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AN OPERANT CONDITIONING APPROACH FOR LARGE SCALE SOCIAL OPTIMIZATION ALGORITHMS

Year 2020, Volume: 8 , 38 - 45, 31.12.2020
https://doi.org/10.36306/konjes.821958

Abstract

The changes that positive or negative results cause in an individual's behavior are called Operant Conditioning. This paper introduces an operant conditioning approach (OCA) for large scale swarm optimization models. The proposed approach has been applied to social learning particle swarm optimization (SL-PSO), a variant of the PSO algorithm. In SL-PSO, the swarm particles are sorted according to the objective function and all particles are updated with learning from the others. In this study, each particle's learning rate is determined by the mathematical functions that are inspired by the operant conditioning. The proposed approach adjusts the learning rate for each particle. By using the learning rate, a particle close to the optimum solution is aimed to learn less. Thanks to the learning rate, a particle is prevented from being affected by particles close to the optimum point and particles far from the optimum point at the same rate. The proposed OCA-SL-PSO is compared with SL- PSO and pure PSO on CEC 13 functions. Also, the proposed OCA-SL-PSO is tested for large-scale optimization (100-D, 500-D, and 1000-D) benchmark functions. This paper has a novel contribution which is the usage of OCA on Social Optimization Algorithms. The results clearly indicate that the OCA is increasing the results of large-scale SL-PSO.

References

  • Aslan, S., Aksoy, A. and Gunay, M., 2018, Performance of parallel artificial bee colony algorithm on solving probabilistic sensor deployment problem, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 1-5.
  • Celtek, S. A., Durdu, A. and Alı, M. E. M., 2020, Real-time Traffic Signal Control with Swarm Optimization Methods, Measurement.
  • Cheng, R. and Jin, Y. J. I. S., 2015, A social learning particle swarm optimization algorithm for scalable optimization, 291, 43-60.
  • Clerc, M. and Kennedy, J., 2002, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6 (1), 58-73.
  • Cui, C.-Y. and Lee, H.-H., 2013, Distributed traffic signal control using PSO based on probability model for traffic jam, In: Intelligent Autonomous Systems 12, Eds: Springer, p. 629-639.
  • Eberhart, R. and Kennedy, J., 1995, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43.
  • Eberhart, R. C., Shi, Y. and Kennedy, J., 2001, Swarm intelligence, Elsevier, p.
  • Eldem, H. and Ülker, E., 2020, A Hierarchical Approach Based on ACO and PSO by Neighborhood Operators for TSPs Solution, International Journal of Pattern Recognition and Artificial Intelligence, 2059039.
  • Holland, J. G. and Skinner, B. F., 1961, The analysis of behavior: A program for self-instruction.
  • Karaboğa, D., 2014, Yapay Zeka Optimizasyon Algoritmalari, Nobel Akademik Yayıncılık, p.
  • Pham, D. and Karaboga, D., 2012, Intelligent optimisation techniques: genetic algorithms, tabu search, simulated annealing and neural networks, Springer Science & Business Media, p.
  • Premalatha, K. and Natarajan, A., 2009, Hybrid PSO and GA for global maximization, Int. J. Open Problems Compt. Math, 2 (4), 597-608.
  • Song, B., Wang, Z. and Zou, L., 2020, An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve, Applied Soft Computing, 106960.
  • Wang, H., Sun, H., Li, C., Rahnamayan, S. and Pan, J.-S., 2013, Diversity enhanced particle swarm optimization with neighborhood search, Information Sciences, 223, 119-135.
  • Wang, Z.-J., Zhan, Z.-H., Kwong, S., Jin, H. and Zhang, J., 2020, Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization, IEEE transactions on cybernetics.
  • Yalcin, N., Tezel, G. and Karakuzu, C., 2015, Epilepsy diagnosis using artificial neural network learned by PSO, Turkish Journal of Electrical Engineering & Computer Sciences, 23 (2), 421-432.
  • Zambrano-Bigiarini, M., Clerc, M. and Rojas, R., 2013, Standard particle swarm optimisation 2011 at cec-2013: A baseline for future pso improvements, 2013 IEEE Congress on Evolutionary Computation, 2337-2344.

Büyük Ölçekli Sosyal Optimizasyon Algoritmaları İçin Edimsel Koşullandırma Yaklaşımı

Year 2020, Volume: 8 , 38 - 45, 31.12.2020
https://doi.org/10.36306/konjes.821958

Abstract

Olumlu veya olumsuz sonuçların bir bireyin davranışında neden olduğu değişikliklere Edimsel Koşullandırma denir. Bu makale, büyük ölçekli sürü optimizasyon modelleri için bir edimsel koşullandırma yaklaşımı (OCA) sunar. Önerilen yaklaşım, PSO algoritmasının bir varyantı olan sosyal öğrenme parçacık sürüsü optimizasyonuna (SL-PSO) uygulanmıştır. SL-PSO'da sürü parçacıkları amaç işlevine göre sıralanır ve tüm parçacıklar diğerlerinden öğrenilerek güncellenir. Bu çalışmada, her parçacığın öğrenme hızı, edimsel koşullanmadan esinlenen matematiksel fonksiyonlar tarafından belirlenir. Önerilen yaklaşım, her parçacık için öğrenme oranını ayarlar. Öğrenme oranını kullanarak, optimum çözüme yakın bir parçacığın daha az öğrenmesi amaçlanmaktır. Öğrenme oranı sayesinde bir parçacığın çözüme yakın partikül ile çözüme uzak partiküllerden aynı oranda etkilenmesinin önüne geçilmektedir. Önerilen OCA-SL-PSO, CEC 13 işlevlerinde SL-PSO ve saf PSO ile karşılaştırılır. Ayrıca, önerilen OCA-SL-PSO, büyük ölçekli optimizasyon (100-D, 500-D ve 1000-D) karşılaştırma işlevleri için test edilmiştir. Bu yazının, Sosyal Optimizasyon Algoritmalarında OCA'nın kullanımı olan yeni bir katkısı vardır. Sonuçlar açıkça OCA'nın büyük ölçekli SL-PSO sonuçlarını artırdığını göstermektedir.

References

  • Aslan, S., Aksoy, A. and Gunay, M., 2018, Performance of parallel artificial bee colony algorithm on solving probabilistic sensor deployment problem, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 1-5.
  • Celtek, S. A., Durdu, A. and Alı, M. E. M., 2020, Real-time Traffic Signal Control with Swarm Optimization Methods, Measurement.
  • Cheng, R. and Jin, Y. J. I. S., 2015, A social learning particle swarm optimization algorithm for scalable optimization, 291, 43-60.
  • Clerc, M. and Kennedy, J., 2002, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6 (1), 58-73.
  • Cui, C.-Y. and Lee, H.-H., 2013, Distributed traffic signal control using PSO based on probability model for traffic jam, In: Intelligent Autonomous Systems 12, Eds: Springer, p. 629-639.
  • Eberhart, R. and Kennedy, J., 1995, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43.
  • Eberhart, R. C., Shi, Y. and Kennedy, J., 2001, Swarm intelligence, Elsevier, p.
  • Eldem, H. and Ülker, E., 2020, A Hierarchical Approach Based on ACO and PSO by Neighborhood Operators for TSPs Solution, International Journal of Pattern Recognition and Artificial Intelligence, 2059039.
  • Holland, J. G. and Skinner, B. F., 1961, The analysis of behavior: A program for self-instruction.
  • Karaboğa, D., 2014, Yapay Zeka Optimizasyon Algoritmalari, Nobel Akademik Yayıncılık, p.
  • Pham, D. and Karaboga, D., 2012, Intelligent optimisation techniques: genetic algorithms, tabu search, simulated annealing and neural networks, Springer Science & Business Media, p.
  • Premalatha, K. and Natarajan, A., 2009, Hybrid PSO and GA for global maximization, Int. J. Open Problems Compt. Math, 2 (4), 597-608.
  • Song, B., Wang, Z. and Zou, L., 2020, An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve, Applied Soft Computing, 106960.
  • Wang, H., Sun, H., Li, C., Rahnamayan, S. and Pan, J.-S., 2013, Diversity enhanced particle swarm optimization with neighborhood search, Information Sciences, 223, 119-135.
  • Wang, Z.-J., Zhan, Z.-H., Kwong, S., Jin, H. and Zhang, J., 2020, Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization, IEEE transactions on cybernetics.
  • Yalcin, N., Tezel, G. and Karakuzu, C., 2015, Epilepsy diagnosis using artificial neural network learned by PSO, Turkish Journal of Electrical Engineering & Computer Sciences, 23 (2), 421-432.
  • Zambrano-Bigiarini, M., Clerc, M. and Rojas, R., 2013, Standard particle swarm optimisation 2011 at cec-2013: A baseline for future pso improvements, 2013 IEEE Congress on Evolutionary Computation, 2337-2344.
There are 17 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Seyit Alperen Çeltek 0000-0002-7097-2521

Akif Durdu 0000-0002-5611-2322

Publication Date December 31, 2020
Submission Date November 5, 2020
Acceptance Date December 15, 2020
Published in Issue Year 2020 Volume: 8

Cite

IEEE S. A. Çeltek and A. Durdu, “AN OPERANT CONDITIONING APPROACH FOR LARGE SCALE SOCIAL OPTIMIZATION ALGORITHMS”, KONJES, vol. 8, pp. 38–45, 2020, doi: 10.36306/konjes.821958.