A new Newton metaheuristic algorithm for discrete performance-based design optimization of steel moment frames. (1st July 2020)

• Record Type: Journal Article
• Authors: Gholizadeh, Saeed; Danesh, Masood; Gheyratmand, Changiz
• Is Part Of: Computers & structures. Volume 234(2020)
• Publication Date: 2020-07-01
• Subjects: Discrete optimization -- Metaheuristic -- Newton algorithm -- Performance-based design -- Steel moment frame -- Statistical test; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2020.106250
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: A new metaheuristic is proposed for discrete structural optimization problems. An innovative scheme is used to update particles position based on Newton's method. A dynamic method is used to provide a balance between exploration and exploitation. Statistical tests are applied to analyze the behavior of metaheuristic algorithms. The proposed new algorithm outperformed some recent metaheuristics in literature. Abstract: In this paper a new and efficient metaheuristic algorithm is proposed for discrete performance-based seismic design optimization of steel moment frames. The proposed metaheuristic uses the Newton gradient-based method as its updating scheme in a population-based framework and therefore it is termed as Newton Metaheuristic Algorithm (NMA). In order to enable the NMA to effectively explore the discrete design space, a term containing the best solution found is added to the basic updating rule of the algorithm. In addition, a simple and efficient method is proposed in order to establish a balance between local and global search abilities of the proposed algorithm. The efficiency of the NMA is illustrated by presenting two benchmark discrete truss optimization problems. Moreover, three steel moment frames are optimized in the framework of performance-based design by the NMA and the results are compared with those of some recent metaheuristics. The performance of the algorithms is analyzed using statistical parametric and non-parametric tests indicating that NMA outperforms the other algorithms in literature.