The matlab code for SADE-AMSS
SADE-AMSS
------------------------------- Reference --------------------------------
H. Gu, H. Wang, and Y. Jin, Surrogate-Assisted Differential Evolution with Adaptive Multi-Subspace Search
for Large-Scale Expensive Optimization in IEEE Transcations on Evolutionary Computation.
------------------------------- Copyright --------------------------------
Copyright (c) 2022 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.
---------------------------- Parameter setting ---------------------------
maxd --- 100 --- Maximum number of variables at each subspace
Ns --- 200 --- Initial size of Arc
tsn --- 2*d --- The number of individuals in the training set
Np --- 10 --- The size of population
K --- 20 --- The maximum number of subspaces in a generation
Gm --- 5 --- The maximum iterations of subspace optimization
tes --- 50 --- A pre-set cutoff generation
tr --- 500 --- A pre-set cutoff generation
beta --- 2 --- The threshold for switching strategy

This code is written by Haoran Gu.
Email: [email protected]