752 publications from this institution
A robust variant of invasive weed optimisation (IWO) algorithm, called enhanced invasive weed optimisation (EIWO) algorithm, is proposed in this paper for the optimisation of constrained benchmark problems. Enjoying the ecological behaviour of colonising weeds, IWO has demonstrated its ability in solving different optimisation problems. Since making a proper balance between these two components is essential, especially to cope with constraint optimisation problems, two new rules are added to the algorithm to improve its performance. The first rule is utilising principles of social standard deviation as proposed in social harmony search (SHS) algorithm. The second rule is utilised to prevent the algorithm to get stuck on local optima. Finally, for constraint handling, three simple heuristic rules of Deb are utilised. The robustness and effectiveness of the proposed method are tested on many constrained benchmark problems and compared against those of state-of-the-art algorithms.
Differential evolution (DE) is a vector-based metaheuristic algorithm that has good convergence properties. There are many DE variants, and they have been applied in a wide range of disciplines. This chapter provides a brief introduction to the basic differential evolution, its main implementation details, and its variants. Fundamental convergence properties in terms of the population variance are also discussed.
Nowadays, many studies have been established to develop strategies for inflammatory bowel disease (IBD) treatment. However, seldom of them explored the synergistic effects of materials and probiotics in IBD treatment. Herein, we prepared probiotics-loaded dietary fiber microspheres and studied their potential in IBD therapy. The carrageenan, a typical dietary fiber, was functionalized with polymerizable groups and employed to fabricate the methylacrylylated carrageenan hydrogel microspheres (CHMSs) by microfluidics. The CHMSs loaded with probiotics exhibited good monodispersity, biocompatibility, and the best effects in relieving the symptoms of IBD and body weight recovery than other groups, indicating the probiotics and carrageenan have synergistic effect for IBD treatment. These results show the huge potential of the probiotics-loaded CHMSs for IBD therapy.
Almost all design problems in the sciences and engineering can be formulated as optimization problems, and many image processing problems can also be related to or formulated as optimization problems. These optimization problems can be solved by optimization techniques. However, these problems are often highly nonlinear and are subject to multiple nonlinear constraints, which makes them very challenging to solve. The further complication to these challenges is the stringent time requirements and high dimensionality, which means that traditional optimization techniques, such as gradient-based methods cannot deal with such kinds of problems well. Recent trends tend to use bio-inspired optimization techniques as a promising alternative, and it is usually combined with traditional methods, especially in the area of image processing. These bio-inspired computational methods are usually based on swarm intelligence and can be very effective in coping with nonlinearity in real-world problems. This chapter presents an overview of bio-inspired computation and its application in image processing, including some current trends and important issues, such as efficiency and time constraints.
No abstract is provided for this article.
The transformation of hydrated clay minerals (smectite) to anhydrous forms (illite) occurs in the diagenesis of shales in sedimentary basins. The released pore water can lead to excess pore pressures and is of concern in oil drilling operations. Most simply, the process can be modeled as a first‐order thermally activated reaction, but an alternative interpretation is that diagenesis occurs via dissolution of smectite in free pore water and subsequent precipitation of illite. We show how such a realistic model of diagenesis can be included in a compactive model of the sediment/pore water system, and by using the limit of weak solubility (also known as solid density asymptotics), we show how an explicit expression can be derived for the smectite dissolution rate, even when this is mediated by feldspar dissolution and other reactions. In certain circumstances, the reaction rate reduces to that of a simple first‐order model. By solving the model we also show that shale diagenesis provides a mechanism for the generation of fractures at depth in rapidly deposited sediments. When sedimentation is slow (or compaction is fast), the smectite to illite transition occurs over a reaction window which typically lies below the equilibrated normally pressured part of the profile, but it does not generally contribute significantly to the overpressuring.
Nonlinear data assimilation can be a very challenging task. Four local search methods are proposed for nonlinear data assimilation in this paper. The methods work as follows: At each iteration, the observation operator is linearized around the current solution, and a gradient approximation of the three dimensional variational (3D-Var) cost function is obtained. Then, samples along potential steepest descent directions of the 3D-Var cost function are generated, and the acceptance/rejection criteria for such samples are similar to those proposed by the Tabu Search and the Simulated Annealing framework. In addition, such samples can be drawn within certain sub-spaces so as to reduce the computational effort of computing search directions. Once a posterior mode is estimated, matrix-free ensemble Kalman filter approaches can be implemented to estimate posterior members. Furthermore, the convergence of the proposed methods is theoretically proven based on the necessary assumptions and conditions. Numerical experiments have been performed by using the Lorenz-96 model. The numerical results show that the cost function values on average can be reduced by several orders of magnitudes by using the proposed methods. Even more, the proposed methods can converge faster to posterior modes when sub-space approximations are employed to reduce the computational efforts among iterations.
1School of Science and Technology, Middlesex University London, London NW4 4BT, UK 2Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, High Educational Town, SIP, Suzhou 215123, China 3Centre for Smart Grid and Information Convergence, Xi’an Jiaotong-Liverpool University, High Educational Town, SIP, Suzhou 215123, China 4Department of Electronic Engineering, Inha University, Inha-ro 100, Incheon, Republic of Korea 5Institute for Information and Electronics Research, Inha University, Inha-ro 100, Incheon, Republic of Korea 6Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand 7Biomedical Engineering Center, Chiang Mai University, Chiang Mai 50200, Thailand
The paper proposes a new class of traffic profiles that is better suited for metering bursty Internet traffic streams than the traditional token bucket profile. A good traffic profile should satisfy two criteria: first, it should consider packets from a conforming traffic stream as in-profile with high probability to ensure a strong QoS guarantee; second, it should limit the network resources consumed by a non-conforming traffic stream to no more than that consumed by a conforming stream. We model a bursty Internet traffic stream as an ON/OFF stream, where both the ON-period and the OFF-period have a heavy-tailed distribution. Our study shows that the heavy-tailed distribution leads to an excessive randomness in the long-term session rate distribution. Therefore, it is inherently difficult for any profile that limits the long-term average session rate to give a strong QoS guarantee for the conforming traffic streams. Our simulation demonstrates that a token bucket profile that couples the average rate control and the burst size control has a weak QoS guarantee. Based on this result, we propose a new class of traffic profiles that decouples the long term average rate control from the burst size control. Compared to a token bucket profile, this profile improves the level of QoS for a conforming traffic stream, yet limits the "effective bandwidth" consumed by a non-conforming traffic stream.
No abstract is provided for this article.
This chapter covers the basic concepts of probability, variance, and probability distributions. All the commonly used probability distributions in engineering simulation are briefly explained, including Bernoulli, geometric, binomial, Gaussian, exponential, chi-squared, and Weibull distributions. Moment-generating function is also outlined with some examples.
No abstract is provided for this article.
Particle swarm optimization (PSO) is a swarm intelligence based algorithm and is among the most widely used nature-inspired algorithms. This chapter introduces the standard PSO, its variants and demo implementation.
In computer science, a computational challenge exists in finding a globally optimized solution from a tremendously large search space. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods have been extensively studied in the past, and progressively extended in order to suit a wide range of optimization problems. Researchers recently have invented a collection of heuristic optimization methods inspired by the movements of animals and insects (e.g., Firefly, Cuckoos, Bats and Accelerated PSO) with the advantages of efficient computation and easy implementation. This paper proposes a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The contribution of the paper is twofold: 1. for verifying the efficacy of the WSA the algorithm is tested quantitatively and compared to other heuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms; 2. The WSA is investigated with respective to its memory requirement. Superior results are observed in most tests.
The firefly algorithm (FA) is a swarm intelligence based algorithm for optimization. FA is a nonlinear system that uses attraction to evolve its multi-agent system. This chapter introduces the basic ideas of FA and its variants. A demo implementation is also provided.
Copyright © 2014 Xin-She Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
No abstract is provided for this article.
