Hybridizing particle swarm and big bang-big crunch optimization methods to explore then exploit the design domain of large planar frame structures

Abstract

This paper presents a hybrid metaheuristic optimization method for large-scale frame structures that minimizes weight while satisfying strength and serviceability requirements. The proposed algorithm first explores then exploits (ETE) the search space by applying a hybridized particle swarm and big bang-big crunch (BB-BC) optimization technique that adjusts the influence of the local and global best designs on the selection of new candidate solutions. A discrete (stochastic) search scheme is then activated in the last stage to exploit the (local) search space near the global optimum. The method is successfully applied to three benchmark planar steel frame structures: (1) a 15-story three-bay frame, (2) a three-bay 24-story moment-resistant frame, and (3) a seven-bay 60-story building structure. The ETE approach produces optimum weights for the 15 and 24 story frame that outperform recently developed metaheuristic strategies. For the 60-story frame, optimum designs from ensemble of independent runs produce frame weights within 2% of results found using deterministic methods, with some only addressing serviceability (drift) requirements. The findings demonstrate how the proposed stochastic (local) search strategy performs minute alterations to the best design, while only permitting the creation of new designs capable of improving the (current) global best solution. ETE also appears to significantly enhance the exploitation capabilities of BB-BC method.