In real-world optimization problems, there is a significant difference in function evaluation time between objectives, and these problems are defined as the problems with heterogeneous objectives. To utilize the latency of objectives, we propose a local correlation estimation based surrogate-assisted bi-objective evolutionary algorithm for problems with heterogeneous objectives. In the proposed algorithm, surrogate models are employed to approximate the objective functions. The proposed local correlation estimation (LCE) method is used to analyze the correlation between objectives within a local region, guiding the search direction for one objective while identifying promising solutions for the other objective.
If you found LCE-SAEA useful, we would be grateful if you cite the following reference:
Chenyan Gu, Handing Wang, A Local Correlation Estimation Surrogate-Assisted Bi-Objective Evolutionary Algorithm for Heterogeneous Objectives, Applied Soft Computing, vol.151, pp.111175, 2024.
Copyright (c) 2021 HandingWangXD Group. Permission is granted to copy and use this code for research, noncommercial purposes, provided this copyright notice is retained and the origin of the code is cited. The code is provided "as is" and without any warranties, express or implied.
Email: gugugcy@163.com