Abstract
<title>Abstract</title> <p> This study proposes an efficient variant of Genghis Khan shark optimizer (GKSO), known as IGKSO, for optimizing static and dynamic engineering problems. Firstly, this method proposes two mutually constraining survival bonds to guide the survival rules of the Genghis Khan Shark (GKS) in the new four stages, which improves the algorithm's capacity to cast off local optima by integrating GKS's self-protection mechanism with other behavioral activities. In the new fourth stage, a light-dark interactive strategy is proposed and combined with self-protection mechanisms, consolidating the balance between exploration and exploitation (EE) of the algorithm. Moreover, an adaptive parameter <italic>M</italic> is designed to match the optimization environment of IGKSO, which enhances the search ability of GKS individuals when moving to the best position. After the new fourth stage, the strategy of fish aggregation devices (FADs) is introduced to change the behavior pattern of the GKS population, and new candidate schemes are generated to guide other GKSs towards better quality group organization, thereby improving the accuracy of the algorithm's solution. Statistical results on CEC2017 show that IGKSO achieves best results on 41.38% of functions, which has certain advantages compared with eleven other types of algorithms. Finally, IGKSO is validated on nine static engineering optimization problems (SEOPs) and two dynamic engineering optimization problems (DEOPs). The results show that IGKSO obtains optimal cost and statistical results on all simulation instances, and is a well-established and efficient method for solving such engineering optimization problems (EOPs) with complicated constraints. </p>