<![CDATA[For many complex optimization problems, the solution process often involves exploring multidimensional space, balancing global and local solutions, and improving the efficiency of the algorithm. In order to improve the optimization efficiency, this paper proposes a new metaheuristic algorithm called the Duck Foraging Algorithm (DFA). The algorithm is inspired by the behavior patterns of wild ducks in nature when foraging, especially their intra-group cooperation, clear division of labor, territoriality, and mobile foraging strategies. By simulating the foraging behavior of ducks, DFA can effectively explore and develop complex solution spaces and find the global optimal solution. The core principles and processes of the algorithm are elaborated in detail and compared with existing optimization algorithms. Finally, we verify its superiority in different types of optimization problems through a series of numerical experiments. Compared with traditional algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC), DFA incorporates unique behavioral mechanisms—such as dynamic leadership switching and decentralized area foraging—based on duck group strategies. In particular, the leader duck guides the group based on fitness ranking, while other ducks balance local search and migration, reflecting a cooperative yet diversified exploration strategy.]]>
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