Locally informed gravitational search algorithm with hierarchical topological structure

• Published in: Engineering applications of artificial intelligence Vol. 123; p. 106236
• Main Authors: Xiao, Leyi, Fan, Chaodong, Ai, Zhaoyang, Lin, Jie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Gravitational constant; Gravitational search algorithm; Hierarchical topology; Neighborhood learning; Self-adaptive mechanism; Hierarchical topology; Gravitational search algorithm; Gravitational constant; Neighborhood learning; Self-adaptive mechanism
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.106236

Recently, gravitational search algorithm (GSA) has been successfully applied to solve various optimization problems. However, GSA tends to fall into local optimum because it ignores the environmental heterogeneity of agents. Therefore, locally informed gravitational search algorithm (LIGSA) based on neighborhood structure is proposed to balance exploration and exploitation. However, LIGSA ignores the differences in the evolutionary states between agents in the same neighborhood, and also ignores the differences in the evolutionary states between neighbors, which affects the performance of the algorithm. Therefore, a hierarchical locally informed gravitational search algorithm (HLIGSA) is proposed in this paper. This algorithm designs a hierarchical topology. In the lower layer, there are several non-overlapping neighborhoods, which constitute the whole population. Each agent in the neighborhood adaptively adjusts the gravitational constant according to its evolutionary state, so as to fully search the region of the neighborhood. The upper layer is a population composed of the best agents in each neighborhood, which performs gravity strategy or merging strategy for those neighborhoods that have lost the evolutionary capability, so as to balance the algorithm’s exploration and exploitation. The two layers work together to complete the entire search process. Experimental results show that HLIGSA outperforms many variants of GSA and many current state-of-the-art heuristic algorithms.