Abstract Metaheuristic optimization algorithms are sophisticated, flexible approaches created to address real-world challenging issues when conventional approaches are unfeasible or ineffective. Rising complexity of optimization issues in the actual world has led to the development of numerous metaheuristic techniques recently. Hence, a new optimization technique called the Black-necked Crane Optimizer (BNCO) is presented in this study. The foraging, migration, and survival of the fittest behaviors of black-necked cranes serve as its inspiration. Building on these behaviors, the proposed algorithm enhances global search capability and convergence speed by effectively balancing global exploration and local exploitation, thereby improving accuracy and avoiding entrapment in local optima for complex engineering optimization problems. To illustrate the validity and efficacy of the proposed approach, BNCO has been tested using 30 complex functions from CEC 2017. Furthermore, complex problems from the CEC 2022 benchmark suite are examined across various categories, including unimodal, multimodal, hybrid, and composition functions. The proposed algorithm achieves success rates of 63.33% and 60% on CEC 2017 with 10 and 30 dimensions, respectively, and 75% and 66.67% on CEC 2022 with 10 and 20 dimensions, demonstrating robust performance across different problem scales. Moreover, to evaluate the feasibility of the suggested method in resolving a variety of real-world optimization issues, ten engineering design issues are analyzed and contrasted. The Friedman and Wilcoxon analysis are well-known statistical techniques that are also employed for comparison. According to the experimental findings and statistical comparisons, the suggested BNCO has the potential to substantially tackle real-world issues by more rapidly identifying superior solutions in comparison to eleven well-known optimizers.
Menbawy et al. (Mon,) studied this question.