Biologically-Inspired Optimisation Methods pp 219-260 | Cite as
The Radio Network Design Optimization Problem
Abstract
The fast growth and merging of communication infrastructures and services turned the planning and design of wireless networks into a very complex subject. The Radio Network Design (RND) is a NP-hard optimization problem which consists on the maximization of the coverage of a given area while minimizing the base station (BS) deployment. Solving such problems resourcefully is relevant for many fields of application and has direct impact in engineering, scientific and industrial areas. Its significance is growing due to cost dropping or profit increase allowance and can additionally be applied to several different business targets. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a non-comparable efficiency. Therefore, the aim of this work is threefold: first, to offer a reliable RND benchmark reference covering a wide algorithmic spectrum, second, to offer a grand insight of accurately comparisons of efficiency, reliability and swiftness of the different employed algorithmic models and third, to disclose reproducibility details of the implemented models, including simulations of a hardware co-processing accelerator.
Keywords
Differential Evolution Graphical Processing Unit Greedy Randomize Adaptive Search Procedure Variable Neighborhood Search Restricted Candidate ListPreview
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References
- 1.Thain, D., Tannenbaum, T., Livny, M.: Distributed computing in practice: The Condor experience. Concurrency and Computation Practice and Experience 17, 323–356 (2005)CrossRefGoogle Scholar
- 2.Mendes, S.P., Gómez-Pulido, J.A., Vega-Rodríguez, M.A., Sánchez-Pérez, J.M.: A differential based algorithm to optimize the radio network design problem. In: Proceedings of the 2nd IEEE International Conference on e-Science and Grid Computing, p. 119 (2006)Google Scholar
- 3.Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Alba, E., Vega-Pérez, D., Mendes, S., Molina, G.: Different evolutionary approaches for selecting the optimal number and locations of omnidirectional BTS in a radio network. In: Proceedings of the 11th International Conference on Computer Aided Systems Theory (2007)Google Scholar
- 4.Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Alba, E., Vega-Pérez, D., Mendes, S.P., Molina, G.: Using omnidirectional BTS and different evolutionary approaches to solve the RND problem. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 853–860. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 5.Mendes, S.P., Gómez-Pulido, J.A., Vega-Rodríguez, M.A., Pereira, A.M., Pérez, J.M.: Fast wide area network design optimisation using differential evolution. In: Proceedings of the International Conference on Advanced Engineering Computing and Applications in Sciences, pp. 3–10 (2007)Google Scholar
- 6.Mendes, S.P., Domingues, P., Pereira, D., Vale, R., Gomez-Pulido, J.A., Silva, L.M., Vega-Rodríguez, M.A., Sánchez-Pérez, J.M.: Omni-directional RND optimisation using differential evolution: In-depth analysis via high throughput computing. In: Proceedings of EPIA (2007)Google Scholar
- 7.Calegari, P., Guidec, F., Kuonen, P.: Combinatorial optimization algorithms for radio network planning. Journal of Theoretical Computer Science 263, 235–265 (2001)MATHCrossRefMathSciNetGoogle Scholar
- 8.Calegari, P., Guidec, F., Kuonen, P., Kobler, D.: Parallel island-based genetic algorithm for radio network design. Journal of Parallel and Distributed Computing 47, 86–90 (1997)CrossRefGoogle Scholar
- 9.Alba, E.: Evolutionary algorithms for optimal placement of antennae in radio network design. In: Proceedings of the International Parallel and Distributed Processing Symposium (2004)Google Scholar
- 10.Alba, E., Almeida, F., Blesa, M., Cotta, C., Díaz, M., Dorta, I., Gabarró, J., León, C., Luque, G., Petit, J., Rodríguez, C., Rojas, A., Xhafa, F.: Efficient parallel LAN/WAN algorithms for optimization: The MALLBA project. Parallel Computing 32, 415–440 (2006)CrossRefGoogle Scholar
- 11.Alba, E., Chicano, F.: On the behaviour of parallel genetic algorithms for optimal placement of antennae in telecommunications. International Journal of Foundations of Computer Science 16, 86–90 (2005)Google Scholar
- 12.Celli, G., Costamagna, E., Fanni, A.: Genetic algorithms for telecommunication network optimization. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1227–1232 (1995)Google Scholar
- 13.Meunier, H., Talbi, E.G., Reininger, P.: A multiobjective genetic algorithm for radio network optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 317–324 (2000)Google Scholar
- 14.Watanabe, S., Hiroyasu, T., Miki, M.: Parallel evolutionary multi-criterion optimization for mobile telecommunication networks optimization. In: Proceedings of Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems Conference, pp. 167–172 (2001)Google Scholar
- 15.Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)Google Scholar
- 16.Talbi, E.G., Cahon, S., Melab, N.: Designing cellular networks using a parallel hybrid metaheuristic on the computational grid. Computer Communications 30, 698–713 (2007)CrossRefGoogle Scholar
- 17.Calégari, P., Guidec, F., Kuonen, P., Chamaret, B., Ubéda, S., Josselin, S., Wagner, D., Pizarosso, M.: Radio network planning with combinatorial optimization algorithms. Theoretical Computer Science 263, 235–265 (2001)MATHCrossRefMathSciNetGoogle Scholar
- 18.Chamaret, B., Josselin, S., Kuonen, P., Pizarroso, M., Salas-Manzanedo, B., Ubeda, S., Wagner, D.: Radio network optimization with maximum independent set search. In: Proceedings of the 47th IEEE Vehicular Technology Conference, pp. 770–774 (1997)Google Scholar
- 19.He, J., Verstak, A., Watson, L., Rappaport, T., Anderson, C., Ramakrishnan, N., Shaffer, C., Tranter, W., Bae, K., Jiang, J.: Global optimization of transmitter placement in wireless communication systems. In: Proceedings of the High Performance Computing Symposium, pp. 328–333 (2002)Google Scholar
- 20.Jones, D.R., Perttunen, C.D., Stuckman, B.E.: Lipschitzian optimization without the Lipschitz constant. Journal of Optimization Theory and Applications 79, 157–181 (1993)MATHCrossRefMathSciNetGoogle Scholar
- 21.Vasquez, M., Hao, J.K.: A heuristic approach for antenna positioning in cellular networks. Journal of Heuristics 7, 443–472 (2001)MATHCrossRefGoogle Scholar
- 22.Elkamchouchi, H.M., Elragal, H.M., Makar, M.A.: Cellular radio network planning using particle swarm optimization. In: Proceedings of the Radio Science Conference, pp. 1–8 (2007)Google Scholar
- 23.Tutschku, K.: Demand-based radio network planning of cellular mobile communication systems. University of Wurzburg Research Report Series, 177 (1997)Google Scholar
- 24.Church, R.L., ReVelle, C.: The maximal covering location problem. Regional Science 30, 101–118 (1974)Google Scholar
- 25.Ibbetson, L.J., Lopes, J.B.: An automatic base site placement algorithm. In: Proceedings of the 47th IEEE Vehicular Technology Conference, pp. 760–764 (1997)Google Scholar
- 26.Fritsch, T., Hanshans, S.: An integrated approach to cellular mobile communication planning using traffic data prestructured by a self-organizing feature map. In: Proceedings of the EEE International Conference on Neural Networks, pp. 822D–822I (1993)Google Scholar
- 27.Nebro, A.J., Alba, E., Molina, G., Chicano, F., Luna, F., Durillo, J.J.: Optimal antenna placement using a new multi-objective CHC algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 876–883 (2007)Google Scholar
- 28.Mendes, S., Domingues, P., Vale, R., Pereira, D., Gomez-Pulido, J.A., Silva, L.M., Vega-Rodríguez, M.A., Sánchez-Pérez, J.M.: Omni-directional RND optimisation using differential evolution: In-depth analysis via high throughput computing. In: Proceedings of the Portuguese Conference on Artificial Intelligence (2007)Google Scholar
- 29.Donninger, C., Kure, A., Lorenz, U.: Parallel Brutus: the first distributed, FPGA accelerated chess program. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium, p. 44 (2004)Google Scholar
- 30.Eklund, S.E.: Time series forecasting using massively parallel genetic programming. In: Proceedings of the 17th International Parallel and Distributed Processing Symposium, p. 143.1 (2003)Google Scholar
- 31.Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. Computer Graphics Forum 26, 80–113 (2007)CrossRefGoogle Scholar
- 32.Fok, K., Wong, T., Man-Leung, J.: Evolutionary computing on consumer graphics hardware. IEEE Intelligent Systems 22, 69–78 (2007)CrossRefGoogle Scholar
- 33.Langdon, W.B., Banzhaf, W.: A SIMD interpreter for genetic programming on GPU graphics cards. In: Proceedings of the European Genetic Programming Conference (2008)Google Scholar
- 34.NVIDIA Corporation, Cuda Zone (2008), http://www.nvidia.com/object/cuda_home.html
- 35.Garey, M., Johnson, D.: Computers and intractability: A guide to the theory of NP-completeness. Freeman and Co., New York (1979)Google Scholar
- 36.Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMUCS, Carnegie Mellon University, pp. 94–163 (1994)Google Scholar
- 37.Baluja, S., Caruana, R.: Removing the genetics from the standard genetic algorithm. In: Proceedings of the Twelfth International Conference on Machine Learning (1995)Google Scholar
- 38.Yang, S.Y., Ho, S.L., Ni, G.Z., Machado, J.M., Wong, K.F.: A new implementation of population based incremental learning method for optimizations in electromagnetics. IEEE Transactions on Magnetics 43, 1601–1604 (2007)CrossRefGoogle Scholar
- 39.Bureerat, S., Sriworamas, K.: Population-based incremental learning for multiobjective optimisation. Soft Computing in Industrial Applications, Advances in Soft Computing 39, 223–232 (2007)CrossRefGoogle Scholar
- 40.Hong, Y., Kwong, S., Chang, Y., Ren, Q.: Clustering ensembles guided unsupervised feature selection using population based incremental learning algorithm. Pattern Recognition 41(9), 2742–2756 (2009)CrossRefGoogle Scholar
- 41.Jelodar, M.S., Fakhraie, S.M., Ahmadabadi, M.N.: A new approach for training of artificial neural networks using population based incremental learning (PBIL). In: Proceedings of the International Conference on Computational Intelligence, pp. 165–168 (2004)Google Scholar
- 42.Chiang, F., Braun, R.: Towards a management paradigm with a constrained benchmark for autonomic communications. In: Wang, Y., Cheung, Y.-m., Liu, H. (eds.) CIS 2006. LNCS, vol. 4456, pp. 250–258. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 43.Domínguez-González, D., Chaves-González, J.M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Using PBIL for solving a real-world frequency assignment problem in GSM networks. New Trends in Artificial Intelligence, 207–218 (2007)Google Scholar
- 44.Papadimitriou, G.I., Obaidat, M.S., Pomportsis, A.S.: On the use of population-based incremental learning in the medium access control of broadcast communication systems. In: Proceedings of the 10th IEEE International Conference on Electronics, Circuits and Systems, pp. 1260–1263 (2003)Google Scholar
- 45.Kendall, R., Braun, R.: Digital communication filter design by stochastic optimization. In: Workshop on the Applications of Radio Science (2002)Google Scholar
- 46.Price, K., Storn, R.: Differential evolution – a simple evolution strategy for fast optimisation. Dr. Dobb’s Journal 22, 18–24 (1997)Google Scholar
- 47.Price, K., Storn, R.: Web site of DE (2006), http://www.ICSI.Berkeley.edu/~storn/code.html (accessed July 1, 2006)
- 48.Joshi, R., Sanderson, A.: Minimal representation multisensor fusion using differential evolution. In: Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, p. 266 (1997)Google Scholar
- 49.Vasan, A., Raju, K.: Optimal reservoir operation using differential evolution. In: Proceedings of International Conference on Hydraulic Engineering: Research and Practice (2004)Google Scholar
- 50.Abbass, H.A., Sarker, R.: The Pareto differential evolution algorithm. International Journal on Artificial Intelligence Tools 11, 531–552 (2002)CrossRefGoogle Scholar
- 51.Storn, R., Price, K.: A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, The University of California, Berkley (1995)Google Scholar
- 52.Lampinen, J., Zelinka, I.: Mixed variable non-linear optimization by differential evolution. In: Proceedings of the 2nd International Prediction Conference, pp. 45–55 (1999)Google Scholar
- 53.Aragão, M.P., Ribeiro, C.C., Uchoa, E., Werneck, R.F.: Hybrid local search for the Steiner problem in graphs. In: Proceedings of the 4th Metaheuristics International Conference (2001)Google Scholar
- 54.Festa, P., Resende, M.G.: An annotated bibliography of GRASP. Technical Report TD-5WYSEW, AT&T Labs Research (2004)Google Scholar
- 55.Mavridou, T., Pardalos, P.M., Pitsoulis, L.S., Resende, M.G.: A GRASP for the biquadratic assignment problem. European Journal of Operational Research 105, 613–621 (1998)MATHCrossRefGoogle Scholar
- 56.Martins, S.L., Resende, M.G., Ribeiro, C.C., Pardalos, P.M.: A parallel GRASP for the Steiner tree problem in graphs using a hybrid local search strategy. Journal of Global Optimization 17, 267–283 (2000)MATHCrossRefMathSciNetGoogle Scholar
- 57.Pardalos, P.M., Qian, T., Resende, M.G.: A greedy randomized adaptive search procedure for the feedback vertex set problem. Journal of Combinatorial Optimization 2, 399–412 (1999)MATHCrossRefMathSciNetGoogle Scholar
- 58.Rosseti, R., Aragão, M.P., Ribeiro, C.C., Uchoa, E., Werneck, R.F.: New benchmarck instances for the Steiner problem in graphs. In: Proceedings of the 4th Metaheuristics International Conference (2001)Google Scholar
- 59.Resende, M.G.: Computing approximate solutions of the maximum covering problem using GRASP. Journal of Heuristics 4, 161–171 (1998)MATHCrossRefGoogle Scholar
- 60.Resende, M.G., Ribeiro, C.C.: A GRASP for graph planarization. Journal of Heuristics 4, 171–181 (1998)CrossRefGoogle Scholar
- 61.Ribeiro, C.C., Uchoa, E., Werneck, R.F.: A hybrid GRASP with perturbations for the Steiner problem in graphs. INFORMS Journal on Computing 14, 228–246 (2002)CrossRefMathSciNetGoogle Scholar
- 62.Resende, M.G., Ribeiro, C.C.: Greedy randomized adaptive search procedures. Technical Report TD-53RSJY, AT&T Labs Research (2002)Google Scholar
- 63.Feo, T.A., Resende, M.G.: A probabilistic heuristic for a computationally difficult set covering problem. Operation Research Letters 8, 67–71 (1989)MATHCrossRefMathSciNetGoogle Scholar
- 64.Feo, T.A., Resende, M.G.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109–133 (1995)MATHCrossRefMathSciNetGoogle Scholar
- 65.Mladenovic, N., Hansen, P.: Variable neighbourhood search. Computers and Operations Research 24, 1097–1100 (1997)MATHCrossRefMathSciNetGoogle Scholar
- 66.Hansen, P., Mladenovic, N.: Variable neighbourhood search: Principles and applications. European Journal of Operational Research 130, 449–467 (2001)MATHCrossRefMathSciNetGoogle Scholar
- 67.Hansen, P., Mladenovic, N.: A Tutorial on variable neighborhood search. Technical Report - GERAD and Mathematical Institute, SANU, Belgrade (2003)Google Scholar
- 68.Fleszar, K., Hindi, K.S.: New heuristics for one-dimensional bin-packing. Computers and Operations Research 29, 821–839 (2002)MATHCrossRefGoogle Scholar
- 69.Hansen, P., Mladenovic, N., Dragan, U.: Variable neighborhood search for the maximum clique. Discrete Applied Mathematics 145, 117–125 (2004)MATHCrossRefMathSciNetGoogle Scholar
- 70.Liberti, L., Draži, M.: Variable neighbourhood search for the global optimization of constrained NLPs. In: Proceedings of Global Optimization, pp. 1–5 (2005)Google Scholar
- 71.Burke, E.K., Cowling, P., Keuthen, R.: Implementation report: Variable neighbourhood search. Technical Report - University of Nottingham, NG8 1BB (2000)Google Scholar
- 72.Avanthay, C., Hertz, A., Zufferey, N.: A variable neighborhood search for graph coloring. European Journal of Operational Research 151, 379–388 (2003)MATHCrossRefMathSciNetGoogle Scholar
- 73.Galinier, P., Hao, J.K.: Hybrid evolutionary algorithms for graph coloring. Journal of Combinatorial Optimization 3, 379–397 (1999)MATHCrossRefMathSciNetGoogle Scholar
- 74.Caporossi, G., Hansen, P.: Variable neighborhood search for extremal graphs: Three ways to automate finding conjectures. Discrete Mathematics 276, 81–94 (2004)MATHCrossRefMathSciNetGoogle Scholar
- 75.Pérez, J.A., Moreno-Vega, J.M., Martín, I.R.: Variable neighbourhood tabu search and its application to the median cycle problem. European Journal of Operational Research 151, 365–378 (2003)MATHCrossRefMathSciNetGoogle Scholar
- 76.Fleszar, K., Hindi, K.H.: Solving the resource-constrained project scheduling problem by a variable neighbourhood search. European Journal of Operational Research 155, 402–413 (2004)MATHCrossRefMathSciNetGoogle Scholar
- 77.Burke, E.K., Causmaecker, P.D., Petrovic, S., Berghe, G.V.: Variable neighbourhood search for nurse rostering problems. In: Resende, M.G., Sousa, J.P. (eds.) Metaheuristics: Computer Decision-Making, pp. 153–172. Kluwer, Norwell (2003)Google Scholar
- 78.Pérez, J.A., Mladenovic, N., Batista, B.M., Amo, I.J.: Variable Neighbourhood Search. Springer, New York (2006)Google Scholar
- 79.Lusa, A., Potts, C.N.: A variable neighbourhood search algorithm for the constrained task allocation problem. Journal of the Operational Research Society 59(6), 812–822 (2007)CrossRefGoogle Scholar
- 80.Lejeune, M.A.: A variable neighborhood decomposition search method for supply chain management planning problems. European Journal of Operational Research 175(2), 959–976 (2006)MATHCrossRefGoogle Scholar
- 81.Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
- 82.Buffa, E.S., Armour, G.C., Vollmann, T.E.: Allocating facilities with CRAFT. Harvard Business Review, 136–158 (1964)Google Scholar
- 83.Saez, Y., Isasi, P., Segovia, J., Hernandez, J.C.: Reference chromosome to overcome user fatigue in IEC. New Generation Computing 23, 129–142 (2005)CrossRefGoogle Scholar
- 84.Saez, Y., Isasi, P., Segovia, J.: Interactive evolutionary computation algorithms applied to solve Rastrigin test functions. In: Proceedings of the 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, pp. 682–691 (2005)Google Scholar
- 85.Dawkins, R.: The Selfish Gene. Oxford University Press, New York (1976)Google Scholar
- 86.Gómez-Pulido, J.: Web site of Net-Centric Optimization (OPLINK:UNEX) (2006), http://oplink.unex.es/rnd (accessed April 1, 2008)
- 87.Mayer, U.: NBenchProject (2007), http://www.tux.org/~mayer/linux/ (accessed December 1, 2007)
- 88.Anderson, D.P.: BOINC (2007), http://boinc.berkeley.edu (accessed June 1, 2008)
- 89.Anderson, D.P.: BOINC: A system for public-Resource computing and storage. In: Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing, pp. 4–10 (2004)Google Scholar
- 90.RND@home (2008), http://arcoboinc.unex.es/rnd (accessed March 1, 2008)
- 91.LinEx (2008), http://www.linex.org (accessed March 1, 2008)
- 92.Buell, D., El-Ghazawi, T., Gaj, K., Kindratenko, V.: High-performance reconfigurable computting. Computer 40, 23–27 (2007)CrossRefGoogle Scholar
- 93.Vega-Rodríguez, M.A., Sánchez-Pérez, J.M., Gómez-Pulido, J.A.: Guest editors’ introduction - special issue on FPGAs: Applications and designs. Microprocessors and Microsystems 28, 193–196 (2004)CrossRefGoogle Scholar
- 94.Hsiao, J.M., Tsai, C.J.: Analysis of an SOC architecture for MPEG reconfigurable video coding framework. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 761–764 (2007)Google Scholar
- 95.Gómez-Pulido, J.A., Vega-Rodríguez, M.A., Pérez, J.M., Mendes, S.P.: Diseño y prototipado de un processador para el cálculo de la cobertura en el diseño de redes de radiocomunicaciones. In: VII Jornadas de Computación Reconfigurable y Aplicaciones (2007)Google Scholar
- 96.Alba, E., Molina, G., Chicano, F.: Optimal placement of antennae using metaheuristics. In: Boyanov, T., Dimova, S., Georgiev, K., Nikolov, G. (eds.) NMA 2006. LNCS, vol. 4310, pp. 214–222. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 97.Alba, E., Cotta, C., Chicano, F., Nebro, A.J.: Parallel evolutionary algorithms in telecommunications: Two case studies. In: Proceedings of Congreso Argentino de Ciencias de la Computación (2002)Google Scholar
- 98.Xilinx Inc., Xilinx (2008), http://www.xilinx.com (accessed March 1, 2008)
- 99.Xess Corporation (200) Xess, http://www.xess.com (accessed March 1, 2008)
- 100.Barr, R.S., Golden, B.L., Kelly, J.P., Resende, M.G., Stewart, W.: Designing and reporting on computational experiments with heuristic methods. Journal of Heuristics 1, 9–32 (1996)CrossRefGoogle Scholar
- 101.Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Proceedings of the 6th International Mendel Conference on Soft Computing, pp. 76–83 (2000)Google Scholar