Genetic algorithm using probabilistic-based natural selections and dynamic mutation ranges in optimizing precast beams. (1st January 2022)

• Record Type: Journal Article
• Authors: Pham, Tien Dat; Hong, Won-Kee
• Is Part Of: Computers & structures. Volume 258(2022)
• Publication Date: 2022-01-01
• Subjects: Genetic algorithm -- Precast concrete -- American standard -- Probabilistic natural selection -- Dynamic mutation ranges -- Design charts; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106681
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: A genetic algorithm-based optimization process for the precast beams was provided. Mutation using dynamic ranges and probabilistic-based natural selections were introduced. Multiple design criteria can be implemented in the optimization processes. Quick and convenient design charts were provided using the proposed algorithm. Abstract: In this paper, optimal designs for precast beams were determined using a genetic algorithm. Two new features for enhancing genetic algorithms were developed in the present study, considerable improvements obtained by applying the proposed methods were shown in a parametric study. Firstly, probabilistic-based natural selection was introduced, selecting parental chromosomes for reproduction using inherited probabilities calculated by the ranking of each design among populations. Secondly, mutations using dynamic ranges were proposed, in which, the mutated parameters were varied between a dynamic range around the inherited values instead of being selected randomly. A dynamic range was expanded according to the convergencies of the objective indexes, allowing the application of high mutation rates without degrading searching efficiencies. Furthermore, genetic algorithm-based design charts were constructed, offering reasonable references for preliminary designs. Overall, the proposed procedures showed adequate applications in practical designs, improving the design efficiencies, and reducing human efforts.