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July 20th, 2017, 01:17 AM   #1
Joined: Jul 2017
From: london

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Lightbulb cost function for parameters optimisation

hello everybody,

I am stuck with an optimisation problem. In brief; I am trying to optimise the weights associated to the control points of a NURBS 3D curve using either a genetic algorithm or a PSO. The cost function is based on some path preferential characteristics such as length , curvature constraint,feasibility (in terms of obstacle avoidance etc)

I feel the results of the optimisation with my current attempts (using the Optimisation Toolbox of Matlab) strongly depend on the parameter settings of the algorithm I am using. Ideally I would like to define my cost function in such a way that the optimisation tends always to the global minimum within some constraints (like curvature and obstacle avoidance).

What I am trying to do is quite similar to what they did in this paper ( ) but it is not clear how they can guarantee the constraints.

does anybody know how to solve this?

Thank you for your attention,
marly892 is offline  

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