3,565 research outputs found
On strong homogeneity of two global optimization algorithms based on statistical models of multimodal objective functions
The implementation of global optimization algorithms, using the arithmetic of
infinity, is considered. A relatively simple version of implementation is
proposed for the algorithms that possess the introduced property of strong
homogeneity. It is shown that the P-algorithm and the one-step Bayesian
algorithm are strongly homogeneous.Comment: 11 pages, 1 figur
Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach
Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently
Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach
Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently
Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms
As an alternative to
classical techniques, the problem of image
segmentation has also been handled through
evolutionary methods. Recently, several
algorithms based on evolutionary principles have
been successfully applied to image segmentation
with interesting performances. However, most of
them maintain two important limitations: (1)
they frequently obtain suboptimal results
(misclassifications) as a consequence of an
inappropriate balance between exploration and
exploitation in their search strategies; (2) the
number of classes is fixed and known in advance.
This paper presents an algorithm for the
automatic selection of pixel classes for image
segmentation. The proposed method combines a
novel evolutionary method with the definition of
a new objective function that appropriately
evaluates the segmentation quality with respect
to the number of classes. The new evolutionary
algorithm, called Locust Search (LS), is based
on the behavior of swarms of locusts. Different
to the most of existent evolutionary algorithms,
it explicitly avoids the concentration of
individuals in the best positions, avoiding
critical flaws such as the premature convergence
to suboptimal solutions and the limited
exploration-exploitation balance. Experimental
tests over several benchmark functions and
images validate the efficiency of the proposed
technique with regard to accuracy and
robustness
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