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Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem

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Evolutionary Computation for Dynamic Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 490))

Abstract

Ant colony optimization (ACO) algorithms have proved to be powerful methods to address dynamic optimization problems (DOPs). However, once the population converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to the new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution is to maintain the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants. In this chapter, we investigate ACO algorithms with different immigrants schemes for two types of dynamic travelling salesman problems (DTSPs) with traffic factor, i.e., under random and cyclic dynamic changes. The experimental results based on different DTSP test cases show that the investigated algorithms outperform other peer ACO algorithms and that different immigrants schemes are beneficial on different environmental cases.

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Authors and Affiliations

  1. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, U.K.

    Michalis Mavrovouniotis & Shengxiang Yang

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  1. Michalis Mavrovouniotis
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  2. Shengxiang Yang
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Correspondence to Michalis Mavrovouniotis .

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Editors and Affiliations

  1. , School of Computer Science and Informati, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Shengxiang Yang

  2. , School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom

    Xin Yao

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Mavrovouniotis, M., Yang, S. (2013). Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem. In: Yang, S., Yao, X. (eds) Evolutionary Computation for Dynamic Optimization Problems. Studies in Computational Intelligence, vol 490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38416-5_13

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  • DOI: https://doi.org/10.1007/978-3-642-38416-5_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38415-8

  • Online ISBN: 978-3-642-38416-5

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