- evaluator1 (input=1)
- solver1 (solver1.py)
- evaluator2 (input=001)
- solver2 (solver2.py)
- evaluator3 (latitude=035.682500&longitude=139.752778)
- solver3 (solver3.py)
- evaluator4 (latitude=035.682500&longitude=139.752778&year=01&month=01)
- solver4 (solver4.py)

Number of variables *D* = 5 (if possible).

**Left image is Pareto front.**

**Center image is Pareto front and random points.** Point size *N* = 200.

**Right image is Pareto front and grid points.** Point size *N* = 21^5.

Red points are Parto optimal solution.

Blue points are infeasible solution.

Grey points are feasible solution.

- BT(Benchmark MOP with bias feature)
- CF(Constrained benchmark MOP)
- Combinatorial MOPs
- DASCMOP(Difficulty-adjustable and scalable constrained benchmark MOP)
- Distance minimization problems
- DOC(Benchmark MOP with constraints in both decision and objective spaces)
- DTLZ(Benchmark MOP proposed by Deb, Thiele, Laumanns, and Zitzler)
- IMOP(Benchmark MOP with irregular Pareto front)
- LIRCMOP(Constrained benchmark MOP with large infeasible regions)
- LSMOP(Large-scale benchmark MOP)
- MaF(Modified 15 test problems for many-objective optimization)
- Benchmark MOP for Inverse Modeling - MOEA
- Benchmark MOP for MOEA/D-DE
- Benchmark MOP for MOEA/D-M2M
- Benchmark MOP for Regularity Model-based - MEDA
- MW(Constrained benchmark MOP proposed by Ma and Wang)
- SMOP(Benchmark MOP with sparse Pareto optimal solutions)
- TREE(The time-varying ratio error estimation problems)
- UF(Unconstrained benchmark MOP)
- VNT(Benchmark MOP proposed by Viennet)
- WFG(Benchmark MOP proposed by Walking Fish Group)
- ZDT(Benchmark MOP proposed by Zitzler, Deb, and Thiele)

Y. Tian, R. Cheng, X. Zhang, and Y. Jin, PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum], IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87.

I use PlatEMO. Some Problems directory files and Public directory files are same as old version of PlatEMO.

I hope visitors can help their visual understanding of multi-objective optimization problem.

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