Improvement of the Arctic Puffin Metaheuristic Algorithm Based on a Chaotic Map
Hamid Reza Ghasemi
1
(
Department of Computer Engineering and Information Technology, Sab.C., Islamic Azad University, Sabzevar, Iran
)
Ali Akbar Neghabi
2
(
Department of Computer Engineering and Information Technology, Sab.C., Islamic Azad University, Sabzevar, Iran
)
Babak Lotfi
3
(
PhD student, Department of Computer Engineering and Information Technology, Sab.C., Islamic Azad University, Sabzevar, Iran
)
Keywords: Arctic Puffin Optimization (APO), Chaotic Optimization, Logistic Map, Metaheuristic Algorithm, Evolutionary Computation.,
Abstract :
The Arctic Puffin Optimization (APO) algorithm, introduced in 2024, is inspired by the hunting behavior of puffins in aerial and marine environments and utilizes swarm intelligence concepts to model the collective movements of puffins. By employing various movement strategies, this algorithm aims to establish an appropriate balance between exploration and exploitation processes in the search space. However, the dependence of some search stages on random processes may lead to irregular variations in the movement trajectories of search agents, reduced control over population diversity, and an increased risk of premature convergence. In this study, an improved version of the Arctic Puffin Optimization algorithm, referred to as the Chaotic Arctic Puffin Algorithm (CAPA), is proposed. The proposed approach incorporates a Logistic chaotic map to enhance the generation quality of search-guiding parameters and improve the movement behavior of optimization agents. By exploiting the nonlinear properties and effective space-filling capability of chaotic sequences, the proposed mechanism aims to achieve a more effective balance between exploration and exploitation. The performance of the proposed algorithm is evaluated on 12 benchmark functions with different dimensions and compared with the original Arctic Puffin Optimization algorithm as well as several well-established metaheuristic algorithms, including Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and their chaotic variants. The experimental results demonstrate that the Chaotic Arctic Puffin Algorithm improves solution quality, search stability, and convergence behavior compared with the original APO algorithm, while maintaining competitive performance against other reference methods. Furthermore, the results of the Wilcoxon signed-rank test and Friedman test confirm that the performance difference between the proposed algorithm and the original Arctic Puffin Optimization algorithm is statistically significant.
مراجع [1] Zheng, Y. J. (2015). Water wave optimization: a new nature-inspired metaheuristic. Computers & Operations Research, 55, 1-11.
[2] Mahfoud, S. W. (1995). A comparison of parallel and sequential niching methods. In Conference on genetic algorithms (Vol. 136, p. 143).
[3] Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
[4] Wang, W. C., Tian, W. C., Xu, D. M., & Zang, H. F. (2024). Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization. Advances in Engineering Software, 195, 103694.
[5] Fakhouri, H. N., Alkhalaileh, M. S., Hamad, F., Sirhan, N. N., & Fakhouri, S. N. (2024). Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems. Algorithms, 17(12), 589.
[6] Sun, L., & Wang, B. (2024). Arctic Puffin Optimization Algorithm Based on Multi-Strategy Blending. Journal of Computer and Communications, 12(12), 151-170.
[7] Xiao, J., Xu, W., & Chen, J. (2024, October). Social media emotional state classification prediction based on Arctic Puffin Algorithm (APO) optimization of Transformer mode. In 2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE) (pp. 1-6). IEEE.
[8] Lv, Y., Zhao, X., & Mou, Z. Parameter Identification and Verification of Doubly Fed Induction Generator Controllers Based on Arctic Puffin Optimization. Available at SSRN 5104987.
[9] Tang, W., Dai, J., Liu, B., Hu, W., Gong, K., & Fan, Y. (2024). Aircraft Range Fuel Consumption Prediction Using CNN-LSTM Enhanced by CEEMDAN and Improved Arctic Puffin Optimization Algorithm.
[10] Firmansyah, R., Widayaka, P. D., Fahrezy, M. R., Saputra, P. P. S., & Septian, D. (2024, October). Voltage Control of DC Microgrid using Arctic Puffin Optimization Algorithm for Tuning of Sliding Mode Control Gain. In 2024 Seventh International Conference on Vocational Education and Electrical Engineering (ICVEE) (pp. 279-282). IEEE.
[11] Chen, L., & Aihara, K. (1995). Chaotic simulated annealing by a neural network model with transient chaos. Neural networks, 8(6), 915-930.
[12] Yuan, X., Yuan, Y., & Zhang, Y. (2002). A hybrid chaotic genetic algorithm for short-term hydro system scheduling. Mathematics and computers in simulation, 59(4), 319-327.
[13] Li-Jiang, Y., & Tian-Lun, C. (2003). Application of chaos in genetic algorithms. Communications in Theoretical Physics, 38(2), 168.
[14] Mingjun, J., & Huanwen, T. (2004). Application of chaos in simulated annealing. Chaos, Solitons & Fractals, 21(4), 933-941.
[15] Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271.
[16] Chuanwen, J., & Bompard, E. (2006). A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Mathematics and computers in Simulation, 68(1), 57-65.
[17] Zuo, X. Q., & Fan, Y. S. (2006). A chaos search immune algorithm with its application to neuro-fuzzy controller design. Chaos, Solitons & Fractals, 30(1), 94-109.
[18] Xiang, T., Liao, X., & Wong, K. W. (2007). An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Applied Mathematics and Computation, 190(2), 1637-1645.
[19] Gong, W., & Wang, S. (2008, December). Chaos ant colony optimization and application. In 2009 fourth international conference on internet computing for science and engineering (pp. 301-303). IEEE.
[20] Alatas, B., Akin, E., & Ozer, A. B. (2009). Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals, 40(4), 1715-1734.
[21] Alatas, B. (2010). Chaotic harmony search algorithms. Applied mathematics and computation, 216(9), 2687-2699.
[22] Alatas, B. (2010). Chaotic bee colony algorithms for global numerical optimization. Expert systems with applications, 37(8), 5682-5687.
[23] Alatas, B. (2011). Uniform big bang–chaotic big crunch optimization. Communications in Nonlinear Science and Numerical Simulation, 16(9), 3696-3703.
[24] Talatahari, S., Azar, B. F., Sheikholeslami, R., & Gandomi, A. H. (2012). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation, 17(3), 1312-1319.
[25] Gandomi, A. H., Yang, X. S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89-98.
[26] Gandomi, A. H., & Yang, X. S. (2014). Chaotic bat algorithm. Journal of computational science, 5(2), 224-232.
[27] Askarzadeh, A., & dos Santos Coelho, L. (2014). A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model. International journal of hydrogen energy, 39(21), 11165-11174.
[28] Zhu, W., & Duan, H. (2014). Chaotic predator–prey biogeography-based optimization approach for UCAV path planning. Aerospace science and technology, 32(1), 153-161.
[29] Koupaei, J. A., & Hosseini, S. M. M. (2015). A new hybrid algorithm based on chaotic maps for solving systems of nonlinear equations. Chaos, Solitons & Fractals, 81, 233-245.
[30] Bingol, H., & Alatas, B. (2016). Chaotic league championship algorithms. Arabian journal for science and engineering, 41, 5123-5147.
[31] Heidari, A. A., Ali Abbaspour, R., & Rezaee Jordehi, A. (2017). An efficient chaotic water cycle algorithm for optimization tasks. Neural Computing and Applications, 28, 57-85.
[32] Kaur, G., & Arora, S. (2018). Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 5(3), 275-284.
[33] Zhenxing, Z., Rennong, Y. A. N. G., Huanyu, L. I., Yuhuan, F. A. N. G., Zhenyu, H., & Ying, Z. (2019). Antlion optimizer algorithm based on chaos search and its application. Journal of Systems Engineering and Electronics, 30(2), 352-365.
[34] Bingol, H., & Alatas, B. (2020). Chaos based optics inspired optimization algorithms as global solution search approach. Chaos, Solitons & Fractals, 141, 110434.
[35] Aydilek, I. B., Karaçizmeli, I. H., Tenekeci, M. E., Kaya, S., & Gümüşçü, A. (2021). Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems. Sādhanā, 46(2), 65.
[36] Zhang, X. Y., Zhou, K. Q., Li, P. C., Xiang, Y. H., Zain, A. M., & Sarkheyli-Hägele, A. (2022). An improved chaos sparrow search optimization algorithm using adaptive weight modification and hybrid strategies. Ieee Access, 10, 96159-96179.
[37] Aydemir, S. B. (2023). A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evolutionary Intelligence, 16(3), 981-996.
[38] Luo, T., Xie, J., Zhang, B., Zhang, Y., Li, C., & Zhou, J. (2024). An improved levy chaotic particle swarm optimization algorithm for energy-efficient cluster routing scheme in industrial wireless sensor networks. Expert Systems with Applications, 241, 122780.
[39] Abdelrazek, M., Abd Elaziz, M., & El-Baz, A. H. (2024). CDMO: Chaotic Dwarf Mongoose optimization algorithm for feature selection. Scientific reports, 14(1), 701.
[40] Abdel-Salam, M., Askr, H., & Hassanien, A. E. (2024). Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems. Expert Systems with Applications, 256, 124882.
[41] Chauhan, U., Chhabra, H., Jain, P., Dev, A., Chauhan, N., & Kumar, B. (2024). Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models. IFAC Journal of Systems and Control, 27, 100239.
[42] Farzin, S. (2025). A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation. Water Resources Management, 1-22.
[43] Qasim, M., Sajid, M., Rajak, R., & Shahid, M. (2025). Task Scheduling Strategy Using Chaotic Whale Optimization Algorithm in Cloud Computing. In Nature-Inspired Optimization Algorithms for Cyber-Physical Systems (pp. 31-52). IGI Global Scientific Publishing.