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Improved Mean Variance Mapping Optimization for the Travelling Salesman Problem

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Computational Intelligence in Data Mining - Volume 1

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 31))

Abstract

This paper presents an improved Mean Variance Mapping Optimization to address and solve the NP-hard combinatorial problem, the travelling salesman problem. MVMO, conceived and developed by István Erlich is a recent addition to the large set of heuristic optimization algorithms with a strategic novel feature of mapping function used for mutation on basis of the mean and variance of the population set initialized. Also, a new crossover scheme has been proposed which is a collective of two crossover techniques to produce fitter offsprings. The mutation technique adopted is only used if it converges towards more economic traversal. Also, the change in control parameters of the algorithm doesn’t affect the result thus making it a fine algorithm for combinatorial as well as continuous problems as is evident from the experimental results and the comparisons with other algorithms which has been tested against the set of benchmarks from the TSPLIB library.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Veer Surendra Sai University of Technology, Burla, 768018, India

    Subham Sahoo

  2. Department of Electrical Engineering and Information Technologies, University of Duisburg, Essen, Germany

    István Erlich

Authors
  1. Subham Sahoo
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  2. István Erlich
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Corresponding author

Correspondence to Subham Sahoo .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Sahoo, S., Erlich, I. (2015). Improved Mean Variance Mapping Optimization for the Travelling Salesman Problem. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_7

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  • DOI: https://doi.org/10.1007/978-81-322-2205-7_7

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2204-0

  • Online ISBN: 978-81-322-2205-7

  • eBook Packages: EngineeringEngineering (R0)

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