arXiv subjects

Xin-She Yang

Publications and source records attributed to Xin-She Yang.

At least 73 records · Page 4Linked to original sources

arXiv2012

Nature-Inspired Mateheuristic Algorithms: Success and New Challenges

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.

math.OC↗

arXiv2012

Free Lunch or No Free Lunch: That is not Just a Question?

The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in algorithm analysis and performance evaluations. No-free-lunch theorems are of both theoretical and practical importance, while many important studies on convergence analysis of various metaheuristic algorithms have proven to be fruitful. This paper discusses the recent results on no-free-lunch theorems and algorithm convergence, as well as their important implications for algorithm development in practice. Free lunches may exist for certain types of problem. In addition, we will highlight some open problems for further research.

math.OC↗

arXiv2012

Memetic firefly algorithm for combinatorial optimization

Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.

math.OC↗

arXiv2012

Bat Algorithm for Multi-objective Optimisation

Engineering optimization is typically multiobjective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimization algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving nonlinear, global optimisation problems. In this paper, we extend this algorithm to solve multiobjective optimisation problems. The proposed multiobjective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multiobjective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.

math.OC↗

arXiv2012

Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.

math.OC↗

arXiv2012

Two-Stage Eagle Strategy with Differential Evolution

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.

math.OC↗

arXiv2012

Are motorways rational from slime mould's point of view?

We analyse the results of our experimental laboratory approximation of motorways networks with slime mould Physarum polycephalum. Motorway networks of fourteen geographical areas are considered: Australia, Africa, Belgium, Brazil, Canada, China, Germany, Iberia, Italy, Malaysia, Mexico, The Netherlands, UK, USA. For each geographical entity we represented major urban areas by oat flakes and inoculated the slime mould in a capital. After slime mould spanned all urban areas with a network of its protoplasmic tubes we extracted a generalised Physarum graph from the network and compared the graphs with an abstract motorway graph using most common measures. The measures employed are the number of independent cycles, cohesion, shortest paths lengths, diameter, the Harary index and the Randic index. We obtained a series of intriguing results, and found that the slime mould approximates best of all the motorway graphs of Belgium, Canada and China, and that for all entities studied the best match between Physarum and motorway graphs is detected by the Randic index (molecular branching index).

nlin.PS↗

arXiv2011

Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.

math.OC↗

arXiv2010

Engineering Optimisation by Cuckoo Search

A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.

math.OC↗

arXiv2010

Test Problems in Optimization

Test functions are important to validate new optimization algorithms and to compare the performance of various algorithms. There are many test functions in the literature, but there is no standard list or set of test functions one has to follow. New optimization algorithms should be tested using at least a subset of functions with diverse properties so as to make sure whether or not the tested algorithm can solve certain type of optimization efficiently. Here we provide a selected list of test problems for unconstrained optimization.

math.OC↗

arXiv2010

Eagle Strategy Using Lévy Walk and Firefly Algorithms For Stochastic Optimization

Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using Lévy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.

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arXiv2010

A New Metaheuristic Bat-Inspired Algorithm

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.

math.OC↗

arXiv2010

Computational Modelling of Nonlinear Calcium Waves

The calcium transport in biological systems is modelled as a reaction-diffusion process. Nonlinear calcium waves are then simulated using a stochastic cellular automaton whose rules are derived from the corresponding coupled partial differential equations. Numerical simulations show self-organized criticality in the complex calcium waves and patterns. Both the stochastic cellular automaton approach and the equation-based simulations can predict the characteristics of calcium waves and complex pattern formation. The implication of locality of calcium distribution with positional information in biological systems is also discussed.

nlin.PS↗

arXiv2010

Nonlinear Viscoelastic Compaction in Sedimentary Basins

In the mathematical modelling of sediment compaction and porous media flow, the rheological behaviour of sediments is typically modelled in terms of a nonlinear relationship between effective pressure $p_e$ and porosity $ϕ$, that is $p_e=p_e(ϕ)$. The compaction law is essentially a poroelastic one. However, viscous compaction due to pressure solution becomes important at larger depths and causes this relationship to become more akin to a viscous rheology. A generalised viscoelastic compaction model of Maxwell type is formulated, and different styles of nonlinear behaviour are asymptotically analysed and compared in this paper.

physics.geo-ph↗

arXiv2010

Characterization of Multispecies Living Ecosystems With Cellular Automata

A multispecies artificial ecosystem is formulated using cellular automata with species interactions and food chain hierarchy. The constructed finite state automaton can simulate the complexity and self-organized characteristics of the evolving multispecies living ecosystems. Numerical experiments show that a small perturbation or extinction event may affect many other species in the ecosystem in an avalanche manner. Both the avalanches and the extinction arising from these changes follow a power law, reflecting that the multispecies living ecosytems have the characteristics of self-organized criticality.

nlin.CG↗

arXiv2010

A Mathematical Model for Voigt Poro-Visco-Plastic Deformation

A mathematical model for poro-visco-plastic compaction and pressure solution in porous sediments has been formulated using the Voigt-type rheological constitutive relation as derived from experimental data. The governing equations reduce to a nonlinear hyperbolic heat conduction equation in the case of slow deformation where permeability is relatively high and the pore fluid pressure is nearly hydrostatic, while travelling wave exists in the opposite limit where over-pressuring occurs and the pore fluid pressure is almost quasi-lithostatic. Full numerical simulation using a finite element method agree well with the approximate analytical solutions.

physics.geo-ph↗

arXiv2010

Density-Driven Compactional Flow in Porous Media

In 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 pressure is high, compaction becomes more akin to a viscous one. Two mathematical models of compaction in porous media are formulated and the noninear 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.

physics.geo-ph↗

arXiv2010

Asymptotic Solutions of Compaction in Porous Media

Compaction in reactive porous media is modelled as a reaction-diffusion process with a moving boundary. Asymptotic analysis is used to find solutions for the coupled nonlinear compaction equations, and a traveling wave solution is obtained above the reaction zone.

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