Configuration design of structures under dynamic constraints by a hybrid bat algorithm and teaching–learning based optimization

Abstract

Optimal design of steel trusses is an interesting task due to existence of many alternatives for their spatial configuration. Meanwhile, performance criteria have enforced their vibration to maintain within certain limits. The present work concerns implementation of frequency constraints as a common approach to implicitly satisfy such practical requirements. The problem objective is to minimize the cost of steel material by simultaneous size and geometry optimization of trusses. In this regard teaching–learning-based-optimization and bat-algorithm are hybridized via a new effective framework. The former acts as a global search engine, the latter provides search refinement to access higher quality solutions. Effectiveness and efficiency of the proposed hybrid method are evaluated using a number of bridge and space structures via comparison with literature works. Numerical simulations show enhanced effectiveness and less deviation about final optimum design as a result of implementing the proposed hybridization.