SUMMARY Optimal design of tall buildings, as large‐scale structures, is a rather difficult task. To efficiently achieve this task, the computational performance of the employed standard meta‐heuristic algorithms needs to be improved. One of the most popular meta‐heuristics is particle swarm optimization (PSO) algorithm. The main aim of the present study is to propose a modified PSO (MPSO) algorithm for optimization of tall steel buildings. In order to achieve this purpose, PSO is sequentially utilized in a multi‐stage scheme where in each stage an initial swarm is generated on the basis of the information derived from the results of previous stages. Two large‐scale examples are presented to investigate the efficiency of the proposed MPSO. The numerical results demonstrate the computational advantages of the MPSO algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

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In the present paper, performance-based design of steel moment-resisting frames (SMRFs) is implemented to minimize total cost of the structures. The total cost is summation of the initial construction cost and the seismic damage cost in operational lifetime of the structures subjected to seismic loading. In order to evaluate the seismic damage cost, Park-Ang damage index (DI), as one of the most realistic measures of structural seismic damage, is utilized. To calculate the DI, nonlinear time-history response of the structure needs to be evaluated during the optimization process. As the computational burden of the process is very high, neural network techniques are utilized to predict the required nonlinear time-history structural responses. As the design constraints, besides the drift checks at immediate occupancy and collapse prevention performance levels, the global DI is also checked at collapse prevention level to control the amount of seismic damage. In order to achieve the optimization task, a sequential enhanced colliding bodies optimization II is proposed. Numerical studies are conducted to demonstrate the efficiency of the proposed methodology involving 2 illustrative examples of a 6-story SMRF and a 12-story SMRF.Optimization algorithms can be classified into two main groups: gradient-based algorithms and metaheuristics. Due to the drawbacks of gradient-based algorithms in tackling complex problems such as PBD optimization problems, the metaheuristics have been developed to resolve the difficulties of these algorithms, and nowadays, they are much popular in the field of structural optimization. Because metaheuristics are independent of gradient computation, they have more chance to find the global optimum compared with gradient-based algorithms. During the last decade, many metaheuristics have been proposed based on natural metaphors and some of them have been applied to solve complex structural optimization problems. [8][9][10] However, their slow convergence rate and the need for excessive call functions are the main shortcoming of metaheuristics. Especially in the case of PBD optimization of structures, computational burden is prohibitively high where the nonlinear structural responses are required at performance levels during the optimization process. In order to address this critical issue, an efficient approximation tool should be utilized and the best candidate for this purpose is neural network (NN) technique. Nowadays, NNs are considered as

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