Two-phase ESO and comprehensive learning PSO method for structural optimization with discrete steel sections. (May 2022)

• Record Type: Journal Article
• Authors: Van, Thu Huynh; Tangaramvong, Sawekchai; Limkatanyu, Suchart; Xuan, Hung Nguyen
• Is Part Of: Advances in engineering software. Volume 167(2022)
• Publication Date: 2022-05
• Subjects: Metaheuristic algorithm -- Evolutionary structural optimization -- Comprehensive learning -- Particle swarm optimization -- Discrete constraint -- Integer program; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103102
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: The method overcomes local optima pitfalls in discrete structural optimizations. First-phase ESO fast eliminates ineffective sections from initial design spaces. Smaller swarm populations are constructed on the reducing sets of section domains. Second-phase processes a comprehensive learning PSO to minimize the design weight. This enhances a standard PSO for the cooperative responses among swarm populations. Abstract: This paper proposes the novel two-phase metaheuristic method that combines the distinctive features provided by evolutionary structural optimization (ESO) and comprehensive learning particle swarm optimization (CLPSO) for the design of steel structures with standard sections. The approach overcomes the challenges associated with local optima pitfalls in processing the mixed integer nonlinear programming problem, and hence determines the accurate solutions at modest computing efforts. In essence, the first-phase ESO design incorporates the constraint relaxations to fast eliminate the ineffective (infeasible) sections from initial discrete variable domains. This advantageously provides a significant reduction in the search spaces performed in the second-phase CLPSO scheme. The comprehensive learning technique enhances a standard particle swarm optimization algorithm by enabling the cooperative responses among swarm populations. The learning probability function defines the comprehensive cross-positions between the sets of best particles constructed from the reducing sets of steel sections specified in the preliminary ESO phase leading to the computation of optimal solutions. Various benchmarks (subjected to infinite design combinations) illustrate robustness and accuracy of the proposed design method (viz., generating the smaller number of particles) as compared to conventional metaheuristic algorithms.