The paper presents an algorithm for detecting both overlapping and hierarchical community structures in complex networks. The method is based on local optimization of a fitness function, which reveals community structure through peaks in the fitness histogram. The resolution parameter allows for exploring different hierarchical levels of organization. The algorithm is tested on both real and artificial networks, showing excellent results. The method is flexible and can be adapted to various network types, including weighted and directed networks. The authors also discuss the statistical properties of community sizes and the comparison of different partitions using normalized mutual information. The paper concludes by highlighting the algorithm's potential for studying large networks and its ability to quantify the participation of overlapping nodes in their communities.The paper presents an algorithm for detecting both overlapping and hierarchical community structures in complex networks. The method is based on local optimization of a fitness function, which reveals community structure through peaks in the fitness histogram. The resolution parameter allows for exploring different hierarchical levels of organization. The algorithm is tested on both real and artificial networks, showing excellent results. The method is flexible and can be adapted to various network types, including weighted and directed networks. The authors also discuss the statistical properties of community sizes and the comparison of different partitions using normalized mutual information. The paper concludes by highlighting the algorithm's potential for studying large networks and its ability to quantify the participation of overlapping nodes in their communities.