Optimum design of steel skeletal structures using learned cuckoo search algorithm

In the last two decades many researchers have implemented various kinds of meta-heuristic algorithms in order to overcome the complex nature of the optimum design of structures. Recently a new population based meta-heuristic algorithm called the cuckoo search algorithm (CS) was developed which inspired by the behavior of some Cuckoo species in combination with the Lévy flight behavior of some birds and insects. This paper describes the modifications made to the CS algorithm based on considering of memory strategy. The improved version of CS entitled as Learned Cuckoo Search (LCS). In order to demonstrate the effectiveness and robustness of the learned version of CS, low-weight design and performance comparisons are made between the original CS, LCS and other algorithms for some benchmark steel structures. The results demonstratethat modifications improve the algorithm’s performance, especially in the aspect of computational effort

Keywords:

Metaheuristics , Cuckoo search , LCS , Optimal design , Steel structures