Yellow Croaker Optimization Algorithm

Swarm intelligence optimization algorithms have become an important research direction in the field of optimization in recent years. Their core idea is to solve complex optimization problems by simulating the collective behavior of groups in nature. This paper proposes a Yellow Croaker Optimization Algorithm (YCOA) based on the foraging, swimming, and cooperative behaviors of yellow croakers. The algorithm achieves a balance between global search and local exploitation through dynamic position updates of individuals in the search space, local perturbations, adaptive weight adjustment, group cooperation, and an elite-guided mechanism. This paper details the algorithm's individual representation, group initialization, position update strategy, energy and cooperation mechanism, convergence criteria, and mathematical formulas, and analyzes the algorithm's characteristics, advantages, and potential application scenarios. This algorithm is suitable for continuous optimization, constrained optimization, and complex combinatorial optimization problems.