752 publications from this institution
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
No abstract is provided for this article.
No abstract is provided for this article.
Swarm intelligence is a very powerful technique to be used for optimization purposes. In this paper we present a new swarm intelligence algorithm, based on the bat algorithm. The Bat algorithm is hybridized with differential evolution strategies. Besides showing very promising results of the standard benchmark functions, this hybridization also significantly improves the original bat algorithm.
No abstract is provided for this article.
The influence of cross coupling terms in double porosity models for fractured reservoirs has been studied in this paper using numerical and perturbation methods. Attention is focused on the influence of the cross coupling coefficients on the transient solutions for the matrix and fissure pressures. Numerical simulations show that the addition of the cross coupling terms is equivalent to reducing the compressibility of the matrix block. A simple approximate solution is proposed for a quick estimation of the significance of the cross coupling effect in reservoir simulation, based on the solution of an uncoupled model.
This chapter introduces the foundations for computational linear algebra, starting with vectors and dot product. Then the matrix inverse and systems of linear equations are introduced. The eigenvalues and eigenvectors of a symmetric matrix are calculated, and their relationship to the definiteness of the corresponding matrix is also explained. Iterative methods for solving linear systems are also given with a worked example.
Finding an appropriate set of features from data of high dimensionality for building an accurate classification model is a well-known NP-hard computational problem. Unfortunately in data mining, some big data are not only big in volume but they are described by a large number of features. Many feature subset selection algorithms have been proposed in the past, they are nevertheless far from perfect. Since using brute-force in exhaustively trying every possible combination of features takes seemingly forever, stochastic optimization may be a solution. In this paper, we propose a new feature selection algorithm for finding an optimal feature set by using metaheuristic, called Swarm Search. The advantage of Swarm Search is its flexibility in integrating any classifier as its fitness function, and installing in any metaheuristic algorithm for facilitating heuristic search. Simulation experiments are carried out by testing the Swarm Search over a high-dimensional dataset, with different classification algorithms and various metaheuristic algorithms. Swarm search is observed to achieve satisfactory results.
No abstract is provided for this article.
This chapter focuses on the fundamental idea of the finite volume method and the property of finite volumes. Basic finite-volume schemes for one-dimensional heat conduction equation and two-dimensional Laplace equation are introduced. Criteria for numerical stability are derived.
Based on Universal Mobile Telecommunications System (UMTS) wideband code division multiple access (WCDMA) technology, the mobile user objective system (MUOS) provides a wide range of wireless telecommunications services to the war fighter over geosynchronous satellites. As MUOS is a multiple beam system, it is of paramount importance for the user entry (UE) to select the best beam, which in turn requires accurate transmit power from the satellite. To minimize the satellite path gain uncertainty from the base station to the UE, the enhanced gain variation reduction (EGVR) algorithm is proposed. Implemented on the basis of the measurements of the received signal code power (RSCP) of the pilot channel from the UE and the measurements of the received signal code power at the terrestrial base station, the EGVR process generates the transmission power estimate and integrates it into the overall gain adjust system to keep the transmission power levels as desired. The features of the algorithm include the weighted gain adjusts generated from the estimation error, appropriate timers to control the measurement flow, and differential measurements to eliminate the common errors between satellite beams. The preliminary study shows that the EGVR can reduce the power uncertainty from 2.4 dB to 0.4 dB, in addition to reducing any differential errors encountered. With the blending of the telemetry feedback into the algorithm, it can make the transmit power uncertainty even smaller.
Nature-inspired algorithms can be flexible and efficient for solving optimization problems. There are a wide spectrum of nature-inspired algorithms in the literature, and most of such algorithms are based on swarm intelligence. This chapter provides an overview of some widely used algorithms for optimization. Their main characteristics will be discussed in comparison with traditional algorithms such as gradient-based algorithms. Some open problems concerning swarm intelligence and nature-inspired computation will be highlighted.
This technical note reports the results of a set of tests of software toolboxes for optimisation and uncertainty evaluation using finite element models.
No abstract is provided for this article.
This first chapter intends to review and analyze the powerful new Harmony Search (HS) algorithm in the context of metaheuristic algorithms. I will first outline the fundamental steps of Harmony Search, and how it works. I then try to identify the characteristics of metaheuristics and analyze why HS is a good meta-heuristic algorithm. I then review briefly other popular metaheuristics such as par-ticle swarm optimization so as to find their similarities and differences from HS. Finally, I will discuss the ways to improve and develop new variants of HS, and make suggestions for further research including open questions.
This chapter introduces logistic regression, linear discriminant analysis, principal component analysis, singular value decomposition, and independent component analysis.
The flower pollination algorithm (FPA) was developed by Xin-She Yang in 2012, inspired by the flower pollination process of flowering plants. FPA has been extended to multi-objective optimization with promising results. This chapter provides an introduction to the flower pollination algorithm and its basic implementation.
