dixon_price
The Dixon prince function is a multimodal minimization [1].
of = dixon_price(x)
Input variables
| Name | Description | Type |
|---|---|---|
x | Current design variables of the i agent. | List |
Output variables
| Name | Description | Type |
|---|---|---|
of | Objective function value of the i agent. | Float |
Problem
| \[ f(\mathbf{x}) = \left ( x_{1} - 1 \right )^2 + \sum^d_{i=2} i \left ( 2x^2_{i} - x_{i-1} \right )^2\] | (1) |
| \[x_{i} \in [-10, 10], i=1, ... , d; \; \;...\;f(\mathbf{x}^*) = 0, \; \mathbf{x}_{i} = 2^{-\frac{2^i - 2}{2^i}}, i = 1, ... , d\] | (2) |
Example 1
Considering the design variable \(\mathbf{x} = [0, -1]\), what value does the objective function expect?
# Data
x = [0, -1]
# Call function
of = dixon_price(x)
# Output details
print("of_best dixon-Price: of = {:.4f}".format(of))
of_best dixon-Price: of = 0.0000