Modelo de optimización multiobjetivo para el algoritmo evolutivo HAEA (Hybrid Adaptive Evolutionary Algorithm)

dc.contributorGómez Perdomo, Jonatan
dc.contributorALIFE: Grupo de Investigación en Vida Artificial
dc.creatorCastro Pinto, Juan Camilo
dc.date.accessioned2020-06-12T00:13:43Z
dc.date.available2020-06-12T00:13:43Z
dc.date.created2020-06-12T00:13:43Z
dc.date.issued2020-02-14
dc.identifierCastro, J. (2020). Modelo de optimización multiobjetivo para el algoritmo evolutivo HAEA (Hybrid Adaptive Evolutionary Algorithm) (Tesis de maestría). Universidad Nacional de Colombia, Bogotá, Colombia
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/77651
dc.description.abstractThis work introduces a new distributed multi-objective evolutionary algorithm which is an extension of the proposed Hybrid Adaptive Evolutionary Algorithm (HAEA). This new algorithm, called NSHAEA, defines a fitness function based on Pareto dominance. Like HAEA, a set of genetic operators can be used to change individuals, but only one can be used in an iteration. A new replacement method is used when the same fitness value are the same in a several number of individuals. This method uses a niching technique, which was proposed to solve multiobjective optimization problems with NSGA II and is based on the Crowding Distance technique. For the distributed part, a master-slave model is used and implemented on CPU, GPU and in a cloud service. Finally the proposed algorithm is tested using benchmark functions and the performance is compared with some others muti-objective algorithms. This comparison shows a competitive new multi-objective algorithm.
dc.description.abstractEste trabajo presenta un nuevo algoritmo distribuido de optimización multiobjetivo, el cual es una extensión del algoritmo HAEA (Hybrid Adaptive Evolutionary Algorithm). Este nuevo algoritmo, llamado NSHAEA, define una función de ajuste basada en la dominancia de Pareto. Al igual que HAEA, un conjunto de operadores genéticos es usado para crear una nueva población de individuos, pero sólo uno puede ser aplicado en cada iteración. Un nuevo método de reemplazo es propuesto con el fin de diferenciar los individuos que tengan el mismo valor en su función de ajuste. Este método de reemplazo usa una técnica de nichos que fue propuesta para resolver problemas multiobjetivos con el algoritmo NSGA II y que está basada en una distancia de apiñamiento (Crowding distance en inglés). En la parte distribuida, un modelo maestro-esclavo es implementado en CPU, GPU y en un servicio en la nube. Finalmente el algoritmo propuesto es probado utilizando funciones de prueba y su comportamiento es comparado con otros algoritmos multiobjetivo. Esta comparación muestra un nuevo algoritmo multiobjetivo competitivo.
dc.languagespa
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
dc.relationJin Hee Cho, Yating Wang, Ing Ray Chen, Kevin S. Chan, and Ananthram Swami. A Survey on Modeling and Optimizing Multi-Objective Systems.IEEE Communications Surveys and Tutorials, 19(3):1867–1901, 2017.
dc.relationDavid E. Goldberg.Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989.
dc.relationKalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A fast and el-tist multiobjective genetic algorithm: NSGA-II.IEEE Transactions on Evolutionary Computation, 6(2):182–197, apr 2002.
dc.relationEckart Zitzler, Marco Laumanns, and Lothar Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm.Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pages 95–100, 2001.
dc.relationYue-Jiao Gong, Wei-Neng Chen, Zhi-Hui Zhan, Jun Zhang, Yun Li, Qingfu Zhang, and Jing-Jing Li. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art.Applied Soft Computing, 34:286 – 300, 2015.
dc.relationJ. J. Durillo, A. J. Nebro, F. Luna, and E. Alba. A study of master-slave approaches to parallelize nsga-ii. In2008 IEEE International Symposium on Parallel and Distributed Processing, pages 1–8, April 2008.
dc.relationG. Ewald, W. Kurek, and M. A. Brdys. Grid implementation of a parallel multiobjective genetic algorithm for optimized allocation of chlorination stations in drinking water distribution systems: Chojnice case study. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(4):497–509, July 2008.
dc.relationJonatan Gomez. Self Adaptation of Operator Rates in Evolutionary Algorithms. In GECCO 2004, LNCS, volume 3102, page 12, 2004.
dc.relationW Stadler. A survey of multicriteria optimization or the vector maximum problem, part I: 1776-1960. Journal of Optimization Theory and Applications, 29(1):1–52, 1979.
dc.relationK Deb. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley and Sons Ltd, 2001.
dc.relationK M Miettinen. Nonlinear multiobjective optimization. Kluwer Academic Publishers, 1999.
dc.relationJ D Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the First International Conference on Genetic Algorithms, pages 93–100, 1985.
dc.relationM. Hyodo, T. Matsuo, and T. Ito. An optimal coalition formation among buyer agents based on a genetic algorithm. volume 2718, pages 759–767, 2003.
dc.relationS. Breban and J. Vassileva. Using inter-agent trust relationships for efficient coalition formation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2338:221–236, 2002.
dc.relationH. Huo, L. Yue, and X. Shen. Trust game analysis between partners in the supply chain alliance. pages 947–950, 2011
dc.relationP. Beautement. Agile and adaptive coalition operations - leveraging the power of complex environments.Proc. 11th Int. Command Control Res. Technol. Symp. (ICCRTS), 2006.
dc.relationM. Chang, J.H. Cho, I.R. Chen, K.S. Chan, and A. Swami. Trustbased task assignment in military tactical networks. Proc. 17th Int. Command Control Res. Technol. Symp. (ICCRTS), 2012.
dc.relationS.D. Ramchurn, M. Polukarov, A. Farinelli, C. Truong, and N.R. Jennings. Coalition formation with spatial and temporal constraints. volume 2, pages 1181–1188, 2010.
dc.relationA. Anagnostopoulos, L. Becchetti, C. Castillo, A. Gionis, and S. Leonardi. Online team formation in social networks. pages 839–848, 2012.
dc.relationC. Dorn, F. Skopik, D. Schall, and S. Dustdar. Interaction mining and skill-dependent recommendations for multi-objective team composition. Data and Knowledge Engineering, 70(10):866–891, 2011
dc.relationR. Subrata, A.Y. Zomaya, and B. Landfeldt. A cooperative game framework for qos guided job allocation schemes in grids. IEEE Transactions on Computers, 57(10):1413–1422, 2008
dc.relationE. Bedeer, O.A. Dobre, M.H. Ahmed, and K.E. Baddour. Rate-interference tradeoff in ofdm-based cognitive radio systems. IEEE Transactions on Vehicular Technology, 64(9):4292–4298, 2015
dc.relationS. Kartik and C.S. Ram Murthy. Task allocation algorithms for maximizing reliability of distributed computing systems. IEEE Transactions on Computers, 46(6):719–724, 1997.
dc.relationL. Jiao, J. Lit, W. Du, and X. Fu. Multi-objective data placement for multi-cloud socially aware services. pages 28–36, 2014
dc.relationG. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, and M. Protasi. Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties, 1999.
dc.relationM.R. Garey and D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness, 1979.
dc.relationV L Huang, A K Qin, K Deb, E Zitzler, P N Suganthan, J J Liang, M Preuss, and S Huband. Problem definitions for performance assessment on multi-objective optimization algorithms. Problem Definitions for Performance Assessment on Multi-objective Optimization Algorithms, 2007.
dc.relationS Huband, P Hingston, L Barone, and L While. A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on EvolutionaryComputation, 10(5):477–506, 2006.
dc.relationH Li and Q Zhang. Multiobjective optimization problems with complicated pareto sets, MOEA/ D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2):284–302, 2009.
dc.relationQ Zhang, A Zhou, S Zhao, P N Suganthan, W Liu, and S Tiwari. Multiobjective Optimization Test Instances for the CEC. Multiobjective Optimization Test Instances for the CEC, 2009.
dc.relationJ Knowles, L Thiele, and E Zitzler. A tutorial on the performance assessment of stochastic multiobjective optimizers. A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers, 2006.
dc.relationE Zitzler, L Thiele, M Laumanns, C M Fonseca, and V G Da Fonseca. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation, 7(2):117–132, 2003.
dc.relationHenrik Esbensen and Ernest S Kuh. Design space exploration using the genetic algorithm. In Proceedings - IEEE International Symposium on Circuits and Systems, volume 4, pages 500–503, 1996.
dc.relationP Czyzak and A Jaszkiewicz. Pareto simulated annealing - A metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7(1):34–47, 1998.
dc.relationE L Ulungu, J Teghem, P H Fortemps, and D Tuyttens. MOSA method: A tool for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Analysis, 8(4):221–236, 1999.
dc.relationD A Van Veldhuizen. Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations, 1999.
dc.relationJ R Schott. Fault tolerant design using single and multicriteria genetic algorithm optimization. Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization, 1995.
dc.relationE Zitzler. Evolutionary algorithms for multiobjective optimization: Methods and applications. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, 1999.
dc.relationS Sayin. Measuring the quality of discrete representations of efficient sets in multiple objective mathematical programming. Mathematical Programming, Series B, 87(3):543–560, 2000.
dc.relationJ Wu and S Azarm. Metrics for quality assessment of a multiobjective design optimization solution set. Journal of Mechanical Design, Transactions of the ASME, 123(1):18–25, 2001.
dc.relationW Khan Mashwani, A Salhi, O Yeniay, H Hussian, and M A Jan. Hybrid non-dominated sorting genetic algorithm with adaptive operators selection. Applied Soft Computing Journal, 56:1–18, 2017.
dc.relationH Seada and K Deb. U-NSGA-III: A unified evolutionary optimization procedure for single, multiple, and many objectives: Proof-of-principle results. 9019:34–49, 2015.
dc.relationEckart Zitzler and Lothar Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999.
dc.relationM Kim, T Hiroyasu, M Miki, and S Watanabe. SPEA2+: Improving the performance of the strength pareto evolutionary algorithm 2, 2004.
dc.relationR Storn and K Price. Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, TR-95-012, 1995.
dc.relationK V Price. An introduction to differential evolution. New Ideas in Optimization, pages 79–108, 1999.
dc.relationR Sarker and H A Abbass. Differential evolution for solving multiobjective optimization problems. Asia-Pacific Journal of Operational Research, 21(2):225–240, 2004.
dc.relationRussell Eberhart and James Kennedy. New optimizer using particle swarm theory. In Proceedings of the International Symposium on Micro Machine and Human Science, pages 39–43, 1995.
dc.relationJ Kennedy, R C Eberhart, and Y Shi. Swarm Intelligence. Morgan Kaufmann, 2001.
dc.relationJ Moore and R Chapman. Application of particle swarm to multiobjective optimization. Application of Particle Swarm to Multiobjective Optimization, 1999.
dc.relationS Janson, D Merkle, and M Middendorf. Molecular docking with multi-objective Particle Swarm Optimization. Applied Soft Computing Journal, 8(1):666–675, 2008.
dc.relationD S Liu, K C Tan, S Y Huang, C K Goh, and W K Ho. On solving multiobjective bin packing problems using evolutionary particle swarm optimization. European Journal of Operational Research, 190(2):357–382, 2008.
dc.relationWang Hu, Gary G. Yen, and Xin Zhang. Multiobjective particle swarm optimization based on Pareto entropy. Ruan Jian Xue Bao/Journal of Software, 25(5):1025–1050, 2014.
dc.relationC. Blum. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4):353–373, 2005
dc.relationM Dorigo and T Stützle. Ant Colony Optimization. Ant Colony Optimization, 2004.
dc.relationO. Dridi, S. Krichen, and A. Guitouni. A multi-objective optimization approach for resource assignment and task scheduling problem: Application to maritime domain awareness. 2012
dc.relationK Deb, M Mohan, and S Mishra. Evaluating the e-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evolutionary Computation, 13(4):501–525, 2005.
dc.relationK C Tan, Y J Yang, and C K Goh. A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Transactions on Evolutionary Computation,10(5):527–549, 2006.
dc.relationC.-K. Goh and K C Tan. A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, 13(1):103–127, 2009.
dc.relationC K Goh, K C Tan, D S Liu, and S C Chiam. A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. European Journal of Operational Research, 202(1):42–54, 2010.
dc.relationQ Zhang and H Li. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6):712–731, 2007.
dc.relationK Li, K Deb, Q Zhang, and S Kwong. An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Transactions on Evolutionary Computation, 19(5):694–716, 2015.
dc.relationQ Zhang, W Liu, and H Li. The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. Working Report CES-491, 2009.
dc.relationAlireza Askarzadeh. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 169(Supplement C):1 – 12, 2016.
dc.relationH Nobahari and A Bighashdel. MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective optimization. In 2nd Conference on Swarm Intelligence and Evolutionary Computation, CSIEC 2017 - Proceedings, pages 60–65, Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran, 2017.
dc.relationS F Adra and P J Fleming. Diversity management in evolutionary many-objective optimization. IEEE Transactions on Evolutionary Computation, 15(2):183–195, 2011.
dc.relationH.-L. Liu, L Chen, K Deb, and E D Goodman. Investigating the effect of imbalance between convergence and diversity in evolutionary multiobjective algorithms. IEEE Transactions on Evolutionary Computation, 21(3):408–425, 2017.
dc.relationMichael Kirley and Robert Stewart. Multiobjective evolutionary algorithms on complex networks. In Shigeru Obayashi, Kalyanmoy Deb, Carlo Poloni, Tomoyuki Hiroyasu, and Tadahiko Murata, editors, Evolutionary Multi-Criterion Optimization, pages 81–95, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg.
dc.relationChen-An Huang and Chia-Feng Juang. Evolutionary fuzzy control of two cooperative object-carrying wheeled robots for wall following through multiobjective continuous ACO. In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), pages 1–3. IEEE, jun 2017.
dc.relationV. Averchenkov, P. Kazakov, V. Kazakov, A. Reutov, and F. Lozbinev. Application of evolutionary algorithm to multiobjective optimization of hydraulic actuation system. In 2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS), pages 1–4. IEEE, dec 2015.
dc.relationKostas Georgoulakos, Kostas Vergidis, George Tsakalidis, and Nikolaos Samaras. Evolutionary Multi-Objective Optimization of business process designs with pre-processing. In 2017 IEEE Congress on Evolutionary Computation (CEC), pages 897–904. IEEE, jun 2017.
dc.relationNVIDIA. Cuda zone. https://developer.nvidia.com/cuda-zone, 2018.
dc.relationAmazon Web Services. Aws lambda. https://aws.amazon.com/lambda, 2018.
dc.relationAmazon Web Services. Amazon elasticache. https://aws.amazon.com/elasticache, 2018.
dc.relationK Deb and R. B. Agrawal. Simulated binary crossover for continuous search space. Complex Syst., 9:115–148, 04 1995.
dc.relationEva Ostertagova and Oskar Ostertag. Methodology and application of one-way anova. American Journal of Mechanical Engineering, 1:256–261, 11 2013.
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightsAcceso abierto
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.titleModelo de optimización multiobjetivo para el algoritmo evolutivo HAEA (Hybrid Adaptive Evolutionary Algorithm)
dc.typeOtro


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