The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies.

海洋捕食者算法是一种新的自然启发式元启发式算法，它模仿了海洋捕食者与猎物之间的生物相互作用。从文献中还可以看出，该算法可以解决许多现实世界中的优化问题，这使其成为研究人员的一种新的流行的优化技术。但是，海洋捕食者算法仍存在缺陷，例如无法以高生产率产生多样化的初始种群，缺乏快速逃避局部优化的能力以及缺乏广泛而广泛的搜索空间探索的能力。在本研究中，基于对立的学习方法，混沌图，种群的自适应以及勘探和开发阶段之间的切换，提出了该算法的开发版本。使用MATLAB环境对标准测试功能（包括CEC-06 2019测试）和基于PID控制的实际优化问题进行仿真，该问题应用于DC电动机以评估建议算法的性能。仿真结果与原始海洋捕食者算法和五个最新的优化算法（粒子群优化，蚱Grass优化算法，JAYA算法，平衡优化器算法，鲸鱼优化算法，差分搜索算法和联赛冠军算法）进行了比较。 。最终，仿真结果证明了该算法与其他算法相比具有更好的结果。仿真结果与原始海洋捕食者算法和五个最新的优化算法（粒子群优化，蚱Grass优化算法，JAYA算法，平衡优化器算法，鲸鱼优化算法，差分搜索算法和联赛冠军算法）进行了比较。 。最终，仿真结果证明了该算法与其他算法相比具有更好的结果。仿真结果与原始海洋捕食者算法和五个最新的优化算法（粒子群优化，蚱Grass优化算法，JAYA算法，平衡优化器算法，鲸鱼优化算法，差分搜索算法和联赛冠军算法）进行了比较。 。最终，仿真结果证明了该算法与其他算法相比具有更好的结果。