Adaptive Weighted Strategy Based Integrated Surrogate Models for Multiobjective Evolutionary Algorithm

Table 5

Comparison of hyper-volume (HV) for different algorithms.

Problems	ParEGO	MOEA/D-EGO	K-RVEA	SMS-EGO	CSEA	Proposed
ZDTI	2.0311e − 1 (1.29e − 1)−	3.4343e − 1 (2.33e − 1)−	8.9466e − 2 (7.60e − 2)−	1.0688e − 2 (1.28e + 1)−	6.3940e − 1 (2.27e + 1)−	6.4567e − 1 (1.48e − 2)
ZDT2	4.0968e − 2 (3.81e − 2)−	9.4252e − 2 (1.02e − 1)−	4.9670e − 3 (1.05e − 2)−	3.2323e + 0 (1.99e + 0)≈	5.4347e + 0 (2.27e + 0)−	3.2907e − 1 (8.19e − 2)
ZDT3	1.9277e − 1 (1.16e − 1)−	2.4279e − 1 (2.35e − 1)−	1.0257e − 1 (1.19e − 1)−	8.7589e − 1 (1.64e + 1)−	5.1495e + 1 (1.87e + 1)−	5.2323e − 1 (1.39e − 1)
DTLZ2	1.8637e − 1 (3.65e − 2)−	1.3774e − 1 (4.64e − 2)−	3.5085e − 1 (2.65e − 2)−	4.3203e − 1 (2.55e − 2)≈	4.8420e − 1 (1.88e − 2)≈	4.3094e − 1 (4.84e − 2)
DTLZ4	2.1018e − 1 (6.82e − 2)≈	8.5741e − 3 (1.49e − 2)−	1.2883e − 1 (1.17e − 1)−	7.1185e + 2 (4.05e + 1)−	2.1205e − 1 (1.18e − 1)≈	2.0978e − 1 (1.07e − 1)
DTLZ5	5.9209e − 2 (2.35e − 2)−	2.5012e − 2 (2.17e − 2)−	1.4107e − 1 (1.17e − 2)−	6.4259e − 1 (7.17e − 2)−	1.62 2e − 1 (6.93e − 3)≈	1.7894e − 1 (6.78e − 3)
DTLZ7	1.5591e − 1 (2.40e − 2)−	2.1222e − 1 (1.71e − 2)−	1.5166e − 1 (1.28e − 2)−	3.5429e − 1 (3.03e − 2)−	3.5002e − 1 (1.28e + 2)−	2.2066e − 1 (1.15e − 2)
+/−/≈	0/6/1	0/7/0	0/7/0	0/5/2	0/4/3	—