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Self-organisation migration technique for enhancing the permutation coded genetic algorithm

Author

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  • K. Dinesh
  • R. Rajakumar
  • R. Subramanian

Abstract

Genetic algorithm (GA) is well-known optimisation algorithm for solving various kinds of the optimisation problems. GA is based on the evolutionary principles and effectively solves the large-scale problem. In addition, it incorporates the variety of hybrid techniques to achieve the best performance in complex problems. However, self-organisation is one of the popular model, which acquire global order from the local interaction among the individuals. The combined version of self-organisation and genetic algorithm are adopted to improve the performance in attaining the convergence. This paper proposes a bi-directional self-organisation migration technique for improving the genetic algorithm which achieves the convergence and well-balanced diversity in the population. The experimentation is conducted on the standard test-bed of travelling salesman problem and instances are obtained from TSPLIB. Thus, the proposed algorithm has shown its dominance with the existing classical GA in terms of various parameter metrics.

Suggested Citation

  • K. Dinesh & R. Rajakumar & R. Subramanian, 2021. "Self-organisation migration technique for enhancing the permutation coded genetic algorithm," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 13(1), pages 15-36.
    • Handle: RePEc:ids:injams:v:13:y:2021:i:1:p:15-36
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    Cited by:

    1. Dinesh Karunanidy & Subramanian Ramalingam & Ankur Dumka & Rajesh Singh & Mamoon Rashid & Anita Gehlot & Sultan S. Alshamrani & Ahmed Saeed AlGhamdi, 2022. "JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem," Mathematics, MDPI, vol. 10(5), pages 1-28, February.

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