An accelerated Cuckoo Search algorithm using Tabu Search for optimum design of two dimensional steel frames

Cuckoo search Optimization algorithm (CS) has been proposed as a new swarm intelligence-based technique which was conceptualized based on Cuckoo birds colony and the Lévy flight behavior. This paper transplants unique mechanisms of Tabu search method to CS algorithm and proposes the hybrid approach of these methods, namely Accelerated Cuckoo Search algorithm based on Tabu search (ACST) algorithm. This hybridization can be benefited for optimum design of steel frames because of special search space related to this type of optimization problems. The objective is to obtain minimum weight frame through suitable selection of sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes under strength and serviceability constraints. The algorithm is experimentally validated on three small, moderate and large benchmark engineering optimization two dimensional steel frames. Its high performance is confirmed by comparing with some well-known meta-heuristic methods and original CS. Numerical results of frame optimization problems reveal that the yielded optimum designs of ACST significantly surpass those of CS in all cases in terms of convergence in return for a slight loss of accuracy. Also it is showed that the ACST like the original CS is comparable in respect of other well-known methods taken from optimization literature