Numerical results obtained indicate the efficiency and reliability of the proposed filled function methods.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, We consider the problem of finding the global minimizer point of a given multi-dimensional unconstrained objective function, for that a new algorithm developed, this algorithm based on two steps.
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Key Findings & Data:First, we transform our problem into one-dimension according to the number of directions.
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Conclusion & Impact:Numerical results obtained indicate the efficiency and reliability of the proposed filled function methods.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:global optimization · filled function method · global optimization
Abstract
In this paper, We consider the problem of finding the global minimizer point of a given multi-dimensional unconstrained objective function, for that a new algorithm developed, this algorithm based on two steps. First, we transform our problem into one-dimension according to the number of directions. Second, we construct a new filled function at each direction to minimize the one-dimension problem to reduce the number of local minimizers and then we find the global minimizer of the multi-dimensional objective function. We present the results of numerical experiments using test problems taken from literature studies. Numerical results obtained indicate the efficiency and reliability of the proposed filled function methods.
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