Uma Nova Estratégia para Seleção de Indivíduos em Algoritmos Multiobjetivos com Busca Local Aplicados ao Problema de Projeto de Redes
Resumo
O aumento das taxas de transmissão necessárias para novos serviços relacionados `a Internet impulsiona novos estudos sobre otimização de rede. Algoritmos de otimização com busca local tˆem se mostrado promissores na tentativa de projetar redes que satisfaçam diferentes objetivos como: custo, probabilidade de bloqueio, gasto de energia e indicadores de robustez. Este trabalho propõe uma nova estratégia de seleção do indivíduos para a busca local, objetivando melhorar a qualidade do Pareto em termos de hipervolume, e ainda obter ganhos em eficiência. A estratégia proposta foi comparada com um método base, em um cenário simulado de uma rede de alta capacidade para o estado de Pernambuco. De acordo com os resultados, a estratégia proposta foi capaz de tornar o algoritmo mais eficaz, melhorando o desempenho em todos os
objetivos, e mais eficiente; superando o m´etodo base em ambos os aspectos estudados.
Palavras-chave:
Evolutionary Computation, Multiobjective Optimization, Network Optimization
Referências
Araújo, D. R. B. (2015). Planejamento de redes ópticas usando inteligência computacional e ciência das redes. PhD thesis, Universidade Federal de Pernambuco.
Brun, M., Sima, C., Hua, J., Lowey, J., Carroll, B., Suh, E., and Dougherty, E. R. (2007). Modelbased evaluation of clustering validation measures. Pattern recognition, 40(3):807–824.
Das, I. and Dennis, J. E. (1998). Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM journal on optimization, 8(3):631–657.
Deb, K. and Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4):577–601.
Dupleix, V., Araújo, D. R., and Bastos-Filho, C. J. (2016). Designing the optical network of haiti using a multi-objective evolutionary approach. In 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pages 1–6. IEEE.
Figueiredo, E. M., Araújo, D. R., Bastos Filho, C. J., and Ludermir, T. B. (2016). Physical topology design of optical networks aided by many-objective optimization algorithms. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), pages 409–414. IEEE.
Heron, R. W., Pfeiffer, T., Veen, D. T. v., Smith, J., and Patel, S. S. (2008). Technology innovations and architecture solutions for the next-generation optical access network. Bell Labs Technical Journal, 13(1):163–181.
Nascimento, J. C., Araujo, D. R., Bastos-Filho, C. J., and Martins-Filho, J. F. (2018). Manyobjective optimization to design physical topology of optical networks with undefined node locations. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–7. IEEE.
Ramaswami, R., Sivarajan, K., and Sasaki, G. (2009). Optical networks: a practical perspective. Morgan Kaufmann.
Tanenbaum, A. S. (2016). Redes de computadores e internet. 6a edição. Editora Campus.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Da Fonseca Grunert, V. (2002). Performance assessment of multiobjective optimizers: An analysis and review. TIK-Report, 139.
Araújo, D. R. B. (2015). Planejamento de redes ópticas usando inteligência computacional e ciência das redes. PhD thesis, Universidade Federal de Pernambuco.
Brun, M., Sima, C., Hua, J., Lowey, J., Carroll, B., Suh, E., and Dougherty, E. R. (2007). Modelbased evaluation of clustering validation measures. Pattern recognition, 40(3):807–824.
Das, I. and Dennis, J. E. (1998). Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM journal on optimization, 8(3):631–657.
Deb, K. and Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4):577–601.
Dupleix, V., Araújo, D. R., and Bastos-Filho, C. J. (2016). Designing the optical network of haiti using a multi-objective evolutionary approach. In 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pages 1–6. IEEE.
Figueiredo, E. M., Araújo, D. R., Bastos Filho, C. J., and Ludermir, T. B. (2016). Physical topology design of optical networks aided by many-objective optimization algorithms. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), pages 409–414. IEEE.
Heron, R. W., Pfeiffer, T., Veen, D. T. v., Smith, J., and Patel, S. S. (2008). Technology innovations and architecture solutions for the next-generation optical access network. Bell Labs Technical Journal, 13(1):163–181.
Nascimento, J. C., Araujo, D. R., Bastos-Filho, C. J., and Martins-Filho, J. F. (2018). Manyobjective optimization to design physical topology of optical networks with undefined node locations. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–7. IEEE.
Ramaswami, R., Sivarajan, K., and Sasaki, G. (2009). Optical networks: a practical perspective. Morgan Kaufmann.
Tanenbaum, A. S. (2016). Redes de computadores e internet. 6a edição. Editora Campus.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Da Fonseca Grunert, V. (2002). Performance assessment of multiobjective optimizers: An analysis and review. TIK-Report, 139.
Brun, M., Sima, C., Hua, J., Lowey, J., Carroll, B., Suh, E., and Dougherty, E. R. (2007). Modelbased evaluation of clustering validation measures. Pattern recognition, 40(3):807–824.
Das, I. and Dennis, J. E. (1998). Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM journal on optimization, 8(3):631–657.
Deb, K. and Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4):577–601.
Dupleix, V., Araújo, D. R., and Bastos-Filho, C. J. (2016). Designing the optical network of haiti using a multi-objective evolutionary approach. In 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pages 1–6. IEEE.
Figueiredo, E. M., Araújo, D. R., Bastos Filho, C. J., and Ludermir, T. B. (2016). Physical topology design of optical networks aided by many-objective optimization algorithms. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), pages 409–414. IEEE.
Heron, R. W., Pfeiffer, T., Veen, D. T. v., Smith, J., and Patel, S. S. (2008). Technology innovations and architecture solutions for the next-generation optical access network. Bell Labs Technical Journal, 13(1):163–181.
Nascimento, J. C., Araujo, D. R., Bastos-Filho, C. J., and Martins-Filho, J. F. (2018). Manyobjective optimization to design physical topology of optical networks with undefined node locations. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–7. IEEE.
Ramaswami, R., Sivarajan, K., and Sasaki, G. (2009). Optical networks: a practical perspective. Morgan Kaufmann.
Tanenbaum, A. S. (2016). Redes de computadores e internet. 6a edição. Editora Campus.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Da Fonseca Grunert, V. (2002). Performance assessment of multiobjective optimizers: An analysis and review. TIK-Report, 139.
Araújo, D. R. B. (2015). Planejamento de redes ópticas usando inteligência computacional e ciência das redes. PhD thesis, Universidade Federal de Pernambuco.
Brun, M., Sima, C., Hua, J., Lowey, J., Carroll, B., Suh, E., and Dougherty, E. R. (2007). Modelbased evaluation of clustering validation measures. Pattern recognition, 40(3):807–824.
Das, I. and Dennis, J. E. (1998). Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM journal on optimization, 8(3):631–657.
Deb, K. and Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4):577–601.
Dupleix, V., Araújo, D. R., and Bastos-Filho, C. J. (2016). Designing the optical network of haiti using a multi-objective evolutionary approach. In 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pages 1–6. IEEE.
Figueiredo, E. M., Araújo, D. R., Bastos Filho, C. J., and Ludermir, T. B. (2016). Physical topology design of optical networks aided by many-objective optimization algorithms. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), pages 409–414. IEEE.
Heron, R. W., Pfeiffer, T., Veen, D. T. v., Smith, J., and Patel, S. S. (2008). Technology innovations and architecture solutions for the next-generation optical access network. Bell Labs Technical Journal, 13(1):163–181.
Nascimento, J. C., Araujo, D. R., Bastos-Filho, C. J., and Martins-Filho, J. F. (2018). Manyobjective optimization to design physical topology of optical networks with undefined node locations. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–7. IEEE.
Ramaswami, R., Sivarajan, K., and Sasaki, G. (2009). Optical networks: a practical perspective. Morgan Kaufmann.
Tanenbaum, A. S. (2016). Redes de computadores e internet. 6a edição. Editora Campus.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Da Fonseca Grunert, V. (2002). Performance assessment of multiobjective optimizers: An analysis and review. TIK-Report, 139.
Publicado
20/10/2020
Como Citar
NASCIMENTO, Jorge; ARAÚJO, Danilo; MIRANDA, Péricles.
Uma Nova Estratégia para Seleção de Indivíduos em Algoritmos Multiobjetivos com Busca Local Aplicados ao Problema de Projeto de Redes. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 164-175.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12126.
