In this study, a new technique has been introduced by changing the convergence of the whale optimization algorithm, which has the principle of approaching its prey by following the pack leader strictly. For this, first of all, average position values of the swarm were obtained in each iteration. Later, when the "p" parameter, which is used to add randomness to the progress of the swarm members, is below a certain value, the swarm average was used for each individual to move to the new position. Thus, slow convergence and frequent falling to the local optimum which is considered to be the biggest shortcoming of the algorithm, has been eliminated. The distance of whales from each other and from prey was modeled as a fitness function and the Euclidean distance formula was used for this. A complex engineering problem was chosen to reveal the power of both the classical whale optimization algorithm and the algorithm that includes the proposed new technique. As a result, this new technique introduced has provided a 10 million times improvement in solving this complex engineering problem used in the control of serial robot manipulators.

在这项研究中，通过改变鲸鱼优化算法的收敛性引入了一种新技术，该算法的原则是严格跟随群首领队接近猎物。为此，首先，在每次迭代中获得群体的平均位置值。后来，当用于为群体成员的进步增加随机性的“p”参数低于某个值时，每个个体都使用群体平均值移动到新位置。从而消除了被认为是该算法最大缺点的收敛速度慢和频繁陷入局部最优的问题。鲸鱼彼此之间以及与猎物之间的距离被建模为适应度函数，并为此使用了欧几里得距离公式。选择了一个复杂的工程问题来揭示经典鲸鱼优化算法和包含提议的新技术的算法的强大功能。因此，引入的这项新技术在解决串行机器人机械手控制中使用的这个复杂工程问题方面提供了 1000 万倍的改进。