Chaos-Mapped Grasshopper Optimization Algorithm

With the escalating intricacy of optimization tasks, conventional optimization techniques often struggle with drawbacks like getting ensnared in local minima and experiencing sluggish convergence. To surmount these hurdles, this research introduces an innovative Chaos-Mapped Grasshopper Optimization Algorithm (CGOA). The core objective of CGOA is to fortify the algorithm's exploration prowess and global search efficiency through the integration of chaotic dynamics. The Grasshopper Optimization Algorithm operates by emulating the foraging patterns of grasshopper colonies to execute optimization. Meanwhile, chaotic mapping is employed to diversify the initial solution set and steer the search trajectory more effectively, thereby improving the overall performance of the optimization process.