Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing

• Autorzy: Valdez Fevrier , Kawano Yunkio , Melin Patricia

Identyfikatory

DOI

10.14313/JAMRIS/1-2020/7

• Języki publikacji: EN

Abstrakty

EN

In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.

Słowa kluczowe

EN

genetic algorithms; particle swarm optimization (PSO); fuzzy logic; parallel processing

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2020
• Tom: Vol. 14, No. 1
• Strony: 55--64
• Opis fizyczny: Bibliogr. 32 poz., rys.

Twórcy

autor

Valdez Fevrier

• Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

autor

Kawano Yunkio

• Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

autor

Melin Patricia

• Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

Bibliografia

• [1] Y. Kawano, F. Valdez and O. Castillo, “Performance Evaluation of Optimization Algorithms based on GPU using CUDA Architecture”. In: 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2018, 1–6, DOI: 10.1109/LA-CCI.2018.8625236.
• [2] G. R. Harik, F. G. Lobo and D. E. Goldberg, “The compact genetic algorithm”, IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, 1999, 287–297, DOI: 10.1109/4235.797971.
• [3] X. H. Shi, Y. H. Lu, C. G. Zhou, H. P. Lee, W. Z. Lin and Y. C. Liang, “Hybrid evolutionary algorithms based on PSO and GA”. In: The 2003 Congress on Evolutionary Computation, 2003. CEC ‘03, vol. 4, 2003, 2393–2399, DOI: 10.1109/CEC.2003.1299387.
• [4] S. Debattisti, N. Marlat, L. Mussi, S. Cagnoni, “Implementation of a Simple Genetic Algorithm within the CUDA Architecture”, GPUs for Genetic and Evolutionary Computation Competition at 2009 Genetic and Evolutionary Computation Conference, 2009.
• [5] L. Mussi and S. Cagnoni, “Particle swarm optimization within the CUDA architecture”, 2009.
• [6] J. C. Vazquez and F. Valdez, “Fuzzy logic for dynamic adaptation in PSO with multiple topologies”. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013, 1197–1202, DOI: 10.1109/IFSA-NAFIPS.2013.6608571.
• [7] F. Olivas, F. Valdez and O. Castillo, “Fuzzy Classification System Design Using PSO with Dynamic Parameter Adaptation Through Fuzzy Logic”. In: O. Castillo and P. Melin (eds.), Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics: Theory and Applications, 2015, 29–47, DOI: 10.1007/978-3-319-10960-2_2.
• [8] F. Valdez, P. Melin and O. Castillo, “Fuzzy control of parameters to dynamically adapt the PSO and GA Algorithms”. In: International Conference on Fuzzy Systems, 2010, 1–8, DOI: 10.1109/FUZZY.2010.5583934.
• [9] F. Valdez, P. Melin and O. Castillo, “Fuzzy Logic for Combining Particle Swarm Optimization and Genetic Algorithms: Preliminary Results”. In: A. H. Aguirre, R. M. Borja and C. A. R. Garciá eds.), MICAI 2009: Advances in Artificial Intelligence, 2009, 444–453, DOI: 10.1007/978-3-642-05258-3_39.
• [10] J. Carnahan and R. Sinha, “Nature’s algorithms [genetic algorithms]”, IEEE Potentials, vol. 20, no. 2, 2001, 21–24, DOI: 10.1109/45.954644.
• [11] Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources”. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, 2001, 81–86, DOI: 10.1109/CEC.2001.934374.
• [12] F. Olivas, F. Valdez and O. Castillo, “Particle swarm optimization with dynamic parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions”. In: 2013 World Congress on Nature and Biologically Inspired Computing, 2013, 36–40, DOI: 10.1109/NaBIC.2013.6617875.
• [13] J. Kennedy and R. Eberhart, “Particle swarm optimization”. In: Proceedings of ICNN’95 – International Conference on Neural Networks, vol. 4, 1995, 1942–1948, DOI: 10.1109/ICNN.1995.488968.
• [14] R. Poli, J. Kennedy and T. Blackwell, “Particle swarm optimization”, Swarm Intelligence, vol. 1, no. 1, 2007, 33–57, DOI: 10.1007/s11721-007-0002-0.
• [15] F. Olivas, L. Amador-Angulo, J. Perez, C. Caraveo, F. Valdez and O. Castillo, “Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers”, Algorithms, vol. 10, no. 3, 2017, DOI: 10.3390/a10030101.
• [16] F. Valdez, P. Melin and O. Castillo, “Parallel Particle Swarm Optimization with Parameters Adaptation Using Fuzzy Logic”. In: I. Batyrshin and M. G. Mendoza (eds.), Advances in Computational Intelligence, 2013, 374–385, DOI: 10.1007/978-3-642-37798-3_33.
• [17] Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization”. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), vol. 1, 2001, 101–106, DOI: 10.1109/CEC.2001.934377.
• [18] J. Kaur, S. Singh and S. Singh, “Parallel Implementation of PSO Algorithm Using GPGPU”. In: 2016 Second International Conference on Computational Intelligence Communication Technology (CICT), 2016, 155–159, DOI: 10.1109/CICT.2016.38.
• [19] F. Valdez, P. Melin and O. Castillo, “An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms”, Applied Soft Computing, vol. 11, no. 2, 2011, 2625–2632, DOI: 10.1016/j.asoc.2010.10.010.
• [20] A. S. Radhamani and E. Baburaj, “Performance evaluation of parallel genetic and particle swarm optimization algorithms within the multicore architecture”, International Journal of Computational Intelligence and Applications, vol. 13, no. 4, 2014, DOI: 10.1142/S1469026814500242.
• [21] Z.-X. Wang and G. Ju, “A parallel genetic algorithm in multi-objective optimization”. In: 2009 Chinese Control and Decision Conference, 2009, 3497–3501, DOI: 10.1109/CCDC.2009.5192490.
• [22] Z. Dingxue, G. Zhihong and L. Xinzhi, “On Multipopulation Parallel Particle Swarm Optimization Algorithm”. In: 2007 Chinese Control Conference, 2007, 763–765, DOI: 10.1109/CHICC.2006.4347299.
• [23] X. Lai and G. Tan, “Studies on migration strategies of multiple population parallel particle swarm optimization”. In: 2012 8th International Conference on Natural Computation, 2012, 798–802, 10.1109/ICNC.2012.6234614.
• [24] H. Pohlheim, “Genetic and Evolutionary Algorithm Toolbox for Matlab”. In: Evolutionäre Algorithmen, 2000, 157–170, DOI: 10.1007/978-3-642-57137-4_6.
• [25] J. G. Digalakis and K. G. Margaritis, “An Experimental Study of Benchmarking Functions for Genetic Algorithms,” International Journal of Computer Mathematics, vol. 79, no. 4, 403–416, 2002, DOI: 10.1080/00207160210939.
• [26] “GEATbx: Example Functions (single and multiobjective functions) 2 Parametric Optimization”. H. Pohlheim, http://www.geatbx.com/docu/fcnindex-01.html. Accessed on: 2020-05-28.
• [27] E. Bernal, O. Castillo, J. Soria and F. Valdez, “Interval Type-2 fuzzy logic for dynamic parameter adjustment in the imperialist competitive algorithm”. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019, 1–5, DOI: 10.1109/FUZZ-IEEE.2019.8858935.
• [28] J. R. Castro, O. Castillo and P. Melin, “An Interval Type-2 Fuzzy Logic Toolbox for Control Applications”. In: 2007 IEEE International Fuzzy Systems Conference, 2007, 1–6, DOI: 10.1109/FUZZY.2007.4295341.
• [29] R. Martinez, A. Rodriguez, O. Castillo and L. T. Aguilar, “Type-2 Fuzzy Logic Controllers Optimization Using Genetic Algoritms and Particle Swarm Optimization”. In: 2010 IEEE International Conference on Granular Computing, 2010, 724–727, DOI: 10.1109/GrC.2010.43.
• [30] N. C. Long and P. Meesad, “Meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction”. In: 2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA), 2013, 69–74, DOI: 10.1109/IWCIA.2013.6624787.
• [31] P. Melin, J. Urias, D. Solano, M. Soto, M. Lopez and O. Castillo, “Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms”, Engineering Letters, vol. 13, no. 2, 2006.
• [32] F. Gaxiola, P. Melin, F. Valdez, J. R. Castro and O. Castillo, “Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO”, Applied Soft Computing, vol. 38, 2016, 860–871, DOI: 10.1016/j.asoc.2015.10.027.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).