Whale Optimization Algorithm (WOA) is a popular swarm-based algorithm with some spotted defects in its generated patterns during the searching phases. In this study, an enhanced WOA-based method is proposed in order to overcome the drawbacks of slow convergence speed and easy falling of WOA into the local optimum. The designed variant is called enhanced WOA (EWOA), which combines two strategies at the same time. First, a new communication mechanism (CM) is embedded into the basic WOA to promote the global optimal search ability and the exploitation tendency of the WOA. Then, the Biogeography-based Optimization (BBO) algorithm is partially utilized to harmonize the exploration and exploitation trends. A representative set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed EWOA. The experimental results show that the exploration ability, exploitation ability, state of the balance, and convergence style of the algorithm has been improved significantly. Based on results, the proposed EWOA is a promising and excellent algorithm, and it has achieved better solution quality and faster convergence rate compared with other most advanced algorithms.

鲸鱼优化算法（WOA）是一种基于群体的流行算法，在搜索阶段其生成的模式中有一些斑点缺陷。在这项研究中，提出了一种基于WOA的增强方法，以克服收敛速度慢和WOA容易陷入局部最优的缺点。设计的变体称为增强WOA（EWOA），它同时结合了两种策略。首先，将一种新的通信机制（CM）嵌入到基本WOA中，以促进WOA的全局最佳搜索能力和开发趋势。然后，部分利用了基于生物地理学的优化（BBO）算法来协调勘探和开发趋势。一组具有代表性的综合基准案例和三个工程案例被用来验证所提出的EWOA的优势。实验结果表明，该算法的探索能力，开发能力，平衡状态和收敛方式都有明显提高。基于结果，提出的EWOA是一种很有前途的优秀算法，与其他最先进的算法相比，它具有更高的解决方案质量和更快的收敛速度。