Adolescent Identity Search Algorithm Based on Fast Search and Balance Optimization for Numerical and Engineering Design Problems

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 25
• Main Authors: Wang, Wentao, Liu, Hao, He, Quanqin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 01.08.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Brownian motion; Chebyshev approximation; Convergence; Design engineering; Design optimization; Engineering; Genetic algorithms; Heuristic; Iterative methods; Local optimization; Optimization algorithms; Population; Product design; Product development; R&D; Research & development; Role models; Search algorithms; Search methods; Social aspects; Teenagers
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/5692427

This paper proposed a fast convergence and balanced adolescent identity search algorithm (FCBAISA) for numerical and engineering design problems. The main contributions are as follows. Firstly, a hierarchical optimization strategy is proposed to balance the exploration and exploitation better. Secondly, a fast search strategy is proposed to avoid the local optimization and improve the accuracy of the algorithm; that is, the current optimal solution combines with the random disturbance of Brownian motion to guide other adolescents. Thirdly, the Chebyshev functional-link network (CFLN) is improved by recursive least squares estimation (RSLE), so as to find the optimal solution more effectively. Fourthly, the terminal bounce strategy is designed to avoid the algorithm falling into local optimization in the later stage of iteration. Fifthly, FCBAISA and comparison algorithms are tested by CEC2017 and CEC2022 benchmark functions, and the practical engineering problems are solved by algorithms above. The results show that FCBAISA is superior to other algorithms in all aspects and has high precision, fast convergence speed, and excellent performance.