problem = get_problem("zdt5") # eliminate_duplicates表示在算法执行过程中消除重复个体,以确保种群的多样性 algorithm = NSGA2(pop_size=100, sampling=BinaryRandomSampling(), crossover=TwoPointCrossover(), mutation=BitflipMutation(), eliminate_duplicates=True) res = minimize(problem, algorithm, ('n...
python 复制代码 import numpy as np from pymoo.core.problem import Problem class MyProblem(Problem...
example: frompymoo.algorithms.nsga2importNSGA2frompymoo.factoryimportget_problemfrompymoo.optimizeimportminimizefrompymoo.visualization.scatterimportScatter problem= get_problem("zdt1") algorithm= NSGA2(pop_size=100) res=minimize(problem, algorithm, ('n_gen', 200), seed=1, verbose=False) plot=Scatt...
定义问题:problem = get_problem("zdt1") 用于定义优化问题,这里使用了pymoo内置的ZDT1问题,它是一个经典的多目标优化测试问题。 选择算法:algorithm = NSGA2(...) 用于选择算法,这里使用了NSGA2(非支配排序遗传算法II),并设置了种群大小和每代产生的子代数量。 设置终止条件:termination = get_termination("n...
main .github docs examples pymoo algorithms constraints core cython decomposition experimental gradient indicators mcdm operators problems dynamic many __init__.py cdtlz.py dcdtlz.py dtlz.py wfg.py multi single __init__.py dyn.py functional.py ...
=0, xl=0, xu=10) def _evaluate(self, x, out, *args, **kwargs): f = x**2 # 目标函数,这里简单地使用平方作为示例 out["F"] = f def _get_discrete_values(self, x): # 自定义离散变量的取值规则 return np.arange(1, 11, 2) # 取 1 到 10 之间的所有奇数 problem = MyProblem(...
Alternatively, on the three-objective problem DTLZ2, it would produce amazing results. problem=get_problem("dtlz2") gde3mnn=GDE3(pop_size=150,variant="DE/rand/1/bin",CR=0.5,F=(0.0,0.9),survival=RankAndCrowding(crowding_func="mnn") )res=minimize(problem,gde3mnn, ('n_gen',250),seed...
重新导入Maven依赖有两种方式，如上图所示。如果多次点击重新导入依赖按钮依然报错，请看下一步 ...
对于 ,系数为 ,,系数为 。 通过用 和除以其相应的系数来实现约束的归一化。 最终目标函数为: pymoo 安装: pipinstall-Upymoo 1. 1) 基于元素的问题定义 定义了一个继承自ElementwiseProblem的新的Python目标,并设置了正确的属性,比如
from pymoo.optimize import minimize res = minimize(problem, algorithm, ("n_gen", 40), seed=1, save_history=True, verbose=False) X, F = res.opt.get("X", "F") hist = res.history print(len(hist)) # 40 n_evals = [] # corresponding number of function evaluations\ hist_F = []...