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Novel local restart strategies with hyper-populated ant colonies for dynamic optimization problems

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
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Abstract

The emergence of novel metaheuristic algorithms and the impracticality of exact algorithms led to the increased application of stochastic optimization techniques to solve combinatorial optimization problems. Determination of population size, stopping criteria, selection of optimal parameter values, getting out of the local optima and most importantly the interplay between various parameters are yet to be addressed. In this work, the significance of population size and how it interplays with other parameters in determining the effective convergence of the system in both static and dynamic scenarios for travelling salesman problem (TSP) are explained. This work utilizes a more complex variant of introducing dynamism in TSP, by swapping existing nodes with new nodes. This work proposes novel local restart strategies for efficient search space reset during node replacements in dynamic TSP. The proposed local restart strategy in combination with hyper-populated ant systems is found to outperform existing state-of-the-art solutions on benchmark datasets including Oliver30, Eilon51 and KROA100 from TSPLIB.

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Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Trichy, Tamilnadu, India

    Anandkumar Prakasam & Nickolas Savarimuthu

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  1. Anandkumar Prakasam
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  2. Nickolas Savarimuthu
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Correspondence to Anandkumar Prakasam.

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Prakasam, A., Savarimuthu, N. Novel local restart strategies with hyper-populated ant colonies for dynamic optimization problems. Neural Comput & Applic 31 (Suppl 1), 63–76 (2019). https://doi.org/10.1007/s00521-018-3638-3

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  • DOI: https://doi.org/10.1007/s00521-018-3638-3

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