Paulo Henrique Siqueira

Publicações ➙ Citações dos meus trabalhos

trabalhos que citam: Siqueira, P.H. ; Scheer, S. ; Steiner, M.T.A. A new approach to solve the Traveling Salesman Problem. Neurocomputing (Amsterdam), v. 70, p. 1013-1021, 2007.

53. Saadatmand-Tarzjan, M. (2018). On computational complexity of the constructive-optimizer neural network for the traveling salesman problem. Neurocomputing, 321, 82-91.
52. Ottoni, A. L., Nepomuceno, E. G., & de Oliveira, M. S. (2018). A Response Surface Model Approach to Parameter Estimation of Reinforcement Learning for the Travelling Salesman Problem. Journal of Control, Automation and Electrical Systems, 29(3), 350-359.
51. Agrawal, A. P., & Kaur, A. (2017). An empirical evaluation of memory less and memory using meta-heuristics for solving travelling salesman problem. International Journal of Computational Systems Engineering, 3(4), 228-236.
50. Wagh, A., & Nemade, V. (2017). Query Optimization using Modified Ant Colony Algorithm. International Journal of Computer Applications, 167(2).
49. Qin, L. (2016). Hybrid algorithm for multi objective optimization problem. In MECHANICS AND MECHANICAL ENGINEERING: Proceedings of the 2015 International Conference (MME2015) (pp. 869-879).
48. Jiang, Z., Discrete Bat Algorithm for Traveling Salesman Problem. 3rd International Conference on Information Science and Control Engineering, p. 343-347, 2016.
47. Lin, Y., Bian, Z., Liu, X. Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing – tabu search algorithm to solve the symmetrical traveling salesman problem. Applied Soft Computing, V. 49, N. 1, p. 937-952, 2016.
46. Duan, P., Ai,Y. Research on an Improved Ant Colony Optimization Algorithm and its Application. International Journal of Hybrid Information Technology, V. 9, N. 4, p. 223-234, 2016.
45. Kosolov, A. D. The toolkit for automation of parallel solving of combinatorial problems in heterogeneous distributed computing environment. Modern problems of science and education, V. 2, N. 2, p. 275-283, 2015.
44. Tarkov, M. S. Solving the traveling salesman problem using a recurrent neural network. Numerical Analysis and Applications, V. 8, N. 3, p. 275-283, 2015.
43. Tarkov, M. S. Mapping parallel program graphs onto graphs of distributed computer systems by neural network algorithms. Parallel Programming: Practical Aspects, Models and Current Limitations, 2014.
42. Agrawal, A. P.; Kaur, A. A comparative analysis of memory using and memory less algorithms for Quadratic Assignment Problem. Confluence The Next Generation Information Technology Summit (Confluence), 2014 5th International Conference, IEEE, p. 815-820, 2014.
41. Michael Jr, D. R. W. Mind, Body, World: Foundations of Cognitive Science. AU Press Athabasca University, 2013.

40. Li, Y.; Ma, K.; Zhang, J. An Efficient Multicore based Parallel Computing Approach for TSP Problems. 2013 Ninth International Conference on Semantics, Knowledge and Grids, SKG 2013, p. 98-104, 2013.

39. Tarkov, M.S. On the efficient construction of hamiltonian cycles in distributed computer systems by recurrent neural networks. 2013 International Siberian Conference on Control and Communications, SIBCON 2013, p. 1-4, 2013.

38. Tarkov, M.S. On the efficient construction of Hamiltonian cycles in distributed computer systems recurrent neural networks. COORDINATING COUNCIL, p. 157-170, 2013.

37. Santos, A.; Barradas, A.; Labidini, S.; Costa, N. A Comparison Between Optimization Heuristics of the Best Path Problem Applied to S-Route, GEOProcessing 2013, The Fifth International Conference on Advanced Geographic Information Systems, Applications, and Services, Nice, France, p. 172-177, 2013.

36. Deng, W.; Chen, R.; He, B.; Liu, Y.; Yin, L.; Guo, J. A novel two-stage hybrid swarm intelligence optimization algorithm and application, Soft Computing, v. 16, n. 10, p. 1707-1722, 2012.

35. Zhang, X.X.; Tong, J.W.; Liu, T. Hybrid path relinking algorithm for solving Traveling Salesman problem, Computer Engineering, V. 38, n. 12, p. 122-125, 2012.

34. Liu, F. A dual population parallel ant colony optimization algorithm for solving the traveling salesman problem, Journal of Convergence Information Technology, V. 7, n. 5, p. 66-74, 2012.

33. Tarkov, M. Optimization of mapping graphs of parallel programs onto graphs of distributed computer systems by recurrent neural network, Recurrent Neural Networks and Soft Computing, Dr. Mahmoud ElHefnawi (Ed.), In-Tech, 2012.

32. Zhou, Z.; Duan, Y.; Li, P.; Pi, Y.; Yi, S. Cultivated land prediction based on least square support vector machines, Application Research of Computers, V. 29, n. 3, p. 873-876, 2012.

31. Meng, X.; Shen, Z.; Yue, Y.; Pi, Y.; Tan, W. An Improvement to the Coordination Method of Ant Colony Algorithm, cdciem, p. 114-117, 2012 International Conference on Computer Distributed Control and Intelligent Enviromental Monitoring, 2012.

30. Li, S.W.; Wang, J.Q.; Zeng, J.W. Uncertain linguistic information objectives preference VRPTW of ant colony algorithm, Application Research of Computers, v. 29, n. 3, p. 869-876, 2012.

29. Li, S.W.; Wang, J.Q.; Zeng, J.W. VRPTW problem solving multi-objective fuzzy preference ant colony algorithm, Application Research of Computers, v. 28, n. 12, p. 4495-4499, 2011.

28. Tao, P.; Yueyu,L.; Wengui, C. Matrix Circle Intelligent Algorithm for the Traveling Salesman Problem. Industrial Engineering Journal, v. 14, n. 5, p. 89-91, 2011.

27. Zhang, Y.; Wu, L.; Wang, S.;Wei, G.; Yan, J.; Zhu, Q. Improved ant colony algorithm based on membership cloud models, Computer Engineering and Applications, v. 47, n. 14, p. 46-55, 2011.

26. Marinakis, Y.; Marinaki, M.; Dounias, G. Honey bees mating optimization algorithm for the Euclidean traveling salesman problem. Information Sciences, v. 181, n. 20, 2011.

25. Zhang, X.; Bai, Q.; Yun, X. A new hybrid artificial bee colony algorithm for the traveling salesman problem. IEEE 3rd International Conference on Communication Software and Networks (ICCSN). p. 155-159, 2011.

24. Tarkov, M.S. Construction of Hamiltonian cycles by recurrent neural networks in graphs of distributed computer systems. Numerical Analysis and Applications, v. 3, n. 4, p. 381-388, 2010.

23. Chen, W.G.; Li, J.D.; Xiang, X.L.; Pan, T. A Single-parameter intelligent algorithm for the traveling salesman problem. 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), v. 3, p. 318-320, 2010.

22. Dawson, M.R.W.; Dupuis, B.; Wilson, M. From Bricks toBrains: The Embodied Cognitive Science of LEGO Robots. Published by AU Press, Athabasca University, 2010.

21. Wang, J.; Luo, C. Adaptive neighborhood method & Genetic Algorithm for solving Travelling Salesman Problem.Computer Engineering and Applications, v. 46, n. 27, p. 20-24, 2010.

20. Wong, L.P.; Low, M.Y.H.; Chong, C.S. Bee colony optimization with local search for traveling salesman problem. International Journal on Artificial Intelligence Tools, v. 19, n. 3, p. 305-334, 2010.

19. Tarkov, M.S. Construction of Hamiltonian cycles of recurrent neural network in toroidal graphs with ribs defects. Scientific Session of Moscow Engineering Physics Institute Part 2: XII All-RussianScientific Conference: Neuroinformatics (Тарков, M.C. Построение Гамильтоновых Циклов Рекуррентной Нейронной Сетью В Тороидальных Графах С Дефектами Ребер. Научная Сессия МИФИ-2010. Ч.2 XII Всероссийская Научно-Техническая Конференция. Нейроинформатика), p. 26-37, 2010.

18. Tarkov, M.S. Construction of Hamiltonian cycles by recurrent neural networks in graphs of distributed computer systems, Siberian Journal of Numerical Mathematics (Тарков, M.C. Построение гамильтоновых циклов в графах
распределенных вычислительных систем рекуррентными нейронными сетями, Сибирский журнал вычислительной математики), v. 13, n. 4, p. 467-475, 2010.

17. Tarkov, M.S.; Dugarov, G.A. Parallel algorithm for solving traveling salesman problem using Recurrent Neural Networks. Problem Info (Тарков, M.C.; Дугаров, Г. А. Параллельный Алгоритм Решения Задачи Коммивояжера C
Использованием Рекуррентной Нейронной Сети, Проблемы Информатики), v. 2, n. 6, p. 4-9, 2010.

16. Meng, W.; Xu, W. Application of Partheno-Genetic Algorithm in Traveling Salesman Problem. Logistics Technology, v. 28, n. 11, p. 73-75, 2009.

15. Marinakis, Y. Heuristic and Metaheuristic Algorithms for the Traveling Salesman Problem. Encyclopedia of Optimization – Part 8. Floudas, C.A. and Pardalos, P.M.(Eds.), p. 1498-1506, 2009.

14. Zhang, Y.; Wu, L.; Wei, G. Comparison on solving TSP via intelligent algorithm. Computer Engineering and Applications, v. 45, n. 11, p. 11-15, 2009.

13. Wang, J. Solving TSP Via Chaotic PSO and HNN Algorithm, Computer Knowledge And Technology, v. 5, n. 13, p. 22-24, 2009.

12. Hammer, B.; Schrauwen, B.; Steil, J.J. Recent advances in efficient learning of recurrent networks, Proceedings of ESANN’2009 – European Symposium on Artificial Neural Networks – Advances in Computational Intelligence and Learning, Bruges (Belgium), D-side public, p. 213-226, 2009.

11. Zhang, J. Natural Computation for the Traveling Salesman Problem, ICICTA Second International Conference on Intelligent Computation Technology and Automation, October 2009 v. 1, p.366-369.

10. Zhang, Y.; Wu, L.; Wu, H. Comparison of neural network and evolutionary algorithm on engineering optimization. Computer Engineering and Applications, v. 45, n. 3, p. 1-6, 2009.

9. Luo, C.Y.; Lu, B.; Liu, F. Neighbour field method for population initialization of TSP. Chongqing Daxue Xuebao/Journal of Chongqing University, v. 32, n. 11, p. 1311-1315, 2009.

8. Hu, Z.; Ding, Y.; Shao, Q. Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system. Expert Systems with Applications, v. 36, n. 3, p. 5248-5255, 2009.

7. Li, M.; Yi, Z.; Zhu, M. Solving TSP by using Lotka-Volterra neural networks. Neurocomputing, v. 72, n. 16-18, p. 3873-3880, 2009.

6. artigo do Wikipedia sobre o problema do Caixeiro Viajante

🌎 site | 📥 pdf

5. Wong, L.P.; Low, M.Y.H.; Chong, C.S. Bee colony optimization with local search for traveling salesman problem. INDIN 2008: 6th IEEE International Conference on Industrial Informatics, p. 1019-1025, 2008.

4. Zhang, X.; Tang, L. A new hybrid ant colony optimization algorithm for the traveling salesman problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5227, p. 148-155, 2008.

3. Zou, G.; Jia, L. A novel evolutionary algorithm for bi-objective symmetric traveling salesman problem. Journal of Computational Information Systems, v.4, n. 5, p. 2051-2056, 2008.

2. Zhang, X.; Tang, L. An ACO&SS algorithm for traveling salesman problem. Kongzhi yu Juece/Control and Decision, v. 23, n. 7, p. 762-766, 2008.

1. Zheng, S.; Hou, D.B.; Zhou, Z.K. Ant colony algorithm with dynamic transition probability. Kongzhi yu Juece/Control and Decision, v. 23, n. 2, p. 225-228, 2008.

trabalhos que citam: Siqueira, P. H. (2012). Application of Wang’s Recurrent Neural Network to solve the Transportation problem. IJCSNS, 12(7), 50.

1. Xavier, G. M. T., Castañeda, F. G., Nava, L. M. F., & Cadenas, J. A. M. (2018). Memristive recurrent neural network. Neurocomputing, 273, 281-295.

trabalhos que citam: Souza, L. V. ; Siqueira, P. H. Heuristic Methods Applied to the Optimization School Bus Transportation Routes: A Real Case. Lecture Notes in Computer Science, v. 6097, p. 247-256, 2010.

14. ELLEGOOD, William A. et al. School bus Routing Problem: Contemporary Trends and Research Directions. Omega, 2019.
13. Campbell, James, et al. Contemporary Trends and Research Directions. 2019.
12. Reihaneh, Mohammad. Integrated Routing Models for Enhanced Product and Service Delivery. 2018.
11. REIHANEH, Mohammad; GHONIEM, Ahmed. A multi-start optimization-based heuristic for a food bank distribution problem. Journal of the Operational Research Society, v. 69, n. 5, p. 691-706, 2018.
10.Alsobky, A., Hrkút, P., & Mikušová, M. (2017, November). A Smart Application for University Bus Routes Optimization. In First International Conference on Intelligent Transport Systems (pp. 12-20). Springer, Cham.
9. Siqueira, V. S.; Lima, F. J. E.; Silva, E. N.; Silva, R. V. S.; Rocha, M. L. Implementation of the Metaheuristic GRASP Applied to the School Bus Routing Problem. International Journal of e-Education, e-Business, e-Management and e-Learning, V. 6, N. 2, p. 137-145, 2016.

8. Campbell, J. F.; North J. W.; Ellegood, W. A. Modeling Mixed Load School Bus Routing. Quantitative Approaches in Logistics and Supply Chain Management. Springer International Publishing, p. 3-30, 2015.

7. Hu, J.H.; Deng, J.; Huang, Z. Travel path based method for estimating public transit section flow, Application Research of Computers, v. 31, n. 5, p. 1399-1402, 2014.

6. Dang, L.X.; Chen, X.; Kong, Y.F. Review os School Bus Routing Problem: Concept, Model and Optimization Algprithms, Journal of Henan University (Natural Science), v. 43, n. 6, p. 43-50, 2013.

5. Dang, L.X.; Wang, Z.; Liu, Q.S.; Kong, Y.F. Heuristic algorithm for solving mixed load school bus routing problem, Computer Science, v. 40, n. 7, p. 248-256, 2013.

4. Malairajan, R.A.; Ganesh, K.; Muhos, M.; Anbuudayasankar, S.P. Class of resource allocation problems in supply chain – a review, International Journal of Business Innovation and Research, v. 7, n. 1, p. 113-139, 2013.

3. Malairajan, R.A.; Ganesh, K.; Muhos, M.; IskaniusSolak, P. CLING: heuristic to solve integrated resource allocation and routing problem with time window, International Journal of Services and Operations Management, v. 13, n. 2, p. 247-266, 2012.

2. Solak, S. ; Scherrer, C. ; Ghoniem, A. The stop-and-drop problem in nonprofit food distribution networks, Annals of Operations Research, p. 1-20, 2012.

1. Malairajan, J. Models and heuristics for a class of resource allocation problems in supply chain, Tese de doutorado: Anna University, 2011.

trabalhos que citam: Siqueira, P.H. ; Steiner, M.T.A.; Scheer, S. Recurrent Neural Networks with the Soft ‘Winner Takes All’ principle applied to the Traveling Salesman Problem. In: Donald Davendra (Org.). Traveling Salesman Problem, Theory and Applications, Rijeka: InTech Education and Publishing, 2010, v. 1, p. 177-196.

1. Zhoua, Y., Luoa, Q., Chena, H., Hea, A., Wua, J., A discrete invasive weed optimization algorithm for solving traveling salesman problem, Neurocomputing, V. 151, n. 3, p. 1227–1236, 2015.

trabalhos que citam: Siqueira, P.H. ; Steiner, M.T.A. ; Scheer, S. Recurrent Neural Network with Soft ‘Winner Takes All’ principle for the TSP. In: ICNC 2010 – International Conference on Neural Computation, 2010, Valencia. Proceedings of the International Conference on Fuzzy Computation and International Conference on Neural Computation, 2010. v. 1. p. 265-270.

2. Sahai, A. K.; Borah, N.; Chattopadhyay, R.; Joseph, S.; Abhilash, S. A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map, Climate Dynamics, v. 48, n. 7-8,  p. 2437-2451, 2017.

1. Tarkov, M. Optimization of mapping graphs of parallel programs onto graphs of distributed computer systems by recurrent neural network, Recurrent Neural Networks and Soft Computing, Dr. Mahmoud ElHefnawi (Ed.), In-Tech, 2012.

trabalhos que citam: Siqueira, P.H. ; Scheer, S. ; Steiner, M.T.A. A New Neural Network Approach to the Traveling Salesman Problem. Lecture Notes in Computer Science, Berlin, v. 3971, n. 1, p. 391-398, 2006.

2. Zhu, R.; Zhong, S. Efficient solution for traveling salesman problem based on neural network, Journal of Computational Information Systems, v. 4, 2008.

1. Benini, F.A.V. Rede Neural Recorrente com Perturbação Simultânea Aplicada no Problema do Caixeiro Viajante. Dissertação de Mestrado da Escola de Engenharia da USP, 2008.

trabalhos que citam: Siqueira, P.H. ; Barboza, Â.O. ; Carnieri, C. ; Steiner, M.T.A. Uma proposta de solução para o problema da construção de escalas de motoristas e cobradores de ônibus por meio do algoritmo do matching de peso máximo. Gestão e Produção (UFSCar), São Carlos, SP, v. 11, n. 2, p. 187-196, 2004.

3. Silva, J. L. C.; Arruda, J. B. F.; Silva, B. C. H. A Mathematical Model Used in Repairs for Electric Power Distribution Network. Latin America Transactions, IEEE (Revista IEEE America Latina) v.12, n.6, p.1101-1105, 2014.

2. Souza, R.A. Otimização das escalas de trabalho dos atendentes e dimensionamento de um call center receptivo. Tese de doutorado de Engenharia de produção da UFSC, 2010.

1. Poltosi, M.R. Elaboração de escalas de trabalho de técnicos de enfermagem com Busca Tabú e Algoritmos Genéticos. Dissertação de Mestrado de Computação Aplicada de São Leopoldo, 2007.

trabalhos que citam: Siqueira, P.H. Aplicação do algoritmo do matching no problema da construção de escalas de motoristas e cobradores de ônibus. Dissertação de Mestrado, Setor de Tecnologia e de Ciências Exatas, UFPR, 1999.

10. Bonato, J. V. R. Clusterização de dados meteorológicos para comparação de técnicas de nowcasting. Dissertação de Mestrado em Métodos Numéricos em Engenharia, UFPR, 2014.

9. Silva, G.P.; Gomes, A.C.; Souza, M.J.F. Modelos de Fluxo em Redes para o Problema de Escala de Motoristas de Ônibus Urbano, XXVIII CNMAC: Congresso Nacional de Matemática Aplicada e Computacional, setembro de 2005, São Paulo-SP.

8. Kotsko, E.G.S.; Machado, A.L.F.; Santos, E.M. Otimização na alocação de professores na construção de uma grade horária escolar. Ambiência, v. 1, n. 1, p. 31-45, 2005.

7. Silva, G.P.; Gomes, A.C.; Souza, M.J.F. Uma Metodologia Baseada em Emparelhamentos Sucessivos Aplicada ao Problema de Programação de Tripulações. XXXV SBPO: Simpósio Brasileiro de Pesquisa Operaciona,l p. 1037-1046, 2005, Gramado-RS.

6. Paes, F.G. Otimização de rotas para a coleta do lixo doméstico: um tratamento GRASP do problema do Carteiro Chinês Misto (PCCM). Dissertação do Centro de Ciência e Tecnologia da Universidade Estadual do Norte Fluminense, 2004.

5. Souza, M.J.F.; Cardoso, L.X.T.; Silva, G.P.; Rodrigues, M.M.S.; Mapa, S.M.S. Metaheurísticas Aplicadas ao Problema de Programação de Tripulações no Sistema de Transporte Público. TEMA Tendências em Matemática Aplicada e Computacional, v. 5, n. 2, p. 357-368, 2004.

4. Mapa, S.M.S. Redução de custos da programação diária de tripulações de ônibus urbano via metaheurísticas. Monografia de graduação em Engenharia de Produção da UFOP, 2004.

3. Souza, M.J.F.; Silva, G.P.; Mapa, S.M.S. Métodos de pesquisa em vizinhança variável aplicados à resolução do problema de programação diária de tripulações de ônibus urbano. XVIII ANPET, Congresso de Pesquisa e Ensino em Transportes, novembro de 2004, Florianópolis-SC.

2. Souza, M.J.F.; Cardoso, L.X.T.; Silva, G.P. Programação de Tripulações de ônibus urbano: uma abordagem heurística. XXXV SBPO: novembro de 2003, Natal-RN.

1. Souza, M.J.F.; Silva, G.P.; Rodrigues, M.M.S.; Mapa, S.M.S. Um estudo das heurísticas Simulated Annealing e VNS aplicadas ao problema de programação de tripulações. XXIII Encontro Nacional de Engenharia de Produção, outubro de 2003, Ouro Preto-MG.

trabalhos que citam: Siqueira, P.H. Uma nova abordagem na Resolução do problema do Caixeiro Viajante. Tese de Doutorado, Setores de Tecnologia e de Ciências Exatas, UFPR, 2005.

9. José, C. M. A. Coordenação ótima de múltiplos robôs de serviço e de recarga em tarefas persistentes. Dissertação de Mestrado em Engenharia Elétrica, UFJF, 2015.
8. Bonato, J. V. R. Clusterização de dados meteorológicos para comparação de técnicas de nowcasting. Dissertação de Mestrado em Métodos Numéricos em Engenharia, UFPR, 2014.

7. Silva, G.L.; Fonseca, A.P. Uma nova abordagem para o problema de roteirização de veículos com restrições operacionais. Anais do XXV ANPET – Congresso de Ensino e Pesquisa em Transportes, 2011.

6. Silva, G.L. Uma nova abordagem para o problema de roteirização de veículos com restrições operacionais. Tese de Doutorado em Transportes da UNB, 2010.

5. Azevedo, R.P.; Santo, Y.B.I. Estudo para otimização de distribuição de medicamentos de uma farmácia através de roteirização de veículos. In: Renata Melo e Silva de Oliveira (Org.) Engenharia de Produção: Tópicos e Aplicações. EDUEPA, Universidade do Estado do Pará, 2010.

4. Benini, F.A.V. Rede Neural Recorrente com Perturbação Simultânea Aplicada no Problema do Caixeiro Viajante. Dissertação de Mestrado da Escola de Engenharia da USP, 2008.

3. Vitor, A. Determinação do roteamento dos atendimentos de uma empresa de comercialização agrícola – variações de soluções heurísticas. Dissertação de mestrado em Métodos Numéricos em Engenharia da UFPR, 2007.

2. Azevedo, S.O. Sistema de agentes poligínicos para esteganálise de imagens digitais. Dissertação de mestrado em Sistemas e Computação da UFRN, 2007.

1. Herrera, B.A.L.M. Combinação de enxame de partículas com inspiração quântica e método Linkernighan-Helsgaun aplicada ao problema do Caixeiro viajante Dissertação de mestrado em Engenharia de Produção e Sistemas da PUC-PR, 2007.