752 publications from this institution
Enzyme reactions with inhibition and cooperativity are common in biological systems. A theoretical model for competitive and cooperative enzyme activity is formulated in terms of a pair of coupled nonlinear reaction-diffusion equations so as to derive the corresponding rule-based algorithm and nature-derived updating algorithms. A new stochastic cellular automaton is then constructed to simulate this nonlinear system. Numerical simulations show stable 2-D and 3-D pattern formation, and complex patterns have the interesting feature of self-organized criticality.
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 1km 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.
No abstract is provided for this article.
The growing costs of fuel and operation of power generating units warrant improvement of optimization methodologies for economic dispatch (ED) problems. The practical ED problems have non-convex objective functions with equality and inequality constraints that make it much harder to find the global optimum using any mathematical algorithms. Modern optimization algorithms are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This paper presents a novel approach to determining the feasible optimal solution of the ED problems using the recently developed Firefly Algorithm (FA). Many nonlinear characteristics of power generators, and their operational constraints, such as generation limitations, prohibited operating zones, ramp rate limits, transmission loss, and nonlinear cost functions, were all contemplated for practical operation. To demonstrate the efficiency and applicability of the proposed method, we study four ED test systems having non-convex solution spaces and compared with some of the most recently published ED solution methods. The results of this study show that the proposed FA is able to find more economical loads than those determined by other methods. This algorithm is considered to be a promising alternative algorithm for solving the ED problems in practical power systems.
In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.
Exponentials and logarithms are important basic mathematical functions. This chapter introduces the basic exponential and logarithmic functions and their properties.
Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.
Computational optimization is becoming increasingly important in engineering design and industrial applications. Products and services are often concerned with the maximization of profits and reduction of cost, but also aim at being more energy-efficient, environment-friendly and safety-ensured; at the same time they are limited by resources, time and money. This second workshop on Computational Optimization, Modelling and Simulation (COMS 2011) at ICCS 2011 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry.
Cunninghamia lanceolate (Lambert.) Hooker is one of the main fast-growing timber forest species in southern China which has a long history of cultivation and spreads across 28 provinces, cities, and regions. Recently, a variant of fir was discovered in the Xiaoxi National Nature Reserve in Hunan Province. The heartwood is hard as iron and its ratio is more than 80%, with the especial character of anti-corruption. It is a natural germplasm resource, called Iron-heart Cunninghamia lanceolate. Study on it is still in the stage of data accumulation. In this paper, we studied it from three points as follows: (1) Plus tree selection and construction of germplasm resources nursery. (2) Study on cone and seed quality. (3) Genetic structure analysis of natural population. The research of Iron-heart Cunninghamia lanceolate lays a theoretical foundation for the protection, development, and utilization of the black-heart wood germplasm resources of Iron-heart Cunninghamia lanceolate in the future.
The interaction of a shock wave with an existing fast flame (convection–reaction driven) is considered whereby the driving piston is not at a constant speed. Between the shock and the flame, there is an induction zone which governs the acoustic coupling between the flame and the shock. The asymptotic matching of this zone to the regions near the flame is presented. The case of a variable piston speed is allowed so that pulsed transient inputs to fast deflagrations can be simulated.
No abstract is provided for this article.
Randomization is widely used in nature-inspired optimization algorithms, and random walks are a form of randomization. This chapter introduces the basic concepts of random walks, Lévy flights and Markov chains as well as their links with optimization algorithms.
