752 publications from this institution
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.
No abstract is provided for this article.
No abstract is provided for this article.
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2003Biology‐derived algorithm in analytical element modelling of groundwater flowAuthors: Xin‐She YangXin‐She YangCivil & Computational Engineering Center, School of Engineering, University of Wales Swansea, Swansea SA2 8PP, UKhttps://doi.org/10.1190/1.1817601 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817601FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2003ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2003 Pages: 2452 publication data© 2003 Copyright © 2003 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Xin‐She Yang, (2003), "Biology‐derived algorithm in analytical element modelling of groundwater flow," SEG Technical Program Expanded Abstracts : 1581-1583. https://doi.org/10.1190/1.1817601 Plain-Language Summary PDF DownloadLoading ...
Swarm intelligence (SI) and SI-based algorithms have become popular and useful in almost all areas of sciences and engineering. Significant developments have been made in recent years. This paper provides a short but timely analysis about SI algorithms and their links with self-organization. Emphasis has been on the present developments by analyzing the main characteristics and properties of algorithms, while future directions are pointed out by highlighting key challenges and their implications.
Metaheuristic optimization algorithms are usually based on swarm intelligence,and these algorithms are often referred to as smart algorithms.We review some of the widely used algorithms for optimization,including ant and bee algorithms,bat algorithm,cuckoo search, firefly algorithm and particle swarm optimization.We also discuss the challenging issues concerning parameter tuning and parameter control in metaheuristic algorithms.
Four schemes for binary solutions are proposed in this work to solve the resource allocation problem in an OFDMA system. The binary solution is sought at the early stage before applying the Genetic Algorithm to solve the real-valued problem. Each scheme performed differently in terms of Energy Efficient (EE) values obtained and the required computational cost. Out of four schemes (maxJ, maxP, minP and CP), the best scheme found in this study is CP as this scheme obtained the highest average EE with moderate computational cost.
In this paper, the concept of analytical continuation is utilized to generate the electric field over a wide frequency band. The problem of interest is TM scattering from a conducting cylinder. A method of moments formulation is utilized to generate information about the current and its derivatives at a specific frequency. This information is utilized to extrapolate the current at other frequencies. The numerical results presented are in good agreement with exact data.
Despite the increasing popularity of metaheuristics, many crucially important questions remain unanswered. There are two important issues: theoretical framework and the gap between theory and applications. At the moment, the practice of metaheuristics is like heuristic itself, to some extent, by trial and error. Mathematical analysis lags far behind, apart from a few, limited, studies on convergence analysis and stability, there is no theoretical framework for analyzing metaheuristic algorithms. I believe mathematical and statistical methods using Markov chains and dynamical systems can be very useful in the future work. There is no doubt that any theoretical progress will provide potentially huge insightful into meteheuristic algorithms.
No abstract is provided for this article.
Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications.
Optimization is everywhere, from business transactions and engineering design to planning your holidays and daily travel routes. Business organizations have to maximize their profits and minimize the costs. Engineering design has to maximize the performance of the designed product while minimizing the manufacturing cost at the same time. Even when we plan holidays we want to maximize the enjoyment and minimize the cost. Therefore, the studies of optimization are of both scientific interest and practical implications and subsequently the methodology can potentially have many applications. This chapter introduces all the important concepts concerning optimization and nonlinear programming.
The calibration of sound intensity instruments—in particular, with respect to instruments' sensitivity to both sound intensity and particle velocity in the rated frequency range—is challenging for laboratories worldwide. The commonly used approach is to measure the pressure sensitivity and phase difference of a pair of microphones, and electrically test the analyzer. The intensity instrument cannot be fully calibrated if only the components of the intensity measurement system are calibrated. Ideally, the sound intensity instrument should be calibrated as a system, along with all its parameters being compared with traceability to recognized standards; this will increase the confidence in the validity of measurement results. Currently, calibration below 50 Hz is still not feasible. To meet this requirement, the Beijing Aerospace Institute of Metrology and Measurement Technology has developed a double coupler calibration system capable of generating an intensity or particle velocity level of known amplitude and direction traceable to recognized standards. This paper covers the principle of the double coupler technique, design of the system, and generation of the reference intensity signal. The calibration results are in good agreement with the theoretical calculation results.
The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings.
Linear systems, especially large-scale spare systems, are very common in engineering simulations and computational sciences. In fact, most numerical methods for solving PDEs such as the finite difference methods and finite element methods often result in large, spare matrices. In this chapter, we introduce the basic concepts and solution methods of linear systems.
This chapter introduces the fundamentals of numerical solution of partial differential equations (PDEs), starting with the upwind scheme for first-order PDEs, followed by von Neumann stability analysis. Numerical methods such as the central difference scheme are then explained for second-order PDEs such as wave equation, heat conduction equation, and Poisson's equation, together with stability analysis of these schemes. Finally, the basic idea of the spectral method is outlined.
This chapter introduces some commonly used optimization techniques, including classical gradient-based methods, gradient-free methods, and the new optimizers for deep learning. It also introduces evolutionary algorithms and nature-inspired algorithms for optimization and computational intelligence.
