Population-based algorithms have become a research hotspot for optimization problems and have been widely applied in various fields in recent decades. This paper presents the birds foraging search (BFS) algorithm, which is a novel population-based optimizer inspired by the different behaviors of birds during the foraging process for solving global optimization problems. The overall framework of the proposed algorithm involves three phases: the flying search behavior phase, the territorial behavior phase and the cognitive behavior phase. In the proposed algorithm, the first two phases balance the exploration and exploitation capabilities of the algorithm, and the third phase enhances the search efficiency. Classical benchmarks and CEC2014 benchmarks are employed to fully evaluate the performance of our BFS. The statistical results reveal that the BFS algorithm outperforms other conventional approaches and state-of-the art algorithms in terms of accuracy and convergence.

中文翻译：

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鸟类觅食搜索：一种基于种群的新型全局优化算法

基于人口的算法已成为优化问题的研究热点，近几十年来已广泛应用于各个领域。本文提出了鸟类觅食搜索（BFS）算法，它是一种新颖的基于种群的优化器，其灵感来自于鸟类在觅食过程中的不同行为，以解决全局优化问题。该算法的总体框架包括三个阶段：飞行搜索行为阶段，领土行为阶段和认知行为阶段。在提出的算法中，前两个阶段平衡了算法的探索和开发能力，第三阶段提高了搜索效率。我们使用经典基准和CEC2014基准来全面评估BFS的性能。