Orthogonally-designed adapted grasshopper optimization: A comprehensive analysis. (15th July 2020)

• Record Type: Journal Article
• Authors: Xu, Zhangze; Hu, Zhongyi; Heidari, Ali Asghar; Wang, Mingjing; Zhao, Xuehua; Chen, Huiling; Cai, Xueding
• Is Part Of: Expert systems with applications. Volume 150(2020)
• Publication Date: 2020-07-15
• Subjects: Grasshopper optimization -- Meta-heuristics -- Orthogonal learning -- Chaotic exploitation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113282
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: This paper proposes an improved variant of the grasshopper optimization algorithm. Orthogonal learning and chaos-based exploitative search are introduced. Extensive comparison using various datasets and benchmark problems are performed. A new feature selection model is established using the proposed method. Abstract: Grasshopper optimization algorithm (GOA) is a newly proposed meta-heuristic algorithm that simulates the biological habits of grasshopper seeking for food sources. Nonetheless, some shortcomings exist in the basic version of GOA. It may quickly drop into local optima and show slow convergence rates when facing some complex basins. In this work, an improved GOA is proposed to alleviate the core shortcomings of GOA and handle continuous optimization problems more efficiently. For this purpose, two strategies, including orthogonal learning and chaotic exploitation, are introduced into the conventional GOA to find a more stable trade-off between the exploration and exploitation cores. Adding orthogonal learning to GOA can enhance the diversity of agents, whereas a chaotic exploitation strategy can update the position of grasshoppers within a limited local region. To confirm the efficacy of GOA, we compared it with a variety of famous classical meta-heuristic algorithms performed on 30 IEEE CEC2017 benchmark functions. Also, it is applied to feature selection cases, and three structural design problems are employed to validate its efficacy in terms of different metrics. The experimental results illustrate that the above tactics can mitigate the deficiencies of GOA, and the improved variant can reach high-quality solutions for different problems.