Ma Yunpeng, Chang Chang, Lin Zehua, Zhang Xinxin, Song Jiancai, Chen Lei
School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134.
College of Science, Tianjin University of Commerce, Beichen, Tianjin 300134.
Math Biosci Eng. 2023 Jan;20(1):93-127. doi: 10.3934/mbe.2023006. Epub 2022 Sep 29.
Marine Predators Algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Lévy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.
海洋捕食者算法（MPA）是一种新提出的受自然启发的元启发式算法，它基于海洋捕食者的莱维飞行和布朗运动提出。自MPA提出以来，它已在许多领域成功应用。然而，它存在一些缺点，如容易陷入局部最优和早熟收敛。为了平衡MPA的开发和探索能力，本文提出了一种与教学学习机制相结合的改进海洋捕食者算法，即MTLMPA。与MPA相比，所提出的MTLMPA有两个亮点。首先，在MPA的第一阶段引入了一种教学机制，以提高全局搜索能力。其次，在MPA的第三阶段引入了一种新颖的学习机制，以提高捕食者与猎物之间的相遇概率并避免早熟收敛。MTLMPA通过23个基准数值测试函数和29个CEC - 2017测试函数进行了验证。实验结果表明，与几种最先进的启发式优化算法相比，MTLMPA更具竞争力。
