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Multi-objective optimization analysis by Non-dominated Sorting Genetic Algorithm (NSGA-II) 1 with Floating Point representation 23 (MATLAB R2007b - ).
└─NSGA_2_ver3 ├─Bench_mark │ └─進化計算パラメータ │ └─html ├─cores │ └─functions │ └─NSGA_2_functions └─save └─fig
[Step 1] Start GUI form
Open the “GUI.fig” from MATLAB.
[Step 2] Pre-setting
Edit the code for evaluation functions in "./cores/functions/NSGA_2_functions/evaluation_func.m".
Next, push the "Parameters" button and edit parameters, or edit the code for parameters in "./save/param_setting.m".
[Step 3] Start optimization
Push the “exe” button or execute the code in "./cores/exe.m", and wait until the finish of the analysis.
[Step 4] Restart optimization (if solutions do not converge at [Step 3])
Execute the code in "./cores/exe_func_restart.m".
[Step 5] Plot results
Push the “plot” button.
[Step 6] View plotted results
Results (figures and movie) plotted by [Step 4] are in "./save" directory.
Optimal solutions are in h_pop_vec{end}(pop_rank{1},:)
.
Pareto-front is plotted by plot3(f_vec(pop_rank{1},1),f_vec(pop_rank{1},2),f_vec(pop_rank{1},3),'ro')
MOP3 bench problem 4
where,
Footnotes
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K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6 (2) (2002) 182–197. doi:10.1109/4235.996017. ↩
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C. Su, A genetic algorithm approach employing floating point representation for economic dispatch of electric power, in: The International Congress on Modelling and Simulation 1997, Vol. 204, 1997, pp. 1444–1449. ↩
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Reducing the Power Consumption of a Shape Memory Alloy Wire Actuator Drive by Numerical Analysis and Experiment, IEEE/ASME Transactions on Mechatronics, Vol. 23, No. 4 (2018).
https://doi.org/10.1109/TMECH.2018.2836352 ↩ -
Veldhuizen, D.A.V. and Lamont, G.B., Multiobjective evolutionary algorithm test suites, Proceedings of the 1999 ACM symposium on Applied computing, February 1999. ↩