An Pigeon-Inspired Optimization Based on Complex-Valued Encoding
Limin Zhao · 2018
Pigeon-inspired optimization (PIO) is a new type of population-based swarm intelligence algorithm which can effectively solve combinatorial optimization problems. However, PIO is easy to trap into local optimum and presents weak global search ability. To overcome these problems, this paper proposes a new complex-valued encoding based pigeon-inspired optimization (CPIO) algorithm in which the real part and the imaginary part are updated simultaneously. Meanwhile, to help pigeons escape local optimum, we introduce a random perturbation strategy. To validate the effectiveness of CPIO, we conduct extensive experiments on ten benchmark functions. Experimental results show that the proposed improved algorithm can achieve better results in function testing.