Ashraf Al-Ou'n, Saleh Ali Alomari, Raed Abu Zitar, Mirjalol Ismoilov, Aseel Smerat, Widi Aribowo, Zeinab Montazeri, Mohammad Dehghani, Om Parkash Malik, Kei Eguchi
A novel bio-inspired metaheuristic algorithm, Red-Crested Turaco Optimization (RCTO), grounded in the natural behaviour of Tauraco erythrolophus, is introduced in this paper. RCTO translates three distinctive behavioural traits—long inter-tree jumps, threat-driven dispersion, and precise branch-to-branch probing—into computational operators that collectively balance global exploration and local exploitation. In the mapping phase, each turaco is assigned to a candidate solution, while the remaining phases exemplify the potential for a global search. This is achieved through the combination of random jumps to each level of the tree, threat adaptive diversification, and local search intensification. The agents’ use of sound and sight signals facilitates the adjustment of the exploration-exploitation balance. The trade-off is more biologically inspired than algorithmically inspired. The algorithm’s structure is such that its computational complexity is linear to the population size, dimensionality, and the number of iterations. This contributes to the algorithm’s ability to be applied to high-dimensional problems. The algorithm was subjected to rigorous simulation testing with 23 benchmark functions, including singular, multi-peak, and fixed-dimensional, high-dimensional variations. The extensive qualitative search path analyses, leading agent analyses, and convergence behaviour analyses affirm the algorithm’s ability to adapt and traverse complex search spaces. Quantitative evaluations, including population diversity indices and exploration–exploitation ratios, show that the RCTO algorithm starts the search with a broad exploration coverage and then gradually tightens its focus around the global optimum. Comparative studies with nine state-of-the-art metaheuristic algorithms show that RCTO consistently achieves higher solution quality, faster convergence, and greater stability across all classes of benchmark functions. These results highlight the effectiveness of animal behaviour-inspired operators in achieving balanced, adaptive, and efficient search dynamics. Overall, the RCTO algorithm is introduced as a highly effective, stable, and biologically based optimization framework that is capable of outperforming contemporary metaheuristics on both single-peak and multi-peak optimization problems. © This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/
Department of Computer Science, Faculty of Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan; Computer Science Department, Faculty of Information Technology, Jadara University, Irbid, 21110, Jordan; College of Engineering and Computing, Liwa University, Abu Dhabi, 41009, United Arab Emirates; Department of Transport systems, Urgench State University named after Abu Rayhan Biruni, 14, Kh. Alimdjan str, Urgench, 220100, Uzbekistan; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan; Department of Electrical Engineering, Faculty of Vocational Studies, Universitas Negeri Surabaya, Indonesia; Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran; Department of Electrical and Software Engineering, University of Calgary, Calgary, T2N 1N4, AB, Canada; Department of Information Electronics, Fukuoka Institute of Technology, Japan
