A Dynamic Multiobjective Evolutionary Algorithm for Multicast Routing Problem

In this work, we propose an evolutionary algorithm to tackle a multiobjective optimization problem, namely a constrained multicast routing with quality demands. The proposed algorithm embeds two different strategies along with SPEA2 (Strength Pareto Evolutionary Algorithm 2) method attempting to improve convergence to Pareto front. These strategies are a heuristic for population diversity augmentation and a neighborhood mating selection scheme. Experimental results showed that selecting which strategy to use depends on population dynamics aspects described by non dominated solutions over evolutionary iterations. It was possible to observe that the proposed mechanism can help the algorithm to achieve better solutions over convergence and diversity goals in most cases.

[1]  S. E. Dreyfus,et al.  The steiner problem in graphs , 1971, Networks.

[2]  Tomoyuki Hiroyasu,et al.  Effectiveness of neighborhood crossover in multiobjective genetic algorithm , 2006, Computational Intelligence.

[3]  G.M.B. Oliveira,et al.  Determining multicast routes with QoS and traffic engineering requirements based on genetic algorithm , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[4]  Panos M. Pardalos,et al.  A survey of combinatorial optimization problems in multicast routing , 2005, Comput. Oper. Res..

[5]  Marcos L. P. Bueno,et al.  Analyzing the effects of neighborhood crossover in multiobjective multicast flow routing problem , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[6]  C. P. Ravikumar,et al.  Source-based delay-bounded multicasting in multimedia networks , 1998, Comput. Commun..

[7]  John E. Beasley,et al.  OR-Library: Distributing Test Problems by Electronic Mail , 1990 .

[8]  Wang Zhengying,et al.  Bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic algorithm , 2001 .

[9]  Gina Maira Barbosa de Oliveira,et al.  Multicast flow routing: Evaluation of heuristics and multiobjective evolutionary algorithms , 2010, IEEE Congress on Evolutionary Computation.

[10]  Gina Maira Barbosa de Oliveira,et al.  Algorithms to Augment Diversity and Convergence in Multiobjective Multicast Flow Routing , 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.

[11]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[12]  Gina Maira Barbosa de Oliveira,et al.  Multiobjective Evolutionary Algorithms and a Combined Heuristic for Route Reconnection Applied to Multicast Flow Routing , 2010, 2010 10th IEEE International Conference on Computer and Information Technology.

[13]  Jorge Crichigno,et al.  Multitree-Multiobjective Multicast Routing for Traffic Engineering , 2006, IFIP AI.

[14]  Tomoyuki Hiroyasu,et al.  NCGA: Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems , 2002, GECCO Late Breaking Papers.

[15]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[16]  Tomoyuki Hiroyasu,et al.  SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2 , 2004, PPSN.

[17]  Hisao Ishibuchi,et al.  Recombination of Similar Parents in EMO Algorithms , 2005, EMO.

[18]  Celso C. Ribeiro,et al.  Hybrid Local Search for the Steiner Problem in Graphs , 2001 .

[19]  Gina M. B. Oliveira,et al.  A multi-objective evolutionary algorithm with ε-dominance to calculate multicast routes with QoS requirements , 2009, 2009 IEEE Congress on Evolutionary Computation.

[20]  Eric D. Siegel.  Designing Quality of Service Solutions for the Enterprise , 1999 .

[21]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .