Results 321 to 330 of about 9,535,029 (378)
Some of the next articles are maybe not open access.
2001
The distinction between local and global techniques is only necessary in the context of nonlinear optimization, since linear problems always have a unique optimum; see Chap. 3. The nonlinear local optimization techniques discussed in the previous chapter start from an initial point in the parameter space and search in directions obtained by ...
openaire +2 more sources
The distinction between local and global techniques is only necessary in the context of nonlinear optimization, since linear problems always have a unique optimum; see Chap. 3. The nonlinear local optimization techniques discussed in the previous chapter start from an initial point in the parameter space and search in directions obtained by ...
openaire +2 more sources
Global optimization and simulated annealing
Mathematical Programming, 1991The first six pages of this well-written paper can be considered an introduction to global optimization algorithms that first focuses on stochastic methods, then on simulated annealing. The authors then present theoretical results for an idealized simulated annealing algorithm that are based on the ergodic theory of Markov chains. The main result gives
AF Anton Dekkers +2 more
openaire +3 more sources
Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems
Applied intelligence (Boston), 2022Shijie Zhao +3 more
semanticscholar +1 more source
Interval methods for global optimization
Applied Mathematics and Computation, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
Bayesian methods in global optimization
Journal of Global Optimization, 1991The paper reviews methods which have been proposed for solving global optimization problems in the framework of the Bayesian paradigm. Three main approaches are singled out. In the first approach, called the Random Function Approach, methods are based on the idea of introducing a probabilistic model for the objective function in the form of a random ...
openaire +2 more sources
A Hybrid Moth Flame Optimization Algorithm for Global Optimization
Journal of Bionic Engineering, 2022S. Sahoo, A. K. Saha
semanticscholar +1 more source
Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization
Engineering applications of artificial intelligence, 2020Satnam Kaur +3 more
semanticscholar +1 more source