Dynamic cuckoo search algorithm based on Taguchi opposition-based search

Abstract

The cuckoo search (CS) algorithm is a relatively new, nature-inspired intelligent algorithm that uses a whole updating and evaluation strategy to find solutions for continuous global optimisation problems. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploitation and exploration. These issues result from interference phenomena among dimensions that arise when solving multi-dimension function optimisation problems. To overcome these issues, we proposed an enhanced CS algorithm called dynamic CS with Taguchi opposition-based search (TOB-DCS) that employed two new strategies: Taguchi opposition-based search and dynamic evaluation. The Taguchi search strategy provided random generalised learning based on opposing relationships to enhance the exploration ability of the algorithm. The dynamic evaluation strategy reduced the number of function evaluations, and accelerated the convergence property. For this research, we conducted experiments on 22 classic benchmark functions, including unimodal, multi-modal and shifted test functions. Statistical comparisons of our experimental results showed that the proposed TOB-DCS algorithm made an appropriate trade-off between exploration and exploitation.