Improved NSGA-III Algorithm Based on Reference Point Selection Strategy
GENG Huantong1,2, DAI Zhongbin1, WANG Tianlei1, XU Ke1
1.School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044; 2.Nanjing Joint Center of Atmospheric Research, Meteorological Bureau of Jiangsu Province, Nanjing 210009
Abstract The traditional multi-objective evolutionary algorithm ignores distribution information of the population in the decision space and Pareto front shape of the optimization problem is not taken into account. To solve the problems, an improved NSGA-III algorithm based on reference point selection strategy is proposed. Firstly, according to the entropy thought in information theory, the entropy difference between two adjacent generations is calculated in line with the distribution characteristics of the population in the decision-making space, and the evolutionary status of the population is determined. Then, in the light of the distribution characteristics of the population in the target space, the importance of reference points is evaluated via statistical information of the number of the individuals associated with reference points. Finally, redundant and invalid reference points are removed according to the importance characteristics of reference points in the middle and late period of population evolution. Reserved reference points can adapt to the population size and Pareto frontier, and the selected reference points are exploited to guide the evolution direction of the population and accelerate the convergence and optimization efficiency. Experiments on test function sets indicate the significant advantages of the proposed algorithm in convergence and distribution.
Fund:Supported by Nanjing Joint Center of Atmospheric Research(No. NJCAR2018MS05), National Natural Science Foundation of China(No. 51977100)
Corresponding Authors:
GENG Huantong, Ph.D., professor. His research interests include computational intelligence, multi-objective optimization and meteorological data mining.
About author:: DAI Zhongbin, master student. His research interests include multi-objective optimization. WANG Tianlei, master student. His research interests include deep learning. XU Ke, master student. His research inte-rests include multi-objective optimization.
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2020, Vol. 33 
