An Improved Gaussian Pigeon-inspired Optimization Algorithm
Jiahao He, Yanbin Liu, Boyi Chen, Chunlun Yi · 2019
An improved Gaussian Pigeon-inspired Optimization algorithm is proposed to maintain diversity and improve the accuracy of the global optimum. In the proposed algorithm, Gaussian mutation is used to maintain the diversity of exploration, and the judgment on global optimum is used to improve the accuracy of global optimum. The simulation results show that the improved Gaussian Pigeon-inspired Optimization algorithm preserves the diversity of early evolution to avoid premature convergence. In addition, the judgment on global optimum improves the accuracy of the global optimum. The entire algorithm shows excellent performance in global optimization and proved to be effective for solving multimodal and non-convex problems with higher dimensions.