Genetic algorithm using probabilistic-based natural selections and dynamic mutation ranges in optimizing precast beams

Tien Dat Pham, Won Kee Hong

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number106681
JournalComputers and Structures
Volume258
DOIs
Publication statusPublished - 1 Jan 2022

Keywords

  • American standard
  • Design charts
  • Dynamic mutation ranges
  • Genetic algorithm
  • Precast concrete
  • Probabilistic natural selection

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