<p>Metaheuristics play a key role in solving a wide range of challenging problems in both science and industry. In this paper, a novel optimization algorithm based on metaheuristic methods called the Boxelder Bug Search Optimization (BBSO) algorithm is proposed. The core idea of this algorithm is inspired by the search, tracking, and following behaviors of Boxelder bugs to find a heat source for surviving the winter season. To evaluate and confirm the effectiveness of the BBSO algorithm in finding optimal solutions, its performance is tested on 23 classical test functions, 30 test functions from CEC2014, seven engineering problems, and an economic load dispatch problem. The results of optimizing mathematical functions demonstrate that the BBSO algorithm can find optimal solutions starting from random initial conditions. The results are compared with eight optimization methods, including differential evolution with random selection, the arithmetic optimization algorithm, the artificial bee colony algorithm, the whale optimization algorithm, the cuckoo search algorithm, the firefly algorithm, particle swarm optimization, and teaching–learning-based optimization. The case studies show that the BBSO algorithm is competitive and capable when tackling real-world problems characterized by complex and unfamiliar search spaces. Statistical evaluation using the Wilcoxon rank-sum test and the Friedman test based on rankings indicates the significant superiority of the BBSO algorithm compared to other methods. Tests on classical and CEC-2014 benchmark functions revealed success rates of 56.52% and 86.67%, respectively, showcasing the algorithm's commendable overall performance.&#xa0;The source codes of the BBSO algorithm are publicly available at&#xa0;<a href="https://github.com/Irajfaraji/BBSO">https://github.com/Irajfaraji/BBSO</a>.</p>