This paper explores the evaluation methodologies and practical applications of cluster region search and route planning algorithms. Various algorithms are thoroughly assessed through the establishment of an evaluation framework. This framework includes criteria such as platform physical capabilities, feasibility indicators, perceived effectiveness indicators, movement performance indicators, and multi-scenario adaptability. Novel algorithms are proposed to ad-dress diverse mission requirements and environmental conditions. These algorithms encompass approaches based on parallel scan lines, sensor configuration and multi-TSP, K-means clustering and feedback mechanism, coverage period and target probability density. Through simulation experiments, the efficacy of the evaluation framework is validated, providing insights to improve the performance and efficiency of cluster region search and route planning algorithms. This study comprehensively considers platform capabilities, target characteristics, and task requirements, offering theoretical foundations and practical advice for handling a variety of search tasks.