目标优化问题真实前沿"/>
绘制(动态)约束多目标优化问题真实前沿
编程了一种采点约束多目标优化基准测试问题(包括动态约束多目标优化基准测试问题)真实前沿的程序。点击获取程序
get_ptrue(name,f1_step,f1_up,f2_step,f2_up,t,flag1,flag2,flag3)
%% 参数意义
% name 测试函数名
% f1_step 目标1采点步长
% f1_up 目标1最大长度
% f2_step 目标2采点步长
% f2_up 目标2最大长度
% t 时间
% flag1 为1输出可行域和真实前沿
% flag2 为1输出前沿数据至文件夹
% flag3 为1在flag1为1的基础上输出无约束真实前沿
约束多目标测试函数[1]
CTP1
目的:使得无约束真实前沿一部分变的不可行
当J=2时
每个约束都是非线性隐函数,在边界上找到许多可行解可能是困难的
get_ptrue('CTP1',0.001,1,0.001,1.25,1,1,0,1)
CTP2-7
约束使得无约束的最优区域变的不可行,从而使得最优解是离散区域或离散的点的集合。
get_ptrue('CTP2',0.001,1,0.001,1.25,1,1,0,1)
get_ptrue('CTP3',0.001,1,0.001,1.25,1,1,0,1)
a是将连续可行区域过度到远离最优解区域的不连续可行区域
,
get_ptrue('CTP4',0.001,1,0.001,2,1,1,0,1)
c+1可以使得离散的最优解非均匀分散
c>1更多的最优解位于右侧
c<1更多的最优解位于左侧
控制最优区域的斜率,e在目标空间移动约束
,
get_ptrue('CTP5',0.001,1,0.001,1.5,1,1,0,1)
get_ptrue('CTP6',0.001,1,0.001,20,1,1,0,1)
get_ptrue('CTP7',0.001,1,0.001,1.5,1,1,0,1)
动态约束多目标测试函数1[2]
动态约束多目标测试函数2[3]
a = 0.2; b = 5; c = 1; d = 6; e = 1; s1 = -0.25*pi; zt = 6; sl=1; s2=-pi/16;
for t = 0:0.5:20
get_ptrue('DCF1',0.01,1,0.01,7,t,1,0,0)
hold on
pause(0.5)
end
PF 连续→间断→连续
可行域 增大→减小
for t = 0:0.5:20
get_ptrue('DCF2',0.01,1,0.01,7,t,1,0,0)
hold on
pause(0.5)
end
可行域 减小→增大
PF 间断→连续→间断
for t = 0:0.5:20
get_ptrue('DCF3',0.01,1,0.01,7,t,1,0,0)
hold on
pause(0.5)
end
可行域 增大→减小
PF 间断→连续→间断
for t = 0:0.5:20
get_ptrue('DCF4',0.01,1,0.01,7,t,1,0,0)
hold on
pause(0.5)
end
可行域 减小→增大
PF 连续→间断→连续
参考文献
[1] K. Deb, A. Pratap, and T. Meyarivan, “Constrained test problems for multi-objective evolutionary optimization,” in Proc. Int. Conf. Evol. Multi Criterion Optim. (EMO), 2001, pp. 284–298.
[2]Radhia Azzouz, Slim Bechikh, and Lamjed Ben Said. 2015. Multi-objective Optimization with Dynamic Constraints and Objectives: New Challenges for Evolutionary Algorithms. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO '15). Association for Computing Machinery, New York, NY, USA, 615–622.
[3]Q. Chen, J. Ding, S. Yang and T. Chai, “A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems,” IEEE Transactions on Evolutionary Computation, vol. 24, no. 4, pp. 792-806, Aug. 2020.
更多推荐
绘制(动态)约束多目标优化问题真实前沿
发布评论