A novel PGSA–PSO hybrid algorithm for structural optimization. Issue 1 (7th August 2019)

• Record Type: Journal Article
• Authors: Jiang, Zhengrong; Lin, Quanpan; Shi, Kairong; Pan, Wenzhi
• Is Part Of: Engineering computations. Volume 37:Issue 1(2020)
• Page Start: 144
• Page End: 160
• Publication Date: 2019-08-07
• Subjects: Plant growth simulation algorithm -- Particle swarm optimization -- Hybrid algorithm -- Structural optimization -- Suspended dome structure; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-01-2019-0025
• 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 particle swarm optimization hybrid algorithm (PGSA–PSO hybrid algorithm), for solving structural optimization problems. Design/methodology/approach: To further enhance the optimization efficiency and precision of this algorithm, the optimization solution process of PGSA–PSO comprises two steps. First, an excellent initial growth point is selected by PSO. Then, the global optimal solution can be obtained quickly by PGSA and its improved strategy called growth space adjustment strategy. A typical mathematical example is provided to verify the capacity of the new hybrid algorithm to effectively improve the global search capability and search efficiency of PGSA. Moreover, PGSA–PSO is applied to the optimization design of a suspended dome structure. Findings: Through typical mathematical example, the improved strategy can improve the optimization efficiency of PGSA considerably, and an initial growth point that falls near the global optimal solution can be obtained. Through the optimization of the pre-stress of a suspended dome structure, compared with other methods, the hybrid algorithm is effective and feasible in structural optimization. Originality/value: Through the examples of suspended dome structure, it shows that the optimization efficiency and precision of PGSA–PSO are better than those of other algorithms and methods. PGSA–PSO is effective and feasible in structural optimization problems such as pre-stress optimization, size optimization, shape optimization and even topology optimization.