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Multi-population-based algorithm with an exchange of training plans based on population evaluation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.
Rocznik
Strony
239--253
Opis fizyczny
Bibliogr. 37 poz., rys.
Twórcy
  • Częstochowa University of Technology, Department of Intelligent Computer Systems 42-200 Częstochowa, Poland
  • Częstochowa University of Technology, Department of Intelligent Computer Systems 42-200 Częstochowa, Poland
  • AGH University of Science and Technology, Institute of Computer Science 30-059 Kraków, Poland
  • Institute of Information Technologies, University of Social Sciences, ul. Sienkiewicza 9, 90-113 Łódź
  • University of Social Science, Management Department 90-113 Łódź, Poland
autor
  • Tan Trao University, Vietnam
Bibliografia
  • [1] Ł. Bartczuk, A. Przybył, K. Cpałka, A new approach to nonlinear modelling of dynamic systems based on fuzzy rules, International Journal of Applied Mathematics and Computer Science (AMCS), 26(3), 603-621, 2016.
  • [2] J. Bilski, B. Kowalczyk, A. Marchlewska, J.M. Zurada, Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks, Journal of Artificial Intelligence and Soft Computing Research, 10(4), 299-316, 2020, ttps://doi.org/10.2478/jaiscr-2020-0020.
  • [3] R. Chen, B. Yang, S. Li, S. Wang, Q. Cheng, An Effective Multi-population Grey Wolf Optimizer based on Reinforcement Learning for Flow Shop Scheduling Problem with Multi-machine Collaboration, Computers & Industrial Engineering, 162, 2021, https://doi.org/10.1016/j.cie.2021.107738.
  • [4] P. Duda, M. Jaworski, A. Cader, L. Wang, On Training Deep Neural Networks Using a Streaming Approach, Journal of Artificial Intelligence and Soft Computing Research, 10(1), 15-26, 2020, https://doi.org/10.2478/jaiscr-2020-0002.
  • [5] K. Cpałka, K. Łapa, L. Rutkowski, A multipopulation-based algorithm with different ways of subpopulations cooperation, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, Springer, 2022 (in print).
  • [6] P. Dziwinski, Ł. Bartczuk, J. Paszkowski, A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 10(2), 95-111, 2020,https://doi.org/10.2478/jaiscr-2020-0007.
  • [7] P. Dziwinski, P. Przybył, P. Trippner, J. Paszkowski, Y. Hayashi, Hardware Implementation of a Takagi-Sugeno Neuro-Fuzzy System Optimized by a Population Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 11(3), 243-266, 2021, https://doi.org/10.2478/jaiscr-2021-0015.
  • [8] L. Fu, H. Ouyang, C. Zhang, S. Li, A.W. Mohamed, A constrained cooperative adaptive multi-population differential evolutionary algorithm for economic load dispatch problems, Applied Soft Computing, 121, 2022, https://doi.org/10.1016/j.asoc.2022.108719.
  • [9] F. Kılıç, Y. Kaya, S. Yildirim, A novel multi population based particle swarm optimization for feature selection, Knowledge-Based Systems, 219, 2021, https://doi.org/10.1016/j.knosys.2021.106894.
  • [10] M. Korytkowski, R. Senkerik, M.M. Scherer, R.A. Angryk, M. Kordos, A. Siwocha, Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic, Journal of Artificial Intelligence and Soft Computing Research, 10(1), 57-69, 2020, https://doi.org/10.2478/jaiscr-2020-0005.
  • [11] K. Łapa, K. Cpałka, Flexible fuzzy PID controller (FFPIDC) and a nature-inspired method for its construction, IEEE Trans. on Industrial Informatics, 14(3), 1078-1088, 2018.
  • [12] K. Łapa, K. Cpałka, Ł. Laskowski, A. Cader, Z. Zeng, Evolutionary Algorithm with a Configurable Search Mechanism, Journal of Artificial Intelligence and Soft Computing Research, 10(3), 151-171, 2020.
  • [13] K. Łapa, K. Cpałka, M. Zalasinski, Algorithm Based on Population with a Flexible Search Mechanism, IEEE Access, 7, 132253-132270, 2019.
  • [14] G. Li, W. Wang, W. Zhang, Z. Wang, H. Tu, W. You, Grid search based multi-population 252 Krystian Łapa, Krzysztof Cpałka, Marek Kisiel-Dorohinicki, Józef Paszkowski, Maciej D˛ebski, Van-Hung Le particle swarm optimization algorithm for multimodal multi-objective optimization, Swarm and Evolutionary Computation, 62, 2021, https://doi.org/10.1016/j.swevo.2021.100843.
  • [15] F. Ming, W. Gong, L. Wang, C. Lu, A tri-population based co-evolutionary framework for constrained multi-objective optimization problems, Swarm and Evolutionary Computation, 70, 2022, https://doi.org/10.1016/j.swevo.2022.101055.
  • [16] T. Niksa-Rynkiewicz, N. Szewczuk-Krypa, A. Witkowska, K. Cpałka, M. Zalasinski, A. Cader, Monitoring Regenerative Heat Exchanger in Steam Power Plant by Making Use of the Recurrent Neural Network, Journal of Artificial Intelligence and Soft Computing Research, 11(2), 143-155, 2021, https://doi.org/10.2478/jaiscr-2021-0009.
  • [17] L.R. Rodrigues, A hybrid multi-population metaheuristic applied to load-sharing optimization of gas compressor stations, Computers & Electrical Engineering, 97, 2022, https://doi.org/10.1016/j.compeleceng.2021.107632.
  • [18] A.K. Saha, Multi-population-based adaptive sine cosine algorithm with modified mutualism strategy or global optimization, Knowledge-Based Systems, 2022.
  • [19] A. Słowik, K. Cpałka, Guest Editorial: Hybrid Approaches to Nature-Inspired PopulationBased Intelligent Optimization for Industrial Applications, IEEE Transactions on Industrial Informatics, 18(1), 542-545, 2022, DOI (identifier) 10.1109/TII.2021.3091137.
  • [20] A. Słowik, K. Cpałka, Hybrid Approaches to Nature-inspired Population-based Intelligent Optimization for Industrial Applications, IEEE Transactions on Industrial Informatics, 18(1), 546-558, 2022, DOI (identifier) 10.1109/TII.2021.3067719.
  • [21] A. Słowik, K. Cpałka, K. Łapa, MultiPopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems, IEEE Transactions on Fuzzy Systems, 28(6), 1125-1139, 2020, DOI (identifier) 10.1109/TFUZZ.2019.2959997.
  • [22] Y. Song, D. Wu, W. Deng, X.Z. Gao, T. Li, B. Zhang, Y. Li, MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization, Energy Conversion and Management, 228, 2021, https://doi.org/10.1016/j.enconman.2020.113661.
  • [23] P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.P. Chen, A. Auger, S. Tiwari, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, KanGAL report, 2005.
  • [24] Y. Sun, Y. Chen, Multi-population improved whale optimization algorithm for high dimensional optimization, Applied Soft Computing, 112, 2021, https://doi.org/10.1016/j.asoc.2021.107854.
  • [25] J. Szczypta, A. Przybył, K. Cpałka, Some aspects of evolutionary designing optimal controllers, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 7895, Springer, 91-100, 2013.
  • [26] R. Tanabe, A. Fukunaga, Evaluating the performance of SHADE on CEC 2013 benchmark problems. In 2013 IEEE Congress on evolutionary computation, pp. 1952-1959, IEEE, 2013.
  • [27] V. Thanasis, B.S. Efthimia, K. Dimitris, Estimation of linear trend onset in time series, Simulation Modelling Practice and Theory, 19(5), 1384-1398, 2011, https://doi.org/10.1016/j.simpat.2011.02.006.
  • [28] B. Yang, S. Wang, Q. Cheng, T. Jin, Scheduling of field service resources in cloud manufacturing based on multi-population competitive-cooperative GWO, Computers & Industrial Engineering, 154, 2021, https://doi.org/10.1016/j.cie.2021.107104.
  • [29] M. Zalasinski, K. Cpałka, A new method of on-line signature verification using a flexible fuzzy oneclass classifier, Academic Publishing House EXIT, 38-53, 2011.
  • [30] M. Zalasinski, K. Cpałka, Novel algorithm for the on-line signature verification using selected discretization points groups, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 7894, Springer, 493-502, 2013.
  • [31] M. Zalasinski, K. Cpałka, Y. Hayashi, New method for dynamic signature verification based on global features, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 8467, Springer, 251-265, 2014.
  • [32] M. Zalasinski, K. Cpałka, Y. Hayashi, New fast algorithm for the dynamic signature verification using global features values, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 9120, Springer, 175-188, 2015.
  • [33] M. Zalasinski, K. Cpałka, Ł. Laskowski, D.C. Wunsch, K. Przybyszewski, An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors, Journal of Artificial Intelligence and Soft Computing Research, 10(3), 173-187, 2020, https://doi.org/10.2478/jaiscr-2020-0012.
  • [34] M. Zalasinski, K. Łapa, K. Cpałka, New algorithm for evolutionary selection of the dynamic signature global features, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 7895, Springer, 113-121, 2013.
  • [35] M. Zalasinski, K. Łapa, K. Cpałka, K. Przybyszewski, G.G. Yen, On-Line Signature Partitioning Using a Population Based Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 10(1), 5-13, 2020, https://doi.org/10.2478/jaiscr-2020-0001.
  • [36] X. Zhang, S. Wen, D. Wang, Multi-population biogeography-based optimization algorithm and its application to image segmentation, Applied Soft Computing, 124, 2022, https://doi.org/10.1016/j.asoc.2022.109005.
  • [37] F. Zhao, G. Zhou, L. Wang, T. Xu, N. Zhu, Jonrinaldi, A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism, Expert Systems with Applications, 203, 2022, https://doi.org/10.1016/j.eswa.2022.117444.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2de0f99a-338a-4f1e-8389-899e787a1683
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