A new unconstrained global optimization method based on clustering and parabolic approximation
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文摘

Proposed method uses clustering and parabolic approximation.

Clustering period provides jumping to better points using with populations.

The vertex of parabola is much close to the local minima than obtained cluster center.

We assume that the best cluster center denotes the position of global optimum.

The proposed method is simple, faster and very successful to gain the global optimum.

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