Marine Predator Algorithm (MPA) is an optimization algorithm inspired by the behavior of predator and prey to catch their own food. MPA is simple and easy to implement. To further improve the performance of MPA, this paper proposes a Multigroup Marine Predator Algorithm (MGMPA). The multigroup mechanism is to divide the initial population into several independent groups. These groups generate the top predator and the Elite matrix based on different strategies and share information after a fixed iteration. Above strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. To verify its performance, the paper compares MGMPA with some classic algorithms such as Particle Swarm Optimization (PSO), Parallel Particle Swarm Optimization (PPSO), Slap Swarm Algorithm (SSA), and Marine Predator Algorithm (MPA). In addition, the proposed MGMPA is also applied to solve Economic Load Dispatch problem (ELD). The experimental results show that the proposed MGMPA has significant advantages under the CEC2013 suite and obtains the minimum cost of power system operation and the maximum economic benefits in the application.

中文翻译：

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一种多群海洋捕食者算法及其在电力系统经济负荷调度中的应用

海洋捕食者算法 (MPA) 是一种优化算法，其灵感来自捕食者和猎物捕捉自己食物的行为。MPA 简单且易于实施。为了进一步提高MPA的性能，本文提出了一种多群海洋捕食者算法（MGMPA）。多组机制是将初始种群分成几个独立的组。这些群体根据不同的策略生成顶级捕食者和精英矩阵，并在固定迭代后共享信息。以上策略包括同组最大值、同组平均值、不同组最大​​值和不同组平均值。为了验证其性能，本文将 MGMPA 与一些经典算法如粒子群优化（PSO）、并行粒子群优化（PPSO）、拍击群算法 (SSA) 和海洋捕食者算法 (MPA)。此外，所提出的 MGMPA 也适用于解决经济负荷调度问题 (ELD)。实验结果表明，所提出的MGMPA在CEC2013套件下具有显着优势，在应用中获得了电力系统运行成本最小和经济效益最大化。