Many fish species form groups to enhance their foraging efficiency and reproductive success. These groups operate as self-organized systems, meaning they exhibit cooperative behaviors without a central leader. One of the most intriguing examples of this is the hunting strategy of the Yellow Saddle Goatfish (Parupeneus cyclostomus), where the group splits into subgroups to cover the hunting area comprehensively. Each subgroup has fish that take on specific roles: chasers actively pursue prey, while blockers position themselves strategically to prevent escape. This chapter presents a computational model of this hunting strategy, reinterpreted as an optimization search strategy. We develop specialized computational operators to simulate this behavior, creating a new search approach that demonstrates enhanced accuracy and convergence in optimization tasks. Through benchmark function comparisons, this strategy outperforms several well-established optimization methods. Beyond theoretical application, the model also proves effective in solving real engineering optimization problems, underscoring its practical relevance and potential impact. The experimental results highlight the model's efficiency, precision, and robustness, establishing it as a reliable tool in the field of optimization.