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Spider Wasp Optimization Algorithm

Yıl 2025, Cilt: 9 Sayı: 1, 42 - 67, 30.06.2025

Osman Karataş , Celal Yaşar , Hasan Temurtaş , Serdar Özyön

https://doi.org/10.47897/bilmes.1659488

Öz

This study aims to improve the performance of the Spider Wasp Optimization (SWO) algorithm, a swarm intelligence algorithm recently introduced in the literature, on various test functions with fixed and variable dimensions. Optimization can be defined as making a system as efficient as possible with minimal cost within certain constraints. Numerous optimization algorithms have been designed in the literature and used to obtain the best solutions for specific problems. The most critical aspects in solving these problems include correctly modeling the problem, determining the problem’s parameters and constraints, and finally selecting an appropriate meta-heuristic algorithm to solve the objective function. Not every algorithm is suitable for every problem structure. Some algorithms perform better on fixed-dimension test functions, while others in solving variable-dimension test functions. In this study, the performance of the SWO algorithm was evaluated on 10 test functions previously used in the literature, consisting of three fixed-dimension functions (Schaffer, Himmelblau and Kowalik Functions) and seven variable-dimension functions, including one unimodal function (Elliptic Function) and six multimodal functions (Non-Continuous Rastrigin, Alpine, Levy, Weierstrass, Michalewicz, and Dixon & Price Functions). The solution values obtained for each of the selected functions were compared with the solutions obtained using the Harris Hawks Optimizer (HHO), the Charged System Search (CSS), and the Backtracking Search Optimization Algorithm (BSA).

Anahtar Kelimeler

Spider Wasp Optimization Harris Hawks Optimizer Charged System Search Algorithm Backtracking Search Optimization Fixed and variable size unimodal and multimodal test functions

Kaynakça

  • F. Cantaş, S. Özyön, and C. Yaşar, “Runge Kutta Optimization for Fixed Size Multimodal Test Functions,” International Scientific and Vocational Studies Journal, vol. 6, no. 2, pp. 144-155, 2022. DOI: 10.47897/bilmes.1219033.
  • S. M. Öztürk and A. Çifci, “A Study in Enhancing Battery Management Systems for Diverse Battery Types,” International Scientific and Vocational Studies Journal, vol. 7, no. 2, pp. 122-136, 2023. DOI: 10.47897/bilmes.1385510.
  • T. Aktaş, İ. M. Temel, and A. Saygılı, “Comparative Analysis of Diabetes Diagnosis with Machine Learning Methods,” International Scientific and Vocational Studies Journal, vol. 8, no. 1, pp. 22-32, 2024. DOI: 10.47897/bilmes.1447878.
  • A. İ. Çanakoğlu, “Monte Carlo Increased-Radius Floating Random Walk Solution For Potential Problems,” International Scientific and Vocational Studies Journal, vol. 8, no. 1, pp. 13-21, 2024. DOI: 10.47897/bilmes.1441414.
  • K. Gencer, G. Gencer, and İ. H. Cizmeci, “Deep Learning Approaches for Retinal Image Classification: A Comparative Study of GoogLeNet and ResNet Architectures,” International Scientific and Vocational Studies Journal, vol. 8, no. 2, pp. 123-128, 2024. DOI: 10.47897/bilmes.1523768.
  • U. Saray and U. Çavdar, “Comparison of Different Optimization Algorithms in the Fashion MNIST Dataset,” International Journal of Multidisciplinary Studies and Innovative Technologies (IJMSIT), vol. 8, no. 2, pp. 52-58, 2024.
  • M. D. Demirbaş and D. Çakır (Sofuoğlu), “Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem,” International Scientific and Vocational Studies Journal (ISVOS), vol. 6, no. 2, pp. 103-115, 2022. DOI: 10.47897/bilmes.1207256.
  • M. Lüy and N. A. Metin, “PID Control Medium Size Wind Turbine Control with Integrated Blade Pitch Angle,” International Scientific and Vocational Studies Journal (ISVOS), vol. 6, no. 1, pp. 22-31, 2022. DOI: 10.47897/bilmes.1091968.
  • B. Durmuş, “Opposite Based Crow Search Algorithm for Solving Optimization Problems,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 2, pp. 164-170, 2021. DOI: 10.47897/bilmes.1031011.
  • Ö. Yavuz, “An Optimization Focused Machine Learning Approach in Analysing Arts Participative Behavior with Fine Arts Education Considerations,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 2, pp. 241-253, 2021. DOI: 10.47897/bilmes.1029139.
  • M. D. Demirbaş, M. Oğuz, and İ. Erişen, “Investigation of Buckling Behavior of Beams with Artificial Neural Network,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 1, pp. 94-106, 2021.
  • E. Dağdevir and M. Tokmakçı, “The Role of Feature Selection in Significant Information Extraction from EEG Signals,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 1, pp. 1-6, 2021.
  • B. Alızada, “Improved Whale Optimization Algorithm Based On π Number,” International Scientific and Vocational Studies Journal (ISVOS), vol. 4, no. 1, pp. 21-30, 2020.
  • E. Göçmen and O. Derse, “A Tabu Search Algorithm for an Excavator Scheduling Problem,” International Scientific and Vocational Studies Journal (ISVOS), vol. 4, no. 1, pp. 1-11, 2020.
  • M. Taşova, “Effect on the Effective Diffusion and Activation Energy Values of Pea (Pisum sativum L.) Grains of Drying Temperature,” International Scientific and Vocational Studies Journal (ISVOS), vol. 3, no. 1, pp. 8-13, 2019.
  • B. Öztürk, L. Uğur, F. Erzincanlı, and Ö. Küçük, “Optimization of Polyethylene Inserts Design Geometry of Total Knee Prosthesis,” International Scientific and Vocational Studies Journal (ISVOS), vol. 2, no. 2, pp. 31-39, 2018.
  • D. Çakır and M. D. Demirbaş, “Modelling of One-directional Functionally Graded Circular Plates with Artificial Neural Network,” International Scientific and Vocational Studies Journal (ISVOS), vol. 3, no. 1, pp. 42-50, 2019.
  • M. Abdel-Basset, R. Mohamed, M. Jameel, and M. Abouhawwash, “Spider wasp optimizer: a novel meta-heuristic optimization algorithm,” Artificial Intelligence Review, vol. 56, no. 10, pp. 11675-11738, 2023..
  • A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems, vol. 97, pp. 849-872, 2019.
  • A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search,” Acta Mechanica, vol. 213, no. 3, pp. 267-289, 2010.
  • P. Civicioglu, “Backtracking search optimization algorithm for numerical optimization problems,” Applied Mathematics and Computation, vol. 219, no. 15, pp. 8121-8144, 2013.
  • J. Sui, Z. Tian, and Z. Wang, “Multiple strategies improved spider wasp optimization for engineering optimization problem solving,” Scientific Reports, vol. 14, no. 1, p. 29048, 2024. DOI: 10.1038/s41598-024-78589-8.
  • İ. Çetinbaş, “Parameter extraction of single, double, and triple-diode photovoltaic models using the weighted leader search algorithm,” Global Challenges (Hoboken, NJ), vol. 8, no. 5, p. 2300355, 2024. DOI: 10.1002/gch2.202300355.
  • H. Liang, W. Hu, L. Wang, K. Gong, Y. Qian, and L. Li, “An Improved Spider Wasp Optimizer for UAV Three-Dimensional Path Planning,” Biomimetics, vol. 9, no. 12, p. 765, Dec. 2024. DOI: 10.3390/biomimetics9120765.
  • A. Fathy, “An efficient spider wasp optimizer-based tracker for enhancing the harvested power from thermoelectric generation sources,” Case Studies in Thermal Engineering, vol. 61, no. 104878, p. 1-26, 2024. DOI: 10.1016/j.csite.2024.104878.
  • H. Nabil, H. Tayeb, “Effect of chaos on the performance of spider wasp meta-heuristic optimization algorithm for high-dimensional optimization problems,” Mathematical Modelling and Numerical Simulation with Applications, vol. 5, no. 1, p. 143-171, 2025. DOI: 10.53391/mmnsa.1571964.
  • D. Evangeline, G. Usha, “Deep Learning Driven LSTM with Spider Wasp Optimizer Algorithm for Frictional Force Based Landslides Prediction Model,” Journal of Intelligent Systems and Internet of Things, vol. 14, no. 1, p. 293-304, 2025. DOI: 10.54216/JISIoT.140123.
  • H. Nabil, H. Tayeb, “Effect of chaos on the performance of spider wasp meta-heuristic optimization algorithm for high-dimensional optimization problems,” Mathematical Modelling and Numerical Simulation with Applications, vol. 5, no. 1, p. 143-171, 2025. DOI: 10.53391/mmnsa.1571964.
  • Y. Gao, Z. Li, H. Wang, Y. Hu, H. Jiang, X. Jiang, D. Chen, “An Improved Spider-Wasp Optimizer for Obstacle Avoidance Path Planning in Mobile Robots,” Mathematics, vol. 12, no. 2604, p. 1-25, 2024. DOI: 10.3390/math12172604
  • S. Saber, S. Salem, “High-Performance Technique for Estimating the Unknown 1Parameters of Photovoltaic Cells and Modules Based on 2Improved Spider Wasp Optimizer,” Sustainable Machine Intelligence Journal, vol. 5, p. 1-14, 2023. DOI: 10.61185/SMIJ.2023.55102.
  • https://benchmarkfcns.info/fcns (Access Date: 10.03.2025).
  • https://al-roomi.org/benchmarks (Access Date: 10.03.2025).

Spider Wasp Optimization Algorithm

Yıl 2025, Cilt: 9 Sayı: 1, 42 - 67, 30.06.2025

Osman Karataş , Celal Yaşar , Hasan Temurtaş , Serdar Özyön

https://doi.org/10.47897/bilmes.1659488

Öz

This study aims to improve the performance of the Spider Wasp Optimization (SWO) algorithm, a swarm intelligence algorithm recently introduced in the literature, on various test functions with fixed and variable dimensions. Optimization can be defined as making a system as efficient as possible with minimal cost within certain constraints. Numerous optimization algorithms have been designed in the literature and used to obtain the best solutions for specific problems. The most critical aspects in solving these problems include correctly modeling the problem, determining the problem’s parameters and constraints, and finally selecting an appropriate meta-heuristic algorithm to solve the objective function. Not every algorithm is suitable for every problem structure. Some algorithms perform better on fixed-dimension test functions, while others in solving variable-dimension test functions. In this study, the performance of the SWO algorithm was evaluated on 10 test functions previously used in the literature, consisting of three fixed-dimension functions (Schaffer, Himmelblau and Kowalik Functions) and seven variable-dimension functions, including one unimodal function (Elliptic Function) and six multimodal functions (Non-Continuous Rastrigin, Alpine, Levy, Weierstrass, Michalewicz, and Dixon & Price Functions). The solution values obtained for each of the selected functions were compared with the solutions obtained using the Harris Hawks Optimizer (HHO), the Charged System Search (CSS), and the Backtracking Search Optimization Algorithm (BSA).

Anahtar Kelimeler

Spider Wasp Optimization Harris Hawks Optimizer Charged System Search Algorithm Backtracking Search Optimization Fixed and variable size unimodal and multimodal test functions

Kaynakça

  • F. Cantaş, S. Özyön, and C. Yaşar, “Runge Kutta Optimization for Fixed Size Multimodal Test Functions,” International Scientific and Vocational Studies Journal, vol. 6, no. 2, pp. 144-155, 2022. DOI: 10.47897/bilmes.1219033.
  • S. M. Öztürk and A. Çifci, “A Study in Enhancing Battery Management Systems for Diverse Battery Types,” International Scientific and Vocational Studies Journal, vol. 7, no. 2, pp. 122-136, 2023. DOI: 10.47897/bilmes.1385510.
  • T. Aktaş, İ. M. Temel, and A. Saygılı, “Comparative Analysis of Diabetes Diagnosis with Machine Learning Methods,” International Scientific and Vocational Studies Journal, vol. 8, no. 1, pp. 22-32, 2024. DOI: 10.47897/bilmes.1447878.
  • A. İ. Çanakoğlu, “Monte Carlo Increased-Radius Floating Random Walk Solution For Potential Problems,” International Scientific and Vocational Studies Journal, vol. 8, no. 1, pp. 13-21, 2024. DOI: 10.47897/bilmes.1441414.
  • K. Gencer, G. Gencer, and İ. H. Cizmeci, “Deep Learning Approaches for Retinal Image Classification: A Comparative Study of GoogLeNet and ResNet Architectures,” International Scientific and Vocational Studies Journal, vol. 8, no. 2, pp. 123-128, 2024. DOI: 10.47897/bilmes.1523768.
  • U. Saray and U. Çavdar, “Comparison of Different Optimization Algorithms in the Fashion MNIST Dataset,” International Journal of Multidisciplinary Studies and Innovative Technologies (IJMSIT), vol. 8, no. 2, pp. 52-58, 2024.
  • M. D. Demirbaş and D. Çakır (Sofuoğlu), “Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem,” International Scientific and Vocational Studies Journal (ISVOS), vol. 6, no. 2, pp. 103-115, 2022. DOI: 10.47897/bilmes.1207256.
  • M. Lüy and N. A. Metin, “PID Control Medium Size Wind Turbine Control with Integrated Blade Pitch Angle,” International Scientific and Vocational Studies Journal (ISVOS), vol. 6, no. 1, pp. 22-31, 2022. DOI: 10.47897/bilmes.1091968.
  • B. Durmuş, “Opposite Based Crow Search Algorithm for Solving Optimization Problems,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 2, pp. 164-170, 2021. DOI: 10.47897/bilmes.1031011.
  • Ö. Yavuz, “An Optimization Focused Machine Learning Approach in Analysing Arts Participative Behavior with Fine Arts Education Considerations,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 2, pp. 241-253, 2021. DOI: 10.47897/bilmes.1029139.
  • M. D. Demirbaş, M. Oğuz, and İ. Erişen, “Investigation of Buckling Behavior of Beams with Artificial Neural Network,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 1, pp. 94-106, 2021.
  • E. Dağdevir and M. Tokmakçı, “The Role of Feature Selection in Significant Information Extraction from EEG Signals,” International Scientific and Vocational Studies Journal (ISVOS), vol. 5, no. 1, pp. 1-6, 2021.
  • B. Alızada, “Improved Whale Optimization Algorithm Based On π Number,” International Scientific and Vocational Studies Journal (ISVOS), vol. 4, no. 1, pp. 21-30, 2020.
  • E. Göçmen and O. Derse, “A Tabu Search Algorithm for an Excavator Scheduling Problem,” International Scientific and Vocational Studies Journal (ISVOS), vol. 4, no. 1, pp. 1-11, 2020.
  • M. Taşova, “Effect on the Effective Diffusion and Activation Energy Values of Pea (Pisum sativum L.) Grains of Drying Temperature,” International Scientific and Vocational Studies Journal (ISVOS), vol. 3, no. 1, pp. 8-13, 2019.
  • B. Öztürk, L. Uğur, F. Erzincanlı, and Ö. Küçük, “Optimization of Polyethylene Inserts Design Geometry of Total Knee Prosthesis,” International Scientific and Vocational Studies Journal (ISVOS), vol. 2, no. 2, pp. 31-39, 2018.
  • D. Çakır and M. D. Demirbaş, “Modelling of One-directional Functionally Graded Circular Plates with Artificial Neural Network,” International Scientific and Vocational Studies Journal (ISVOS), vol. 3, no. 1, pp. 42-50, 2019.
  • M. Abdel-Basset, R. Mohamed, M. Jameel, and M. Abouhawwash, “Spider wasp optimizer: a novel meta-heuristic optimization algorithm,” Artificial Intelligence Review, vol. 56, no. 10, pp. 11675-11738, 2023..
  • A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems, vol. 97, pp. 849-872, 2019.
  • A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search,” Acta Mechanica, vol. 213, no. 3, pp. 267-289, 2010.
  • P. Civicioglu, “Backtracking search optimization algorithm for numerical optimization problems,” Applied Mathematics and Computation, vol. 219, no. 15, pp. 8121-8144, 2013.
  • J. Sui, Z. Tian, and Z. Wang, “Multiple strategies improved spider wasp optimization for engineering optimization problem solving,” Scientific Reports, vol. 14, no. 1, p. 29048, 2024. DOI: 10.1038/s41598-024-78589-8.
  • İ. Çetinbaş, “Parameter extraction of single, double, and triple-diode photovoltaic models using the weighted leader search algorithm,” Global Challenges (Hoboken, NJ), vol. 8, no. 5, p. 2300355, 2024. DOI: 10.1002/gch2.202300355.
  • H. Liang, W. Hu, L. Wang, K. Gong, Y. Qian, and L. Li, “An Improved Spider Wasp Optimizer for UAV Three-Dimensional Path Planning,” Biomimetics, vol. 9, no. 12, p. 765, Dec. 2024. DOI: 10.3390/biomimetics9120765.
  • A. Fathy, “An efficient spider wasp optimizer-based tracker for enhancing the harvested power from thermoelectric generation sources,” Case Studies in Thermal Engineering, vol. 61, no. 104878, p. 1-26, 2024. DOI: 10.1016/j.csite.2024.104878.
  • H. Nabil, H. Tayeb, “Effect of chaos on the performance of spider wasp meta-heuristic optimization algorithm for high-dimensional optimization problems,” Mathematical Modelling and Numerical Simulation with Applications, vol. 5, no. 1, p. 143-171, 2025. DOI: 10.53391/mmnsa.1571964.
  • D. Evangeline, G. Usha, “Deep Learning Driven LSTM with Spider Wasp Optimizer Algorithm for Frictional Force Based Landslides Prediction Model,” Journal of Intelligent Systems and Internet of Things, vol. 14, no. 1, p. 293-304, 2025. DOI: 10.54216/JISIoT.140123.
  • H. Nabil, H. Tayeb, “Effect of chaos on the performance of spider wasp meta-heuristic optimization algorithm for high-dimensional optimization problems,” Mathematical Modelling and Numerical Simulation with Applications, vol. 5, no. 1, p. 143-171, 2025. DOI: 10.53391/mmnsa.1571964.
  • Y. Gao, Z. Li, H. Wang, Y. Hu, H. Jiang, X. Jiang, D. Chen, “An Improved Spider-Wasp Optimizer for Obstacle Avoidance Path Planning in Mobile Robots,” Mathematics, vol. 12, no. 2604, p. 1-25, 2024. DOI: 10.3390/math12172604
  • S. Saber, S. Salem, “High-Performance Technique for Estimating the Unknown 1Parameters of Photovoltaic Cells and Modules Based on 2Improved Spider Wasp Optimizer,” Sustainable Machine Intelligence Journal, vol. 5, p. 1-14, 2023. DOI: 10.61185/SMIJ.2023.55102.
  • https://benchmarkfcns.info/fcns (Access Date: 10.03.2025).
  • https://al-roomi.org/benchmarks (Access Date: 10.03.2025).
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Dağıtılmış Sistemler ve Algoritmalar, Memnuniyet ve Optimizasyon
Bölüm Makaleler
Yazarlar

Osman Karataş 0009-0006-3067-1157

Celal Yaşar 0000-0002-5069-8545

Hasan Temurtaş 0000-0001-6738-3024

Serdar Özyön 0000-0002-4469-3908

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 17 Mart 2025
Kabul Tarihi 30 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 1

Kaynak Göster

APA Karataş, O., Yaşar, C., Temurtaş, H., Özyön, S. (2025). Spider Wasp Optimization Algorithm. International Scientific and Vocational Studies Journal, 9(1), 42-67. https://doi.org/10.47897/bilmes.1659488
AMA Karataş O, Yaşar C, Temurtaş H, Özyön S. Spider Wasp Optimization Algorithm. ISVOS. Haziran 2025;9(1):42-67. doi:10.47897/bilmes.1659488
Chicago Karataş, Osman, Celal Yaşar, Hasan Temurtaş, ve Serdar Özyön. “Spider Wasp Optimization Algorithm”. International Scientific and Vocational Studies Journal 9, sy. 1 (Haziran 2025): 42-67. https://doi.org/10.47897/bilmes.1659488.
EndNote Karataş O, Yaşar C, Temurtaş H, Özyön S (01 Haziran 2025) Spider Wasp Optimization Algorithm. International Scientific and Vocational Studies Journal 9 1 42–67.
IEEE O. Karataş, C. Yaşar, H. Temurtaş, ve S. Özyön, “Spider Wasp Optimization Algorithm”, ISVOS, c. 9, sy. 1, ss. 42–67, 2025, doi: 10.47897/bilmes.1659488.
ISNAD Karataş, Osman vd. “Spider Wasp Optimization Algorithm”. International Scientific and Vocational Studies Journal 9/1 (Haziran 2025), 42-67. https://doi.org/10.47897/bilmes.1659488.
JAMA Karataş O, Yaşar C, Temurtaş H, Özyön S. Spider Wasp Optimization Algorithm. ISVOS. 2025;9:42–67.
MLA Karataş, Osman vd. “Spider Wasp Optimization Algorithm”. International Scientific and Vocational Studies Journal, c. 9, sy. 1, 2025, ss. 42-67, doi:10.47897/bilmes.1659488.
Vancouver Karataş O, Yaşar C, Temurtaş H, Özyön S. Spider Wasp Optimization Algorithm. ISVOS. 2025;9(1):42-67.


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