A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization

• Published in: Neural computing & applications Vol. 35; no. 35; pp. 24603 - 24620
• Main Authors: Geng, Junqi, Sun, Xianming, Wang, Haihua, Bu, Xianghai, Liu, Daohuan, Li, Fei, Zhao, Zengwu
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2023; Springer Nature B.V
• Subjects: Adaptive algorithms; Adaptive search techniques; Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Convergence; Data Mining and Knowledge Discovery; Exploitation; Global optimization; Heuristic methods; Image Processing and Computer Vision; Probability and Statistics in Computer Science; S.I.: Applications and Techniques in Cyber Intelligence (ATCI2022); Search algorithms; Special Issue on Applications and Techniques in Cyber Intelligence (ATCI2022); Sparrow search algorithm (SSA); Global optimization problems; Swarm-based algorithm; Metaheuristics; Single objective optimization
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-08207-7

A population-based metaheuristic algorithm that takes its cues from the foraging strategy of sparrows is called the sparrow search algorithm (SSA). While SSA is competitive when compared to other algorithms, it nevertheless has a propensity to carry out imbalanced exploitation and exploration and find the local optimum. Therefore, the modified adaptive sparrow search algorithm (MASSA), an SSA modification, is created to address these problems. To increase population variety, the MASSA uses a chaotic reverse learning technique. Second, to balance the exploitation and exploration capacities, a dynamic adaptive weight is added. In the end, an adaptive spiral search technique improves algorithm performance. Among 23 classical test functions, of which 13 are multidimensional and the other 10 are fixed dimensional, the best chaotic operator is found. It is proven that MASSA is superior. Simulation studies demonstrate that the MASSA described in this study is superior to previous algorithms in terms of stability, convergence speed, and convergence accuracy. Finally, a sample robot path planning problem is resolved using MASSA, and the experimental outcomes confirmed the viability and usefulness of MASSA.