%% NOTES % This package contains the source codes of the paper % "Ruan, G., Minku, L. L., Menzel, S., Sendhoff, B., & Yao, X. (2019, December). When and how % to transfer knowledge in dynamic multi-objective optimisation. In 2019 IEEE Symposium Series % on Computational Intelligence (SSCI) (pp. 2034-2041). IEEE," % which is modified from the code of the paper % "Jiang M, Huang Z, Qiu L, et al. Transfer Learning based Dynamic Multiobjective Optimization Algorithms" % %% %

Benchmark functions

% % * <benchmark%20functions\html\getFunc.html getfunc.m>, returns benchmark functions of the paper % % ¡¶Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization¡· % %% %

Metrices

% % * <Metrics\html\IGD.html IGD>, returns the IGD value % * <Metrics\html\react.html react>, returns the react value % %% %

DMOOP solvers

% % There are the source codes of all three algorithms listed in the paper. % % * <Tr-MOPSO\html\Main.html Tr-MOPSO> % * <Tr-NSGA-II\html\Main.html Tr-NSGA-II> % * <Tr-RMMEDA\html\Main.html Tr-RMMEDA> % %% %

TruePOF

% % There are the python scripts for generating the true POF and the % generated true POF for the 12 benchmark functions listed in CEC 2015 Special Session and Competition. % %% %

Usage

% % Please add the folder TR-DMOEA to the Matlab path, then run the Main.m in any Tr-** folder for test. % % After testing, the calculated metrics at every moment will be stored in a new folder .\Results .