Binary Comprehensive Learning Particle Swarm Optimization Approach for Optimal Design of Nonlinear Steel Structures with Standard Sizes

Abstract

This paper proposes the binary comprehensive learning particle swarm optimization (BCLPSO) method to determine the optimal design for nonlinear steel structures, adopting standard member sizes. The design complies with the AISC-LRFD standard specifications. Moreover, the sizes and layouts of cross-brace members, appended to the steel frames, are simultaneously optimized. Processing this design is as challenging as directly solving the nonlinear integer programming problem, where any solution approaches are often trapped into local optimal pitfalls or even do not converge within finite times. Herein, the BCLPSO method incorporates not only a comprehensive learning technique but also adopts a decoding process for discrete binary variables. The former ascertains the cross-positions among the sets of best swarm particles at each dimensional space. The latter converts design variables into binary bit-strings. This practice ensures that local optimal searches and premature termination during optimization can be overcome. The influence of an inertial weight parameter on the BCLPSO approach is investigated, where the value of 0.98 is recommended. The accuracy and robustness of the proposed method are illustrated through several benchmarks and practical structural designs. These indicate that the lowest minimum total design weight (some 3% reduction as compared to the benchmark) can be achieved of about 40% lower than the total number of analyses involved.

Keywords

• metaheuristic algorithms
• particle swarm optimization
• binary comprehensive learning
• nonlinear geometry
• steel structures