Araştırma Makalesi
BibTex RIS Kaynak Göster

On Particle Swarm Optimization Variants for Solution of Some Objective Functions

Yıl 2023, Cilt: 1 Sayı: 1, 25 - 37, 23.06.2023

Öz

Particle Swarm Optimization (PSO) is a widely used metaheuristic algorithm in the field of optimization. Over the years, several variants of PSO have been proposed to improve its performance and overcome its limitations. This study focuses on the comparison of the performance of different PSO variants by solving benchmark functions. We have selected five PSO variants, including constant inertia weight PSO, random inertia weight PSO, time-varying inertia weight PSO, inertia weight-free PSO, nonlinear inertia weight PSO and adaptive inertia weight PSO. These variants have been implemented in MATLAB and tested on some benchmark functions. The results of the experiments show that the performance of the PSO variants changes significantly depending on the benchmark function. However, overall, the adaptive inertia weight PSO variant has shown superior performance compared to the other variants. This variant is capable of finding the global optimum solution with higher accuracy and in a shorter time compared to the other variants.

Kaynakça

  • Blum, C., Roli, A., and Sampels, M. (Eds.). (2008). Hybrid metaheuristics: an emerging approach to optimization (Vol. 114). Springer.
  • Das, R. R., Elumalai, V. K., Subramanian, R. G., and Kumar, K. V. A. (2018). Adaptive predator–prey optimization for tuning of infinite horizon LQR applied to vehicle suspension system. Applied Soft Computing, 72, 518-526.
  • David Reddipogu, J. S., and Elumalai, V. K. (2020). Hardware in the loop testing of adaptive inertia weight PSO-tuned LQR applied to vehicle suspension control. Journal of Control Science and Engineering, 2020, 1-16.
  • Eberhart, R. C., and Shi, Y. (2001, May). Tracking and optimizing dynamic systems with particle swarms. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 94-100). IEEE.
  • Fan, S. K. S., and Chiu, Y. Y. (2007). A decreasing inertia weight particle swarm optimizer. Engineering Optimization, 39(2), 203-228.
  • Gogna, A., and Tayal, A. (2013). Metaheuristics: review and application. Journal of Experimental & Theoretical Artificial Intelligence, 25(4), 503-526.
  • Imran, M., Hashim, R., and Abd Khalid, N. E. (2013). An overview of particle swarm optimization variants. Procedia Engineering, 53, 491-496.
  • Jaberipour, M., Khorram, E., and Karimi, B. (2011). Particle swarm algorithm for solving systems of nonlinear equations. Computers & Mathematics with Applications, 62(2), 566-576.
  • Kennedy, J., and Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
  • Osman, I. H., and Laporte, G. (1996). Metaheuristics: A bibliography. Annals of Operations research, 63, 511-623.
  • Reynolds, C. W. (1987, August). Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques (pp. 25-34).
  • Rueda, J. L., and Erlich, I. (2015, May). MVMO for bound constrained single-objective computationally expensive numerical optimization. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 1011-1017). IEEE.
  • Shi, Y., and Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In Evolutionary Programming VII: 7th International Conference, EP98 San Diego, California, USA, March 25–27, 1998 Proceedings 7 (pp. 591-600). Springer Berlin Heidelberg.
  • Shi, Y., and Eberhart, R. C. (1999, July). Empirical study of particle swarm optimization. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 3, pp. 1945-1950). IEEE.
  • Tanweer, M. R., Suresh, S., and Sundararajan, N. (2015, May). Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems. In 2015 IEEE congress on evolutionary computation (CEC) (pp. 1943-1949). IEEE.
  • Wilson, E. O. (2000). Sociobiology: The new synthesis. Harvard University Press.
  • Zhang, W. (2015). Selforganizology: The Science of Self-Organization. World Scientific.
  • Zhang, W. (2022). Particle swarm optimization: A Matlab algorithm. Selforganizology, 9(3-4), 35-41.

Bazı Amaç Fonksiyonların Çözümü için Parçacık Sürü Optimizasyon Varyantları Üzerine bir Çalışma

Yıl 2023, Cilt: 1 Sayı: 1, 25 - 37, 23.06.2023

Öz

Parçacık Sürü Optimizasyonu (PSO), optimizasyon alanında yaygın olarak kullanılan metasezgisel bir algoritmadır. Yıllar boyunca, performansını iyileştirmek ve sınırlamalarının üstesinden gelmek için çeşitli PSO varyantları önerilmiştir. Bu çalışma, kıyaslama fonksiyonlarını çözerek farklı PSO varyasyonlarının performansını karşılaştırılmasına odaklanmaktadır. Sabit atalet ağırlıklı PSO, rastgele atalet ağırlıklı PSO, zamanla değişen atalet ağırlıklı PSO, atalet ağırlıksız PSO, doğrusal olmayan atalet ağırlıklı PSO ve uyarlanabilir atalet ağırlıklı PSO dâhil olmak üzere PSO varyantları seçilmiştir. Bu varyasyonlar MATLAB'da gerçekleştirilmiş ve kıyaslama fonksiyonlarında test edilmiştir. Deneylerin sonuçları, PSO varyantlarının performansının kıyaslama fonksiyonuna bağlı olarak önemli ölçüde değiştiğini göstermektedir. Bununla birlikte, genel olarak, uyarlanabilir atalet ağırlıklı PSO varyantı, diğer varyantlara kıyasla üstün performans göstermiştir. Bu varyant, global optimum çözümü diğer varyantlara göre daha yüksek doğrulukta ve daha kısa sürede bulabilmektedir.

Kaynakça

  • Blum, C., Roli, A., and Sampels, M. (Eds.). (2008). Hybrid metaheuristics: an emerging approach to optimization (Vol. 114). Springer.
  • Das, R. R., Elumalai, V. K., Subramanian, R. G., and Kumar, K. V. A. (2018). Adaptive predator–prey optimization for tuning of infinite horizon LQR applied to vehicle suspension system. Applied Soft Computing, 72, 518-526.
  • David Reddipogu, J. S., and Elumalai, V. K. (2020). Hardware in the loop testing of adaptive inertia weight PSO-tuned LQR applied to vehicle suspension control. Journal of Control Science and Engineering, 2020, 1-16.
  • Eberhart, R. C., and Shi, Y. (2001, May). Tracking and optimizing dynamic systems with particle swarms. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 94-100). IEEE.
  • Fan, S. K. S., and Chiu, Y. Y. (2007). A decreasing inertia weight particle swarm optimizer. Engineering Optimization, 39(2), 203-228.
  • Gogna, A., and Tayal, A. (2013). Metaheuristics: review and application. Journal of Experimental & Theoretical Artificial Intelligence, 25(4), 503-526.
  • Imran, M., Hashim, R., and Abd Khalid, N. E. (2013). An overview of particle swarm optimization variants. Procedia Engineering, 53, 491-496.
  • Jaberipour, M., Khorram, E., and Karimi, B. (2011). Particle swarm algorithm for solving systems of nonlinear equations. Computers & Mathematics with Applications, 62(2), 566-576.
  • Kennedy, J., and Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
  • Osman, I. H., and Laporte, G. (1996). Metaheuristics: A bibliography. Annals of Operations research, 63, 511-623.
  • Reynolds, C. W. (1987, August). Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques (pp. 25-34).
  • Rueda, J. L., and Erlich, I. (2015, May). MVMO for bound constrained single-objective computationally expensive numerical optimization. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 1011-1017). IEEE.
  • Shi, Y., and Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In Evolutionary Programming VII: 7th International Conference, EP98 San Diego, California, USA, March 25–27, 1998 Proceedings 7 (pp. 591-600). Springer Berlin Heidelberg.
  • Shi, Y., and Eberhart, R. C. (1999, July). Empirical study of particle swarm optimization. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 3, pp. 1945-1950). IEEE.
  • Tanweer, M. R., Suresh, S., and Sundararajan, N. (2015, May). Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems. In 2015 IEEE congress on evolutionary computation (CEC) (pp. 1943-1949). IEEE.
  • Wilson, E. O. (2000). Sociobiology: The new synthesis. Harvard University Press.
  • Zhang, W. (2015). Selforganizology: The Science of Self-Organization. World Scientific.
  • Zhang, W. (2022). Particle swarm optimization: A Matlab algorithm. Selforganizology, 9(3-4), 35-41.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik, Elektrik Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Hasan Başak

Kadri Doğan 0000-0002-6622-3122

Yayımlanma Tarihi 23 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 1 Sayı: 1

Kaynak Göster

APA Başak, H., & Doğan, K. (2023). On Particle Swarm Optimization Variants for Solution of Some Objective Functions. Artvin Çoruh Üniversitesi Mühendislik Ve Fen Bilimleri Dergisi, 1(1), 25-37.