Optimum Performance‐Based Seismic Design Using a Hybrid Optimization Algorithm

Abstract

A hybrid optimization method is presented to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized. The algorithm is based on the concepts of the charged system search in which each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Comparison of the results of the hybrid algorithm with those of other metaheuristic algorithms shows the efficiency of the hybrid algorithm.

Experimental and finite element study of optimal designed steel corrugated web beams

“…Among these restricted studies of the FFA, the technique was effectively used by Gandomi et al (2011) for solving mixed continuous structural optimization problems. The optimum design of tower-shaped structures using an adaptive version of the FFA method was done by Talatahari et al (2014). After that, an enhanced FFA method is used for solution of mixed variable structural optimization problems by Brajevic and Ignjatovic (2015).…”

Performance-Based Seismic Design of Steel Frames Utilizing Colliding Bodies Algorithm

“…Two building frameworks are selected for seismic optimum design using the metaheuristic algorithm [ 15 – 17 ]. These frames have previously been used to illustrate the pushover analysis technique by Hasan et al [ 21 ] and Talatahari [ 16 ].…”

“…The optimum results for the CBO, a hybrid CSS [ 17 ], PACO [ 16 ], PSO [ 15 ], ACO [ 15 ], and GA [ 15 ] metaheuristic algorithm are presented in Table 1 . The new algorithm as well as the CSS and PSACO needs 4500 analyses to reach a convergence while 3900, 6800, and 8500 analyses are required by the ACO, GA, and PSO.…”

“…Also, various approaches were developed using sequential approximate programming concept [ 13 , 14 ], the optimality criteria (OC) algorithm [ 15 ], and a line search strategy [ 15 ]. The metaheuristic algorithms such genetic algorithm (GA) as well as ant colony optimization (ACO) [ 15 ], particle swarm optimization (PSO) [ 16 ], and charged system search (CSS) [ 17 ] (developed by Kaveh and Talatahari [ 18 ]) are also some other variants which were utilized for PBDO problems.…”