Tree Growth Algorithm (TGA): A novel approach for solving optimization problems. (June 2018)

• Record Type: Journal Article
• Authors: Cheraghalipour, Armin; Hajiaghaei-Keshteli, Mostafa; Paydar, Mohammad Mahdi
• Is Part Of: Engineering applications of artificial intelligence. Volume 72(2018)
• Page Start: 393
• Page End: 414
• Publication Date: 2018-06
• Subjects: Trees growth algorithm -- Combinatorial optimization -- Metaheuristic algorithm -- Approximate methods; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.04.021
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Nowadays, most of real world problems are complex and hence they cannot be solved by exact methods. So generally, we have to utilize approximate methods such as metaheuristics. So far, a significant amount of metaheuristic algorithms are proposed which are different with together in algorithm motivation and steps. Similarly, this paper presents the Tree Growth Algorithm (TGA) as a novel method with different approach to address optimization tasks. The proposed algorithm is inspired by trees competition for acquiring light and foods. Diversification and intensification phases and their tradeoff are detailed in the paper. Besides, the proposed algorithm is verified by using both mathematical and engineering benchmarks commonly used in this research area. This new approach in metaheuristic is compared and studied with well-known optimization algorithms and the comparison of TGA with standard versions of these employed algorithms showed the superiority of TGA in these problems. Also, convergence analysis and significance tests via some nonparametric technique are employed to confirm efficiency and robustness of the TGA. According to the results of conducted tests, the TGA can be considered as a successful metaheuristic and suitable for optimization problems. Therefore, the main purpose of providing this algorithm is achieving to better results, especially in continuous problems, due to the natural behavior inspired by trees. Highlights: A novel metaheuristic algorithm called TGA inspired by trees behavior is developed. A comprehensive literature review of metaheuristics have been proposed. Taguchi method is utilized to tune the parameters of the algorithms. TGA is evaluated using performance evaluation and statistical analysis. TGA is measured by thirty benchmark functions and five engineering problems.