Results 11 to 20 of about 415,862 (310)
On testing global optimization algorithms for space trajectory design [PDF]
, 2008 In this paper we discuss the procedures to test a global search algorithm applied to a space trajectory design problem. Then, we present some performance indexes that can be used to evaluate the effectiveness of global optimization algorithms.
Marco Locatelli +10 more
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A hybrid multiagent approach for global trajectory optimization [PDF]
, 2008 In this paper we consider a global optimization method for space trajectory design problems. The method, which actually aims at finding not only the global minimizer but a whole set of low-lying local minimizers(corresponding to a set of different design
Vasile, M. +4 more
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Learning to be Global Optimizer
CoRR, 2020 The advancement of artificial intelligence has cast a new light on the development of optimization algorithm. This paper proposes to learn a two-phase (including a minimization phase and an escaping phase) global optimization algorithm for smooth non-convex functions. For the minimization phase, a model-driven deep learning method is developed to learn
Haotian Zhang, Jianyong Sun, Zongben Xu
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Distributed global optimization (DGO) [PDF]
Proceedings of International Conference on Neural Networks (ICNN'96), 2002 A new technique of global optimization and its applications in particular to neural networks are presented. The algorithm is also compared to other global optimization algorithms such as Gradient descent (GD), Monte Carlo (MC), Genetic Algorithm (GA) and other commercial packages.
Homayoun Valafar +2 more
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Mountain Segmentation Based on Global Optimization with the Cloth Simulation Constraint
Remote Sensing, 2023 Mountains are an important research object for surveying, mapping, cartography, space science, and ecological remote sensing. Automatic mountain segmentation is one of the most critical techniques in large-scale mountain analyses.
Lekang Wen, Jun He, Xu Huang
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Global Optimality in Low-Rank Matrix Optimization [PDF]
IEEE Transactions on Signal Processing, 2017 This paper considers the minimization of a general objective function $f(X)$ over the set of rectangular $n\times m$ matrices that have rank at most $r$. To reduce the computational burden, we factorize the variable $X$ into a product of two smaller matrices and optimize over these two matrices instead of $X$. Despite the resulting nonconvexity, recent
Zhihui Zhu +3 more
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A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
Algorithms, 2022 The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization ...
Quan Zhang +5 more
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Mathematics, 2022 Quadratically constrained quadratic programs (QCQP), which often appear in engineering practice and management science, and other fields, are investigated in this paper.
Chenyang Hu +3 more
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Neural Network Algorithm with Dropout Using Elite Selection
Mathematics, 2022 A neural network algorithm is a meta-heuristic algorithm inspired by an artificial neural network, which has a strong global search ability and can be used to solve global optimization problems.
Yong Wang, Kunzhao Wang, Gaige Wang
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Diffusions for Global Optimization
SIAM Journal on Control and Optimization, 1986 This paper describes a problem of finding a global minimum of a real- valued function defined on the unit hypercube in Euclidean n-space. The problem is changed to a stochastic differential equation by using the gradient of the above function as the drift term and a diffusion term which is interpreted as a constant times the square root of ...
Geman, Stuart, Hwang, Chii-Ruey
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