This study tenders a new nature-inspired metaheuristic algorithm (MA) based on the behavior of the Genghis Khan shark (GKS), called GKS optimizer (GKSO), which is used for numerical optimization and engineering design. The inspiration for GKSO comes from the predation and survival behavior of GKS, and the entire optimization process is achieved by simulating four different activities of GKS, including hunting (exploration), movement (exploitation), foraging (switch from exploration to exploitation), and self-protection mechanism. These operators are mimicked using various mathematical models to efficiently perform optimization tasks of agents in different regions of the search space. In an effort to validate this method's viability and superiority, an in-depth analysis of the proposed GKSO is carried out from both qualitative and quantitative perspectives. Qualitative analysis verifies that GKSO has good exploration and exploitation (ENE) capability. Simultaneously, GKSO is quantitatively analyzed with eight existing fish optimization algorithms and the other nine well-known MAs on CEC2019 and CEC2022, respectively. Among them, a series of experimental scenarios are conducted to validate the applicability and robustness of GKSO by exploring its performance for CEC2022 at different dimensions and maximum fitness evaluation quantity. Statistical results indicate that GKSO has a strong advantage in the competition between two different types of algorithms. Furthermore, five different kinds of real-world constrained optimization problems (OPs) in CEC2020 benchmark constrained optimization functions, including 50 engineering case suites, are selected to evaluate GKSO's performance and the other seven optimizers, further validating GKSO's extensive usefulness and validity in solving practical complex problems.

本研究提出了一种基于成吉思汗鲨 (GKS) 行为的新的受自然启发的元启发式算法 (MA)，称为 GKS 优化器 (GKSO)，用于数值优化和工程设计。GKSO的灵感来源于GKS的捕食和生存行为，整个优化过程是通过模拟GKS的四种不同活动来实现的，包括狩猎（探索）、运动（剥削）、觅食（从探索转向剥削）和自我保护机制。使用各种数学模型来模拟这些算子，以有效地执行搜索空间不同区域中代理的优化任务。为了验证该方法的可行性和优越性，从定性和定量的角度对所提出的 GKSO 进行了深入分析。定性分析验证了GKSO具有良好的勘探开发（ENE）能力。同时，GKSO 与八种现有鱼类进行定量分析分别在CEC2019和CEC2022上介绍了优化算法和其他九个著名的MA。其中，通过探索GKSO在不同维度和最大适应度评估量下对CEC2022的性能，进行了一系列实验场景来验证GKSO的适用性和鲁棒性。统计结果表明GKSO在两种不同类型算法的竞争中具有较强的优势。此外，选择CEC2020基准约束优化函数中的五种不同的现实世界约束优化问题（OP）（包括50个工程案例套件）来评估GKSO的性能和其他七个优化器，进一步验证了GKSO在解决实际问题方面的广泛实用性和有效性。复杂的问题。