Dynamic Network Formation for FSO Satellite Communication

Satellite network optimization is essential, particularly since the cost of manufacturing, launching and maintaining each satellite is significant. Moreover, classical communication optimization methods, such as Minimal Spanning Tree, cannot be applied directly in dynamic scenarios where the satellite constellation is constantly changing. Motivated by the rapid growth of the Star-Link constellation that, as of Q4 2021, consists of over 1600 operational LEO satellites with thousands more expected in the coming years, this paper focuses on the problem of constructing an optimal inter-satellite (laser) communication network. More formally, given a large set of LEO satellites, each equipped with a fixed number of laser links, we direct each laser module on each satellite such that the underlying laser network will be optimal with respect to a given objective function and communication demand. In this work, we present a novel heuristic to create an optimal dynamic optical network communication using an Ant Colony algorithm. This method takes into account both the time it takes to establish an optical link (acquisition time) and the bounded number of communication links, as each satellite has a fixed amount of optical communication modules installed. Based on a large number of simulations, we conclude that, although the underlying problem of bounded-degree-spanning-tree is NP-hard (even for static cases), the suggested ant-colony heuristic is able to compute cost-efficient solutions in semi-real-time.

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A new routing method based on ant colony algorithm for LEO satellite communication networks

10.1117/12.772828 ◽

2007 ◽

Author(s):

Ying Wang ◽

Xiulin Hu

Keyword(s):

Communication Networks ◽

Ant Colony Algorithm ◽

Satellite Communication ◽

Ant Colony ◽

Routing Method ◽

Leo Satellite

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Enhanced Ant Colony Algorithm for Best Features Selection for a Decision Tree Classification of Medical Data

Advances in Library and Information Science - Critical Approaches to Information Retrieval Research ◽

10.4018/978-1-7998-1021-6.ch015 ◽

2020 ◽

pp. 278-293

Author(s):

Abdiya Alaoui ◽

Zakaria Elberrichi

Keyword(s):

Ant Colony Algorithm ◽

Ant Colony ◽

Large Set ◽

Features Selection ◽

Robust Learning ◽

Decision Tree Classification ◽

Wrapper Approach ◽

Irrelevant Attributes ◽

Np Hard Problem ◽

The Cost

Classification algorithms are widely applied in medical domain to classify the data for diagnosis. The datasets have considerable irrelevant attributes. Diagnosis of the diseases is costly because many tests are required to predict a disease. Feature selection is one of the significant tasks of the preprocessing phase for the data. It can extract a subset of attributes from a large set and exclude redundant, irrelevant, or noisy attributes. The authors can decrease the cost of diagnosis by avoiding numerous tests by selection of features, which are important for prediction of disease. Applied to the task of supervised classification, the authors construct a robust learning model for disease prediction. The search for a subset of features is an NP-hard problem, which can be solved by the metaheuristics. In this chapter, a wrapper approach by hybridization between ant colony algorithm and adaboost with decision trees to ameliorate the classification is proposed. The authors use an enhanced global pheromone updating rule. With the experimental results, this approach gives good results.

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