Introduction
Online data-driven evolutionary multiobjective optimization
- Choice of EMO algorithm: The chosen EMO algorithm is a foundation of an online data-driven EMO algorithm, which significantly affects its performance.
- Choice of surrogate model: The quality of the chosen surrogate model determines whether the evolutionary search can be corrected guided. To improve the robustness of surrogate models, multiple models can be used as an ensemble. Furthermore, surrogate models can approximate the objectives, aggregation functions, performance indicators, and selection for multiobjective optimization problems.
- Choice of online data: The chosen online data can efficiently and economically improve the surrogate models and benefit the following optimization search. Different online data sampling strategies would result in different performance of online data-driven EMO algorithms.
Test problems
- DDMOP1: This problem is a vehicle performance optimization problem, termed car cab design, which has 11 decision variables and 9 objectives. The decision variables include the dimensions of the car body and bounds on nature frequencies, e.g., thickness of B-Pillar inner, thickness of floor side inner, thickness of door beam, and barrier height. Meanwhile, the nine objectives characterize the performance of the car cab, e.g., weight of the car, fuel economy, acceleration time, road noise at different speed, and roominess of the car.
- DDMOP2: This problem aims at structural optimization of the frontal structure of vehicles for crashworthiness, which involves 5 decision variables and 3 objectives. The decision variables include the thickness of five reinforced members around the frontal structure. Meanwhile, the mass of vehicle, deceleration during the full-frontal crash (which is proportional to biomechanical injuries caused to the occupants), and toe board intrusion in the offset-frontal crash (which accounts for the structural integrity of the vehicle) are taken as objectives, which are to be minimized.
- DDMOP3: This problem is an LTLCL switching ripple suppressor with two resonant branches, which includes 6 decision variables and 3 objectives. This switching ripple suppressor is able to achieve zero impedance at two different frequencies. The decision variables are the design parameters of the electronic components, e.g., capacitors, inductors, and resistors. Meanwhile, the objectives of this problem involve the total cost of the inductors (which is proportional to the consume of the copper and economic cost) and the harmonics attenuations at two different resonant frequencies (which are related to the performance of the designed switching ripple suppressor).
- DDMOP4: This problem is also an LTLCL switching ripple suppressor but with nine resonant branches, including 13 decision variables and 10 objectives. This switching ripple suppressor is able to achieve zero impedance at nine different frequencies. The decision variables are the design parameters of the electronic components, e.g., capacitors, inductors, and resistors. Meanwhile, the objectives of this problem involve the total cost of the inductors and the harmonics attenuations at nine different resonant frequencies.
- DDMOP5: This problem is a reactive power optimization problem with 14 buses, which involves 11 decision variables and 3 objectives. The decision variables include the dimensions of the system conditions, e.g., active power of the generators, initial values of the voltage, and per-unit values of the parallel capacitor and susceptance. Meanwhile, the five objectives characterize the performance of the power system, e.g., active power loss, voltage deviation, reciprocal of the voltage stability margin, generation cost, and emission of the power system.
- DDMOP6: This problem is a portfolio optimization problem, which has 10 decision variables and 2 objectives. The data consist of 10 assets with the closing prices in 100 min. Each decision variable indicates the investment proportion on an asset. The first objective denotes the overall return, and the second objective denotes the financial risk according to the modern portfolio theory.
- DDMOP7: This problem is a neural network training problem, which has 17 decision variables and 2 objectives. The training data consist of 690 samples with 14 features and 2 classes. Each decision variable indicates a weight of the neural network with a size of 14 \(\times \) 1 \(\times \) 1. The first objective denotes the complexity of the network (i.e., the ratio of nonzero weights), and the second objective denotes the classification error rate of the neural network.
General shape of the approximate Pareto front
Software platform information
Comparative study
Compared algorithms
Experimental settings
Performance indicators
Problem | CSEA | K-RVEA | NSGA-II | ParEGO |
---|---|---|---|---|
DDMOP1 | 8.23E+07(4.52E+06) | 6.96E+07(2.91E+06) | 5.81E+07(2.00E+06) | 1.35E+08(8.67E+05) |
DDMOP2 | 6.14E+02(1.16E+01) | 6.08E+02(6.81E+00) | 5.68E+02(9.38E+00) | 6.58E+02(6.45E−01) |
DDMOP3 | 3.66E+02(2.10E+00) | 3.52E+02(6.52E−01) | 3.64E+02(1.63E+00) | 3.70E+02(1.45E+00) |
DDMOP4 | 4.33E+21(4.29E+19) | 4.18E+21(3.12E+19) | 4.01E+21(5.90E+19) | 4.48E+21(1.37E+19) |
DDMOP5 | 2.00E−02(3.66E−18) | 0.00E+00(0.00E+00) | 2.00E-02(3.66E−18) | 0.00E+00(0.00E+00) |
DDMOP6 | 0.00E+00(0.00E+00) | 0.00E+00(0.00E+00) | 0.00E+00(0.00E+00) | 0.00E+00(0.00E+00) |
DDMOP7 | 3.00E−01(2.21E−02) | 2.70E−01(2.26E−02) | 2.70E−01(1.49E−02) | 0.00E+00(0.00E+00) |
Results
Computation time
Problem | CSEA | K-RVEA | NSGA-II | ParEGO |
---|---|---|---|---|
DDMOP1 | 6.41E+03 | 6.48E+03 | 6.40E+03 | 6.75E+03 |
DDMOP2 | 5.73E+03 | 5.72E+03 | 5.70E+03 | 6.19E+03 |
DDMOP3 | 5.63E+03 | 5.62E+03 | 5.60E+03 | 5.92E+03 |
DDMOP4 | 5.19E+03 | 5.28E+03 | 5.10E+03 | 5.10E+03 |
DDMOP5 | 5.99E+03 | 6.04E+03 | 5.92E+03 | 6.78E+03 |
DDMOP6 | 4.39E+03 | 4.38E+03 | 4.35E+03 | 4.58E+03 |
DDMOP7 | 4.05E+03 | 3.88E+03 | 3.74E+03 | 4.91E+03 |