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July 20th, 2017, 02:17 AM  #1 
Newbie Joined: Jul 2017 From: london Posts: 1 Thanks: 0  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 (https://link.springer.com/content/pd...192642_19.pdf ) but it is not clear how they can guarantee the constraints. does anybody know how to solve this? Thank you for your attention, 

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cost, function, optimisation, parameters 
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