Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization (MFO), inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats. The foundation of MFO is based on the kleptoparasitic behavior of these birds, where they steal prey from other seabirds. In this process, a magnificent frigatebird targets a food-carrying seabird, aggressively pecking at it until the seabird drops its prey. The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water. The theoretical framework of MFO is thoroughly detailed and mathematically represented, mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases: exploration and exploitation. During the exploration phase, the algorithm searches for new potential solutions across a broad area, akin to the frigatebird scouting for vulnerable seabirds. In the exploitation phase, the algorithm fine-tunes the solutions, similar to the frigatebird focusing on a single target to secure its meal. To evaluate MFO’s performance, the algorithm is tested on twenty-three standard benchmark functions, including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization process. Comparative studies with twelve well-known metaheuristic algorithms demonstrate that MFO consistently achieves superior optimization results, outperforming its competitors across various metrics. In addition, the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications, thereby validating its practical utility and robustness.