Actor Optimization Algorithm: A Novel Approach for Engineering Design Challenges
Received: 8 January 2025 | Revised: 28 January 2025 | Accepted: 6 February 2025 | Online: 3 April 2025
Corresponding author: Widi Aribowo
Abstract
In this paper, a novel human-based metaheuristic algorithm called Actor Optimization Algorithm (AOA) is introduced. AOA mimics the behaviors of an actor when playing a role. The main idea in designing AOA is derived from a specific behavior of the actor including (i) simulating the movements and dialogues of the given role and (ii) practicing to better present the assigned role. The theory of AOA is stated and mathematically modeled in the phases of exploration and exploitation. The performance of AOA to address real-world applications is evaluated on the CEC 2011 test suite. The optimization results show that AOA, with its high ability in exploration, exploitation, and balancing during the search process, achieved suitable results. In addition, the performance of AOA was challenged by comparing it with 12 known metaheuristic algorithms. Result comparison showed that the proposed AOA outperformed the competing algorithms by 100% (in all 22 optimization problems) of the CEC 2011 test suite. The simulation results show that AOA has a successful performance in handling optimization tasks in real-world applications by achieving better results in competition with the compared algorithms.
Keywords:
optimization, human-based metaheuristic, actor optimization algorithmDownloads
References
S. Zhao, T. Zhang, S. Ma, and M. Chen, "Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications," Engineering Applications of Artificial Intelligence, vol. 114, Sep. 2022, Art. no. 105075.
X. Wang, "Draco lizard optimizer: a novel metaheuristic algorithm for global optimization problems," Evolutionary Intelligence, vol. 18, no. 1, Nov. 2024, Art. no. 10.
V. Tomar, M. Bansal, and P. Singh, "Metaheuristic Algorithms for Optimization: A Brief Review," Engineering Proceedings, vol. 59, no. 1, 2024, Art. no. 238.
R. Abu-Gdairi, R. Mareay, and M. Badr, "On Multi-Granulation Rough Sets with Its Applications," Computers, Materials & Continua, vol. 79, no. 1, pp. 1025–1038, 2024.
H. Qawaqneh, "New contraction embedded with simulation function and cyclic (α, β)-admissible in metric-like spaces," International Journal of Mathematics and Computer Science, vol. 15, no. 1, pp. 1029–1044, 2020.
T. Hamadneh, M. Ali, and H. AL-Zoubi, "Linear Optimization of Polynomial Rational Functions: Applications for Positivity Analysis," Mathematics, vol. 8, no. 2, Feb. 2020, Art. no. 283.
M. Dehghani, E. Trojovska, and P. Trojovsky, "A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process," Scientific Reports, vol. 12, no. 1, Jun. 2022, Art. no. 9924.
T. Hamadneh et al., "On the Application of Potter Optimization Algorithm for Solving Supply Chain Management Application," International Journal of Intelligent Engineering and Systems, vol. 17, no. 5, pp. 88–99, Oct. 2024.
S. Alomari et al., "Carpet Weaver Optimization: A Novel Simple and Effective Human-Inspired Metaheuristic Algorithm," International Journal of Intelligent Engineering and Systems, vol. 17, no. 4, pp. 230–242, Aug. 2024.
T. Hamadneh et al., "Sales Training Based Optimization: A New Human-inspired Metaheuristic Approach for Supply Chain Management," International Journal of Intelligent Engineering and Systems, vol. 17, no. 6, pp. 1325–1334, Oct. 2024.
S. Al omari et al., "Dollmaker Optimization Algorithm: A Novel Human-Inspired Optimizer for Solving Optimization Problems," International Journal of Intelligent Engineering and Systems, vol. 17, no. 3, pp. 816–828, Jun. 2024.
T. Hamadneh et al., "On the Application of Tailor Optimization Algorithm for Solving Real-World Optimization Application," International Journal of Intelligent Engineering and Systems, vol. 18, no. 1, pp. 1–12, Feb. 2025.
T. Hamadneh et al., "Sculptor Optimization Algorithm: A New Human-Inspired Metaheuristic Algorithm for Solving Optimization Problems," International Journal of Intelligent Engineering and Systems, vol. 17, no. 4, pp. 564–575, Aug. 2024.
N. Nouri and A. Tadaion, "Energy optimal resource allocation for mobile edge computation offloading in presence of computing access point," in Iran Workshop on Communication and Information Theory, Tehran, Iran, Apr. 2018, pp. 1–6.
N. Nouri, F. Fazel, J. Abouei, and K. N. Plataniotis, "Multi-UAV Placement and User Association in Uplink MIMO Ultra-Dense Wireless Networks," IEEE Transactions on Mobile Computing, vol. 22, no. 3, pp. 1615–1632, Mar. 2023.
N. Nouri, A. Entezari, J. Abouei, M. Jaseemuddin, and A. Anpalagan, "Dynamic Power–Latency Tradeoff for Mobile Edge Computation Offloading in NOMA-Based Networks," IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2763–2776, Apr. 2020.
J. de Armas, E. Lalla-Ruiz, S. L. Tilahun, and S. Voß, "Similarity in metaheuristics: a gentle step towards a comparison methodology," Natural Computing, vol. 21, no. 2, pp. 265–287, Jun. 2022.
E. Trojovska, M. Dehghani, and P. Trojovsky, "Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm," IEEE Access, vol. 10, pp. 49445–49473, 2022.
D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, Apr. 1997.
S. Das and P. N. Suganthan, "Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems," Jadavpur University and Nanyang Technological University, Technical Report, Dec. 2010.
D. E. Goldberg and J. H. Holland, "Genetic Algorithms and Machine Learning," Machine Learning, vol. 3, no. 2, pp. 95–99, Oct. 1988.
J. Kennedy and R. Eberhart, "Particle swarm optimization," in International Conference on Neural Networks, Perth, WA, Australia, Dec. 1995, vol. 4, pp. 1942–1948 vol.4.
E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, "GSA: A Gravitational Search Algorithm," Information Sciences, vol. 179, no. 13, pp. 2232–2248, Jun. 2009.
R. V. Rao, V. J. Savsani, and D. P. Vakharia, "Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems," Computer-Aided Design, vol. 43, no. 3, pp. 303–315, Mar. 2011.
S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, "Multi-Verse Optimizer: a nature-inspired algorithm for global optimization," Neural Computing and Applications, vol. 27, no. 2, pp. 495–513, Feb. 2016.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014.
S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Advances in Engineering Software, vol. 95, pp. 51–67, May 2016.
A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, "Marine Predators Algorithm: A nature-inspired metaheuristic," Expert Systems with Applications, vol. 152, Aug. 2020, Art. no. 113377.
S. Kaur, L. K. Awasthi, A. L. Sangal, and G. Dhiman, "Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization," Engineering Applications of Artificial Intelligence, vol. 90, Apr. 2020, Art. no. 103541.
L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, and A. H. Gandomi, "Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer," Expert Systems with Applications, vol. 191, Apr. 2022, Art. no. 116158.
B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, "African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems," Computers & Industrial Engineering, vol. 158, Aug. 2021, Art. no. 107408.
M. Braik, A. Hammouri, J. Atwan, M. A. Al-Betar, and M. A. Awadallah, "White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems," Knowledge-Based Systems, vol. 243, May 2022, Art. no. 108457.
F. Wilcoxon, "Individual Comparisons by Ranking Methods," in Breakthroughs in Statistics: Methodology and Distribution, S. Kotz and N. L. Johnson, Eds. New York, NY, USA: Springer, 1992, pp. 196–202.
Downloads
How to Cite
License
Copyright (c) 2025 Widi Aribowo, Belal Batiha, Tareq Hamadneh, Gharib Mousa Gharib, Hind Monadhel, Riyadh Kareem Jawad, Ibraheem Kasim Ibraheem, Zeinab Monrazeri, Mohammad Dehghani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
