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

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