Journal Article10.1007/S40314-016-0407-8
A filled function method for global optimization with inequality constraints
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TL;DR: A new filled function method for finding a global minimizer of global optimization with inequality constraints, which is a continuously differentiable function with only one parameter is proposed.
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Abstract: In this paper, we propose a new filled function method for finding a global minimizer of global optimization with inequality constraints. The proposed filled function is a continuously differentiable function with only one parameter. Then, we can use classical local optimization methods to find a better minimizer of the proposed filled function with a few parameter adjustment. The numerical experiments are made and the results show that the proposed filled function method is effective.
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Citations
A New Hybrid Algorithm for Solving Large Scale Global Optimization Problems
TL;DR: A hybrid search strategy is proposed, which adaptively chooses the one-dimensional search scheme or the covariance matrix adaptation evolutionary strategy to solve the subproblems of separable, partially (additively) separable problems or non-separable problems, respectively.
Non parameter-filled function for global optimization
TL;DR: The capability of this proposed method is demonstrated and the computational weaknesses of the parametric filled function are proved to be surmounted by this new non parameter-filled function.
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A new filled function for global minimization and system of nonlinear equations
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On a new smoothing technique for non-smooth, non-convex optimization
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TL;DR: This study proposes a new smoothing approach in order to smooth out non-smooth and non-Lipschitz functions playing a very important role in global optimization problems.
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A Filled Flatten Function Method Based on Basin Deepening and Adaptive Initial Point for Global Optimization
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8
References
A deterministic annealing algorithm for approximating a solution of the max-bisection problem
Chuangyin Dang,Wei Ma,Jiye Liang +2 more
TL;DR: In this paper an equivalent linearly constrained continuous optimization problem is formulated and a deterministic annealing algorithm is proposed for approximating its solution and it is proved that the algorithm converges to at least an integral local minimum point of the continuous problem if a local minimum points of the barrier problem is generated for a sequence of descending values of the Barrier parameter with zero limit.
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A Class of Continuously Differentiable Filled Functions for Global Optimization
Xian Liu
- 01 Jan 2008
TL;DR: A class of new filled functions that are continuously differentiable and do not include exponential terms are proposed, which will help to find global minima of multidimensional multimodal functions.
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A filled function method applied to nonsmooth constrained global optimization
TL;DR: A one-parameter filled function is constructed to improve the efficiency of numerical computation and a corresponding algorithm is presented which is a global optimization method which modify the objective function as a filled function, and which find a better local minimizer gradually by optimizing the filled function constructed on the minimizer previously found.
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The barrier attribute of filled functions
TL;DR: A new perspective of the filled functions: their barrier attribute is presented, which shows that two of them are with finite barriers while the remains are with infinite barriers.
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