Biography:

In the past Xin-She Yang has collaborated on articles with Andy C McIntosh and Xiaoge Zhang. One of their most recent publications is 1 - Swarm Intelligence and Bio-Inspired Computation: An Overview. Which was published in journal .

More information about Xin-She Yang research including statistics on their citations can be found on their Copernicus Academic profile page.

Xin-She Yang's Articles: (29)

1 - Swarm Intelligence and Bio-Inspired Computation: An Overview

Swarm intelligence (SI) and bio-inspired computing in general have attracted great interest in almost every area of science, engineering, and industry over the last two decades. In this chapter, we provide an overview of some of the most widely used bio-inspired algorithms, especially those based on SI such as cuckoo search, firefly algorithm, and particle swarm optimization. We also analyze the essence of algorithms and their connections to self-organization. Furthermore, we highlight the main challenging issues associated with these metaheuristic algorithms with in-depth discussions. Finally, we provide some key, open problems that need to be addressed in the next decade.

Chapter 3 - Random Walks and Optimization

AbstractRandom walks and other stochastic components are an intrinsic part of nature-inspired metaheursitic algorithms. They are often used as random numbers and randomization techniques in metaheuristic algorithms, and the efficiency of a metaheuristic algorithm may implicitly depend on the appropriate use of such randomization. In this chapter, we first introduce the fundamental ideas of random variables and theory of random walks and Lévy flights. Then, we discuss the relationship between optimization, random walks and Markov chains, followed by the analysis of step sizes and efficiency of an algorithm using the framework of Markov chain theory.

Chapter 5 - Genetic Algorithms

AbstractGenetic algorithms are among the most popular evolutionary algorithms in terms of the diversity of their applications. A vast majority of well-known optimization problems have been solved using genetic algorithms. In addition, genetic algorithms are population-based, and many modern evolutionary algorithms are directly based on genetic algorithms or have some strong similarities to them.

Chapter 10 - Bat Algorithms

AbstractThe bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. BA has been found to be very efficient. As a result, the literature has expanded significantly since then. This chapter provides a detailed introduction to BA and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly.

Chapter 5 - Exponentials and Logarithms

AbstractExponentials and logarithms are important basic mathematical functions. This chapter introduces the basic exponential and logarithmic functions and their properties.

Chapter 6 - Trigonometry

AbstractTrigonometry is an essential part of engineering mathematics. For example, in robotics, trigonometry can be useful in calculating the positions of robotic arms, rotations as well as other quantities. In addition, trigonometrical functions are also intrinsically related to complex numbers. This chapter introduces the fundamentals of trigonometrical functions.

Chapter 17 - Laplace Transforms

AbstractLaplace transforms are an important tool with many applications in engineering such as control system and automation. This chapter introduces the fundamentals of Laplace transforms, their properties and applications in solving differential equations.

Chapter 18 - Probability and Statistics

AbstractAll mathematical models and differential equations we have discussed so far are deterministic systems in the sense that, for given initial and boundary conditions, the solutions of the system can be determined. There is no intrinsic randomness in differential equations. In reality, randomness occurs everywhere, and not all models are deterministic. In fact, it is necessary to use stochastic models and sometimes the only sensible models are stochastic descriptions. In these cases, we have to deal with probability and statistics.

Chapter 27 - Integral Equations

AbstractDifferential equations concern equations with unknown functions and their derivatives, and there is no integral in the equation. Sometimes, physical laws can lead to equations with integrals in the equation. In this case, we have to deal with integral equations. This chapter introduces the basics of integral equations and their solution techniques.

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3 - Optimization algorithms

AbstractThis 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.

5 - Logistic regression, PCA, LDA, and ICA

AbstractThis chapter introduces logistic regression, linear discriminant analysis, principal component analysis, singular value decomposition, and independent component analysis.

6 - Data mining techniques

AbstractThis chapter introduces some of the most widely used techniques for data mining, including nearest-neighbor algorithm, k-mean algorithm, decision trees, random forests, Bayesian classifier, and others. Special techniques such as CURE and BFR for mining big data are also briefly introduced.

The effect of large step pressure drops on strained premixed flames

AbstractA model of the interaction of pressure changes with strained premixed flames is formulated by extending previous work on interactions with nonstrained flames. By making the assumption of no divergence in the velocity field (∇ · u = 0) and writing the full equations in terms of mass-weighted coordinates (with explicit strain terms) following the flame sheet, we study the time-varying response of the mass burning rate to an imposed pressure disturbance. Numerical solution of the full nonlinear equations shows that strain has a strong effect on the flame response. It is found that, for large amplitude step pressure drops, the flame does not recover for moderate and small strain rates. As expected, for larger positive strain rates the flame recovers, because it is known that positive strain stabilizes premixed flames.The reverse happens for flames experiencing pressure drops in a convergent flow (negative strain). In this case, extinction of the flame by pressure drops becomes more likely as the convergence of the flow increases. Of particular interest for the negative strain case is the onset of the pulsating instability for a finite range of small values of pressure drop, which then gives way to a region of recovery if the pressure drop is larger, before finally for larger drops the extinction pressure drop is reached.

A bio-inspired algorithm for identification of critical components in the transportation networks

AbstractCritical components in a transportation or communication network are those which should be better protected or secured because their removal has a significant impact on the whole network. In such networks, they will be congested if they are being offered more traffic than it can process. In this paper, we employ principles of slime mould Physarum polycephalum foraging behaviour to identify the critical components in congested networks. When Physarum colonises a substrate, it develops a network of protoplasmic tube aimed at transporting nutrients and metabolites between distance parts of the cell. The protoplasmic network is continuously updating to minimize the transportation time, maximize the amount of cytoplasm pumped and minimize the overall length of the network. This optimization is achieved via a positive feedback between flux of cytoplasm and tube diameters. When a segment of a protoplasmic network is removed, the whole network reconfigures and thickness of tubes is updated till an equilibrium state is reached. The transient period from a disturbed state to an equilibrium state shows how critical the removed segment was. We develop a Physarum-inspired algorithm to identify critical links or nodes in a network by removing them from the network or calculating the transient period to new equilibrium state. The efficiency of the proposed method are demonstrated in numerical examples.

Pattern formation in enzyme inhibition and cooperativity with parallel cellular automata

AbstractEnzyme reactions with inhibition and cooperativity are modelled in terms of a pair of coupled nonlinear reaction–diffusion equations. The governing equations are solved using stochastic cellular automata with local rules derived from the corresponding nonlinear partial differential equations. The parallel cellular automaton is implemented using domain decomposition according to the nature of the locality of its update rules. Numerical simulations show stable 2-D and 3-D pattern formation, and complex patterns have the interesting feature of self-organized criticality. The numerical results of cellular automata are also compared with results obtained from finite difference and finite element methods.

Multiobjective cuckoo search for design optimization

AbstractMany design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.

Density-driven compactional flow in porous media

AbstractIn the mathematical modelling of compactional flow in porous media, the constitutive relation is typically modelled in terms of a nonlinear relationship between effective pressure and porosity, and compaction is essentially poroelastic. However, at depths deeper than 1 km where the pressure is high, compaction becomes more akin to a viscous one. Two mathematical models of compaction in porous media are formulated and the nonlinear equations are then solved numerically. The essential features of numerical profiles of poroelastic and viscous compaction are thus compared with asymptotic solutions. Two distinguished styles of density-driven compaction in fast and slow compacting sediments are analysed and shown in this paper.

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