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Solving multi-objective multicast routing problems by evolutionary multi-objective simulated annealing algorithms with variable neighbourhoods

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  • Y Xu

    (The University of Nottingham
    Hunan University)

  • R Qu

    (The University of Nottingham)

Abstract

This paper presents the investigation of an evolutionary multi-objective simulated annealing (EMOSA) algorithm with variable neighbourhoods to solve the multi-objective multicast routing problems in telecommunications. The hybrid algorithm aims to carry out a more flexible and adaptive exploration in the complex search space by using features of the variable neighbourhood search to find more non-dominated solutions in the Pareto front. Different neighbourhood strictures have been designed with regard to the set of objectives, aiming to drive the search towards optimising all objectives simultaneously. A large number of simulations have been carried out on benchmark instances and random networks with real world features including cost, delay and link utilisations. Experimental results demonstrate that the proposed EMOSA algorithm with variable neighbourhoods is able to find high-quality non-dominated solutions for the problems tested. In particular, the neighbourhood structures that are specifically designed for each objective significantly improved the performance of the proposed algorithm compared with variants of the algorithm with a single neighbourhood.

Suggested Citation

  • Y Xu & R Qu, 2011. "Solving multi-objective multicast routing problems by evolutionary multi-objective simulated annealing algorithms with variable neighbourhoods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 313-325, February.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:2:d:10.1057_jors.2010.138
    DOI: 10.1057/jors.2010.138
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    References listed on IDEAS

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    1. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    2. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    3. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
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    1. Ahern, Zeke & Paz, Alexander & Corry, Paul, 2022. "Approximate multi-objective optimization for integrated bus route design and service frequency setting," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 1-25.
    2. Ying Xu & Rong Qu & Renfa Li, 2013. "A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems," Annals of Operations Research, Springer, vol. 206(1), pages 527-555, July.

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