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Neural Computing and Applications

, Volume 31, Supplement 1, pp 63–76 | Cite as

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

  • Anandkumar PrakasamEmail author
  • Nickolas Savarimuthu
S.I. : Machine Learning Applications for Self-Organized Wireless Networks

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.

Keywords

Ant colony optimization Dynamic travelling salesman problem Hyper-population Metaheuristic algorithms Wireless sensor networks Local restart strategy 

Notes

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest to declare.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Computer ApplicationsNational Institute of TechnologyTrichyIndia

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