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A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

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  • Ying Xu
  • Rong Qu
  • Renfa Li

Abstract

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:206:y:2013:i:1:p:527-555:10.1007/s10479-013-1322-7
    DOI: 10.1007/s10479-013-1322-7
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    References listed on IDEAS

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    1. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    2. Jaszkiewicz, Andrzej, 2002. "Genetic local search for multi-objective combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 137(1), pages 50-71, February.
    3. 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.
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    1. Hailin Wu & Fengming Tao & Qingqing Qiao & Mengjun Zhang, 2020. "A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions," IJERPH, MDPI, vol. 17(2), pages 1-21, January.
    2. Lixia Li & Yu Yang & Gaoyuan Qin, 2019. "Optimization of Integrated Inventory Routing Problem for Cold Chain Logistics Considering Carbon Footprint and Carbon Regulations," Sustainability, MDPI, vol. 11(17), pages 1-22, August.

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