I am trying to use scipy optimization to solve an optimization problem. I have defined the non-linear constraints and fitness functions as shown below in the code. I am able to pass arguments to the fitness function but not to the non-linear constraints. Is there clean way to do it? The arguments to be passed to fitness function and the non-linear constraints are the same.
def func_nlc1(x, a1, a2): . . return val def func_nlc1(x, a1, a2): . . return val def fitness_function(x, args): . . return val if __name__ == '__main__': nlc1 = NonlinearConstraint(func_nlc1, -0.01, 0.01)) nlc2 = NonlinearConstraint(func_nlc1, -0.01, 0.01)) bounds = Bounds([0.0, 0.0], [1.0, 1.0]) result = differential_evolution(self.fitness_function, args=(a1,a2), bounds=bounds, strategy='rand1bin', constraints=(nlc1, nlc2))
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Answer
You can use lambda functions:
a1, a2 = 1, 2 # example values nlc1 = NonlinearConstraint(lambda x: func_nlc1(x, a1, a2), -0.01, 0.01)) nlc2 = NonlinearConstraint(lambda x: func_nlc1(x, a1, a2), -0.01, 0.01))