A Novel Cuckoo Search and its Performance Analysis

Inspired by the original cuckoo search and share individual historical optimal information, we present a novel cuckoo search (NCS) algorithm for solving the optimization problems. Through considering the individual historical optimal information, the NCS algorithm iteratively shares and delivers these valuable information between two near generation solutions in order to improve its search performance. We research on the constant, Normal and Uniform distribution random orientation coefficient combination strategies, and analysis the performance of the algorithm under different parameters. It is tested with a set of improved complex nonlinear combination continuous functions by comparing it with standard cuckoo search and ICSPSO. The experimental results have validated that the proposed NCS is efficient and reliable.