In recent decades, the demand for optimization techniques has grown due to rising complexity in real-world problems. Hence, this work introduces the Hyperbolic Sine Optimizer (HSO), an innovative metaheuristic specifically designed for scientific optimization. Unlike conventional approaches, HSO takes a unique approach by engaging individual members of the population, ensuring a comprehensive exploration of solution spaces. Employing distinctive exploration and exploitation phases, coupled with hyperbolic \(sinh\) function convergence, the optimizer enhances speed, simplify parameter adjustment, alleviates slow convergence, and demonstrates efficiency in high-dimensional optimization. This approach is designed to tackle optimization challenges and enhance adaptability in unpredictable real-world scenarios. The evaluation of HSO's performance unfolds through four distinct testing phases. Initially, a set of 65 widely recognized benchmark functions is employed. These functions cover both unimodal and multi-modal varieties across dimensions of 30, 100, 500, and 1000, including fixed-dimensional functions, to comprehensively assess the exploration, exploitation, local optima avoidance, and convergence capabilities of the proposed algorithm. The results of the HSO algorithm are then compared to those of 15 state-of-the-art metaheuristic algorithms and 8 recently published algorithms. Secondly, HSO's performance is assessed in comparison with the benchmark suite from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC). This suite includes 15 benchmark functions for CEC-2015 and an additional 30 benchmark functions for CEC-2017. During the third phase, HSO tackles seven real-world classical engineering design problems by addressing both the constrained and unconstrained optimization challenges of IEEE CEC-2020. Finally, HSO undertakes training for a multilayer perceptron, utilizing four distinct datasets. To qualitatively assess HSO's performance, two statistical analyses—the Friedman and T tests—are employed. The findings of HSO showcase its adaptability and effectiveness as a high-performing optimizer in engineering optimization challenges. Note that the source code of the HSO algorithm are publicly accessible via https://github.com/Shivankur07/Hyperbolic-Sine-Optimizer.git.

近几十年来，由于现实世界问题的复杂性不断增加，对优化技术的需求不断增长。因此，这项工作引入了双曲正弦优化器（HSO），这是一种专为科学优化而设计的创新元启发式算法。与传统方法不同，HSO 采用独特的方法，让个体成员参与进来，确保对解决方案空间进行全面探索。采用独特的探索和利用阶段，再加上双曲\(sinh\)函数收敛，优化器提高了速度，简化了参数调整，缓解了收敛缓慢的问题，并展示了高维优化的效率。这种方法旨在解决优化挑战并增强在不可预测的现实场景中的适应性。HSO 性能的评估通过四个不同的测试阶段展开。最初，采用了一组 65 个广泛认可的基准函数。这些函数涵盖了30、100、500和1000维度的单峰和多峰品种，包括固定维度函数，以全面评估所提出算法的探索、利用、局部最优避免和收敛能力。然后将 HSO 算法的结果与 15 种最先进的元启发式算法和 8 种最近发布的算法的结果进行比较。其次，HSO 的性能是通过与电气和电子工程师协会 (IEEE) 进化计算大会 (CEC) 的基准套件进行比较来评估的。该套件包括 15 个 CEC-2015 基准测试函数和另外 30 个 CEC-2017 基准测试函数。在第三阶段，HSO 通过解决 IEEE CEC-2020 的约束和无约束优化挑战来解决七个现实世界的经典工程设计问题。最后，HSO 利用四个不同的数据集对多层感知器进行训练。为了定性评估 HSO 的性能，采用了两种统计分析——弗里德曼检验和T检验。HSO 的研究结果展示了其作为高性能优化器应对工程优化挑战的适应性和有效性。请注意，HSO 算法的源代码可通过 https://github.com/Shivankur07/Hyperbolic-Sine-Optimizer.git 公开访问。