Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (5): 2359-2388.doi: 10.1007/s42235-023-00386-2
A Global Best‑guided Firefy Algorithm for Engineering Problems
Mohsen Zare1; Mojtaba Ghasemi2; Amir Zahedi3; Keyvan Golalipour4; Soleiman Kadkhoda Mohammadi5; Seyedali Mirjalili6,7,12; Laith Abualigah8,9,10,11,13,14
- 1 Department of Electrical Engineering, Faculty of Engineering, Jahrom University, Jahrom 7413188941, Fras, Iran 2 Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz 1387671557, Iran 3 Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 1411713116, Iran 4 Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari 4816119318, Iran 5 Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia 571696896, Iran 6 Centre for Artifcial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 4006, Australia 7 Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea 8 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan 9 Hourani Center for Applied Scientifc Research, Al-Ahliyya Amman University, Amman 19328, Jordan 10 Faculty of Information Technology, Middle East University, Amman 11831, Jordan 11 School of Computer Sciences, Universiti Sains Malaysia, 11800 George Town, Pulau Pinang, Malaysia 12 University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary 13 School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia 14 Applied science research center, Applied science private university, Amman 11931, Jordan
A Global Best‑guided Firefy Algorithm for Engineering Problems
Mohsen Zare1; Mojtaba Ghasemi2; Amir Zahedi3; Keyvan Golalipour4; Soleiman Kadkhoda Mohammadi5; Seyedali Mirjalili6,7,12; Laith Abualigah8,9,10,11,13,14
- 1 Department of Electrical Engineering, Faculty of Engineering, Jahrom University, Jahrom 7413188941, Fras, Iran 2 Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz 1387671557, Iran 3 Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 1411713116, Iran 4 Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari 4816119318, Iran 5 Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia 571696896, Iran 6 Centre for Artifcial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 4006, Australia 7 Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea 8 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan 9 Hourani Center for Applied Scientifc Research, Al-Ahliyya Amman University, Amman 19328, Jordan 10 Faculty of Information Technology, Middle East University, Amman 11831, Jordan 11 School of Computer Sciences, Universiti Sains Malaysia, 11800 George Town, Pulau Pinang, Malaysia 12 University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary 13 School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia 14 Applied science research center, Applied science private university, Amman 11931, Jordan
摘要： The Firefly Algorithm (FA) is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating. This article proposes a method based on Differential Evolution (DE)/current-to-best/1 for enhancing the FA's movement process. The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution. However, employing the best solution can lead to premature algorithm convergence, but this study handles this issue using a loop adjacent to the algorithm's main loop. Additionally, the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA. The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values. Additionally, the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms. In all cases, GbFA provides the optimal result compared to other methods. Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.