Abstract In this paper, a new swarm intelligence optimization algorithm called Frigate Bird Optimizer (FBO) is proposed, which is inspired by the unique flight and foraging activities of frigatebirds in the natural environment. The optimization process of the Frigate Bird Optimizer algorithm is divided into two phases to simulate different behavioral characteristics of frigate birds. The first phase simulates its behavior of harassing other seabirds to grab food, and the individuals of the population in this phase show a certain degree of randomness and uncertainty in the search direction and radius, which is conducive to a comprehensive and extensive exploration of the global search space. The second stage simulates its behavior of observing large predatory fish driving small fish to leap out of the water, and using high-speed swooping and flexible steering techniques to capture food. In this phase, the algorithm enters the second half of the algorithm, where individuals of the population tend to aggregate towards the optimal search direction, increasing the search intensity of the offspring in that region. The whole optimization process shows good algorithmic performance by simulating the behavior of frigatebirds under different survival strategies, achieving extensive global search in the first stage and fine-grained local exploitation by learning information in the second stage. To assess the optimization capabilities of the FBO algorithm, forty-six functions from the recognized CEC2014 and CEC2017 benchmark suites were utilized as target objectives, and its performance was benchmarked against nine contemporary meta-heuristic algorithms. The outcomes demonstrate that the FBO algorithm exhibits superior optimization performance, featuring outstanding iterative refinement and resilience, making it a potent contender for tackling diverse optimization challenges.