Red-crested Turaco Optimization (RCTO): A Novel Nature-inspired Metaheuristic for Solving Optimization Problems

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 explorationexploitation 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 fixeddimensional, 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.

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