Chaotic grasshopper optimization algorithm for global optimization

• Published in: Neural computing & applications Vol. 31; no. 8; pp. 4385 - 4405
• Main Authors: Arora, Sankalap, Anand, Priyanka
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chaos theory; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Global optimization; Heuristic methods; Image Processing and Computer Vision; Optimization algorithms; Original Article; Probability and Statistics in Computer Science; Swarming; Grasshopper optimization algorithm; Multimodal function; Chaotic maps; Global optimization problem
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3343-2

Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.