A novel grasshopper optimization algorithm based on swarm state difference and its application

Article type: Research Article

Authors: Lv, Zhaoming | Peng, Rong^{; *}

Affiliations: School of Computer Science, Wuhan University, Wuhan, China

Correspondence: [*] Corresponding author. Rong Peng, School of Computer Science, Wuhan University, Wuhan, China. E-mail: [email protected].

Abstract: The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population; the second stage merges the learned population into the gregarious population and updates each grasshopper; and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems.

Keywords: Meta-heuristic algorithms, grasshopper optimization algorithm, solitarious and gregarious states, chemotaxis operator, ontology matching

DOI: 10.3233/JIFS-212633

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5973-5986, 2022