Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dynamic opposite learning (IDOL) and an adaptive encircling prey stage, is proposed to overcome the problems. IDOL plays an important role in the initialization part and the algorithm iterative process of EWOA. By evaluating the optimal solution in the current population, IDOL can adaptively switch exploitation/exploration modes constructed by the DOL strategy and a modified search strategy, respectively. On the other hand, for the encircling prey stage of EWOA in the latter part of the iteration, an adaptive inertia weight strategy is introduced into this stage to adaptively adjust the prey’s position to avoid falling into local optima. Numerical experiments, with unimodal, multimodal, hybrid and composition benchmarks, and three typical engineering problems are utilized to evaluate the performance of EWOA. The proposed EWOA also evaluates against canonical WOA, three sub-variants of EWOA, three other common algorithms, three advanced algorithms and four advanced variants of WOA. Results indicate that according to Wilcoxon rank sum test and Friedman test, EWOA has balanced exploration and exploitation ability in coping with global optimization, and it has obvious advantages when compared with other state-of-the-art algorithms.

鲸鱼优化算法（WOA）作为一种新提出的基于群体的算法，已逐渐成为各个工程领域优化问题的流行方法。然而，WOA 存在探索与利用的平衡性差、收敛过早等问题。在本文中，提出了一种新的增强型 WOA（EWOA），它采用改进的动态反向学习（IDOL）和自适应包围猎物阶段来克服这些问题。IDOL 在 EWOA 的初始化部分和算法迭代过程中起着重要的作用。通过评估当前人群中的最优解，IDOL可以自适应地切换分别由DOL策略和修改后的搜索策略构建的开发/探索模式。另一方面，对于EWOA在迭代后期的包围猎物阶段，该阶段引入自适应惯性权重策略，自适应调整猎物位置，避免陷入局部最优。利用具有单峰、多峰、混合和组合基准的数值实验以及三个典型的工程问题来评估 EWOA 的性能。提议的 EWOA 还针对规范 WOA、EWOA 的三个子变体、其他三种常见算法、三种高级算法和 WOA 的四种高级变体进行评估。结果表明，根据 Wilcoxon 秩和检验和 Friedman 检验，EWOA 在应对全局优化方面具有均衡的探索和利用能力，与其他最先进的算法相比具有明显的优势。