Chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

黑猩猩优化算法（ChOA）是最近提出的元启发式算法。有趣的是，它模拟了黑猩猩的社会地位关系和狩猎行为。由于应用领域的灵活性和复杂性，研究人员对本机算法有更高的要求。本文提出了一种改进的黑猩猩优化算法（EChOA），以提高解的准确性。首先，使用高度破坏性的多项式突变来初始化总体，这为全局搜索提供了基础。接下来，相对于领导黑猩猩，计算具有最低社会地位的黑猩猩的斯皮尔曼等级相关系数。为了降低陷入局部最优的可能性，甲虫触角算子用于获得视觉能力的同时改善不太适合的黑猩猩。三种策略增强了对本机算法的探索和利用。为了验证功能优化性能，对EChOA的12个经典基准功能和15个CEC2017基准功能进行了全面分析。此外，三个工程设计问题和训练多层感知器也突出了EChOA的实用性。与ChOA和五种最新算法相比，统计结果表明EChOA具有很强的竞争能力和广阔的前景。EChOA的实用性还通过三个工程设计问题和训练多层感知器来强调。与ChOA和五种最新算法相比，统计结果表明EChOA具有很强的竞争能力和广阔的前景。EChOA的实用性还通过三个工程设计问题和训练多层感知器来强调。与ChOA和五种最新算法相比，统计结果表明EChOA具有很强的竞争能力和广阔的前景。