Improved plant growth simulation and genetic hybrid algorithm (PGSA-GA) and its structural optimization. Issue 1 (5th March 2018)

• Record Type: Journal Article
• Authors: Shi, Kairong; Ruan, Zhijian; Jiang, Zhengrong; Lin, Quanpan; Wang, Long
• Is Part Of: Engineering computations. Volume 35:Issue 1(2018)
• Page Start: 268
• Page End: 286
• Publication Date: 2018-03-05
• Subjects: Plant growth simulation algorithm -- Elitist strategy -- Hybrid algorithm -- Intelligent variable step size -- Structural optimization; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-03-2017-0113
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and genetic hybrid algorithm (PGSA-GA), for solving structural optimization problems. Design/methodology/approach: PGSA-GA is based on PGSA and three improved strategies, namely, elitist strategy of morphactin concentration calculation, strategy of intelligent variable step size and strategy of initial growth point selection based on GA. After a detailed formulation and explanation of its implementation, PGSA-GA is verified using the examples of typical truss and single-layer lattice shell. Findings: Improved PGSA-GA was implemented and optimization was carried out for two typical optimization problems; then, a comparison was made between the PGSA-GA and other methods. The results show that the method proposed in the paper has the advantages of high efficiency and rapid convergence, which enable it to be used for the optimization of various types of steel structures. Originality/value: Through the examples of typical truss and single-layer lattice shell, it shows that the optimization efficiency and effect of PGSA-GA are better than those of other algorithms and methods, such as GA, secondary optimization method, etc. The results show that PGSA-GA is quite suitable for structural optimization.