The strong need and global interests for green communications necessitate the maximum reduction of energy consumption. Hence, one of the major challenges is to optimize the resource allocation and to improve the energy efficiency in an orthogonal frequency division multiple access system. In this paper, we analyze the energy‐efficient resource allocation problem mathematically. Our analytical results show that the optimal resource allocation can be achieved by primarily assigning the user with either the best channel gain or the least optimal power. Furthermore, our detailed case studies show that such analytical results are consistent with the best result obtained from the exhaustive search method. This means that the proposed approach can provide quick and better resource allocation. It can be expected that real‐time in situ optimal resource allocation can be achieved by extending the current methodology. Copyright © 2014 John Wiley & Sons, Ltd.
Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
The amount of data available over Internet and World Wide Web is increasing exponentially. Retrieving data that is more close to user's query effectively and efficiently is a challenging task in Information Retrieval (IR) system. Clustering of Documents is one of the solutions to this. Clustering is the process of partitioning a set of objects in such a way that the objects in same cluster are more similar. The number of possible ways in which the documents can be clustered is enormous and this makes the problem to be a combinatorial optimization problem. Nature inspired algorithms are commanding tools to attack this type of problem. In this paper, an attempt has been made to use Cuckoo Search Optimization (CSO) algorithm to solve the problem of document clustering. The CSO algorithm is experimented with standard benchmark dataset, Classic4 dataset. The quality of solutions generated by CSO algorithm in terms of DB Index was compared with K-means algorithm and Ant Colony Optimization (ACO) algorithm. The results reveal that CSO algorithm is a viable to achieve world class solutions to high dimensional data clustering.
Both the bat algorithm and the cuckoo search algorithm are nature-inspired optimization algorithms, and they have been shown to be flexible and effective in solving various optimization problems. This chapter introduces both algorithms in detail, with an emphasis on the main ideas and implementations. Examples are also given to show how these algorithms work.
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No abstract is provided for this article.
So, automatic control systems, based on software tools, are becoming more desirable in distribution power systems.Primarily, such schemes are expected to manage system voltage fluctuations, network power flows and fault levels.Functionalities include also power balancing, system frequency control and management of demand side resources for the primary system constraints.
No abstract is provided for this article.
No abstract is provided for this article.
Simulated annealing is one of the earliest and yet widely used nature-inspired algorithms. This chapter introduces its main search mechanism, parameter setting, and implementation.
Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.
