Rocky area ecosystems, characterized by complex topography, diverse microenvironments, scarce resources, and dynamic changes, place high selective pressures and adaptability demands on organisms. Inspired by these ecosystems, this paper proposes an original Rocky Area Ecosystem Optimization (RAEO) algorithm. This algorithm leverages the microenvironmental characteristics of rocky areas, such as cracks, bare rock, and slopes, and incorporates hierarchical microenvironmental search, transient resource hopping, and dynamic environmental pressure regulation mechanisms to achieve an adaptive balance between local search and global exploration. Through mathematical modeling, it defines population initialization, fitness assessment, microenvironment weighting, local and global search iteration formulas, and a competitive elimination mechanism. The RAEO algorithm highlights the characteristics of rocky area ecosystems, achieving efficient search and achieving local optimality, offering a new approach to solving complex optimization problems.