Knapsack Problem

  • José GarcíaEmail author
  • Broderick Crawford
  • Ricardo Soto
  • Gino Astorga
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 662)

Abstract

The binarization of Swarm Intelligence continuous metaheuristics is an area of great interest in operational research. This interest is mainly due to the application of binarized metaheuristics to combinatorial problems. In this article we propose a general binarization algorithm called Percentile Transition Ranking Algorithm (PTRA). PTRA uses the percentile concept as a binarization mechanism. In particular we will apply this mechanism to the Cuckoo Search metaheuristic to solve the set multidimensional Knapsack problem (MKP). We provide necessary experiments to investigate the role of key ingredients of the algorithm. Finally to demonstrate the efficiency of our proposal, we solve Knapsack benchmark instances of the literature. These instances show PTRA competes with the state-of-the-art algorithms.

Keywords

Combinatorial optimization Multidimensional knapsack problem Metaheuristics 

Notes

Acknowledgments

Broderick Crawford is supported by grant CONICYT/FONDECYT/REGULAR 1171243, Ricardo Soto is supported by Grant CONICYT /FONDECYT /REGULAR /1160455, and José García is supported by INF-PUCV 2016.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • José García
    • 1
    • 2
    Email author
  • Broderick Crawford
    • 2
  • Ricardo Soto
    • 2
  • Gino Astorga
    • 2
    • 3
  1. 1.Centro de Investigación y Desarrollo TelefónicaSantiagoChile
  2. 2.Pontificia Universidad Católica de ValparaísoValparaísoChile
  3. 3.Universidad de ValparaísoValparaísoChile

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