The metaheuristic algorithms do not depend on the functional form when solving the optimization problem. They have strong adaptability and are widely used in many fields. Whale Optimization Algorithm (WOA) is a metaheuristic algorithm based on the social behavior of humpback whales. Compared with other metaheuristic algorithms, WOA shows better performance. However, when solving high dimensional optimization problems, WOA tends to fall into local optima and has slow convergence speed and low accuracy of solution. Aiming at these problems, a multi-population improved WOA (MIWOA) is proposed to improve the performance of WOA when tackling high dimensional optimization. First of all, the multi-population exploitation and exploration processes are introduced. The population is divided into a better group and a worse group. The better individuals are used to improve exploitation performance, while the poorer individuals are used to improve exploration performance. Secondly, the current optimal individual and weighted center are taken to improve the learning process, which enhances the exploration ability and convergence speed. Moreover an interpolation method is introduced to enhance the search ability in the vicinity of the current optimum and further improve the exploitation performance. Finally, a control parameter is used to balance the exploitation and exploration processes. In the experimental part, MIWOA is compared with several state-of-the-art algorithms on 30 high dimensional benchmark functions with dimensions ranging from 100 to 2000. Simulation results show that MIWOA has good performance in both high dimensional single-mode and multi-mode optimization problems. MIWOA is superior to other algorithms in solution accuracy, convergence speed, and execution time.

元启发式算法在求解优化问题时不依赖于函数形式。它们具有很强的适应性，被广泛应用于许多领域。Whale Optimization Algorithm (WOA) 是一种基于座头鲸社会行为的元启发式算法。与其他元启发式算法相比，WOA 表现出更好的性能。然而，在求解高维优化问题时，WOA容易陷入局部最优，收敛速度慢，求解精度低。针对这些问题，提出了一种多种群改进WOA（MIWOA）来提高WOA在处理高维优化时的性能。首先，介绍了多种群开发和探索过程。人口被分为更好的组和更差的组。较好的个体用于提高开发性能，而较差的个体用于提高探索性能。其次，采用当前最优个体和加权中心来改进学习过程，增强了探索能力和收敛速度。此外，还引入了插值方法，以增强当前最优值附近的搜索能力，进一步提高开发性能。最后，使用控制参数来平衡开发和探索过程。在实验部分，在 30 个维度从 100 到 2000 的高维基准函数上，将 MIWOA 与几种最先进的算法进行了比较。仿真结果表明，MIWOA在高维单模和多模优化问题上都具有良好的性能。MIWOA 在求解精度、收敛速度和执行时间方面优于其他算法。