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.
The differentiation and integration we just discussed so far are for functions with a single variable, and they are univariate functions. For functions with more than one variable, partial derivatives and multiple integrals are needed, which will be the main topics of this chapter.
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.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Currently, sound intensity instrument calibrations consist of a paired microphone phase and pressure calibrations, and the general uncertainty analysis focuses on how to calculate and express the uncertainty in the pressure lever and phase difference. However, no guidance for intensity level uncertainty is available. A significant barrier is the requirement to develop a measurement model that constitutes a relationship between the output quantities and input quantities, known to be the paired microphone pressure levels and phase difference. The Beijing Aerospace Institute of Metrology and Measurement Technology (AIM) is addressing this issue using a double-coupler calibration system, which is the incentive for the systematic evaluation of the associated measurement uncertainty in this study. A set of methodologies based on the plane-wave model is developed for estimating intensity and particle velocity uncertainty for sound intensity instruments.
Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.
The cuckoo search (CS) algorithm is a population-based algorithm, and its convergence is usually higher, even for potentially higher-dimensional problems. The algorithm is stable and robust, and has been applied in various applications. This chapter introduces the fundamentals of the standard cuckoo search algorithm with detailed descriptions of the main steps. Some demo codes in both Matlab and C++ are provided. Discussions of the efficient generation of L'evy flights are also given with examples.
The bat algorithm (BA) is a nature-inspired algo-rithm, which has recently been applied in many applications. BA can deal with both continuous optimization and discrete optimization problems. The literature has expanded significantly in the past few years, this paper provides a timely review of the latest developments. We also highlight some topics for further research.
Constrained multi-objective optimization problems exist widely in real-world applications, and they involve a simultaneous optimization of multiple and often conflicting objectives subject to several equality and/or inequality constraints. To deal with these problems, a crucial issue is how to handle constraints effectively. This paper proposes a simple yet effective constrained decomposition-based multi-objective evolutionary algorithm. In the proposal, the evolutionary process is divided into two stages in which constraints are handled differently. In the first stage, constraints are totally ignored and the population is pulled toward the unconstrained Pareto-optimal front (PF) by optimizing objectives only. This can help the proposed algorithm handle well problems with the following features, i.e., the constrained PF has an intersection with the unconstrained counterpart, and there are infeasible regions blocking the way of convergence. In the second stage, with the purpose of approximating the constrained PF well,constraint satisfaction is emphasized over objective minimization.Moreover, different evolutionary frameworks are adopted in the two stages to promote the performance of the algorithm as much as possible. The proposed algorithm is comprehensively compared with several state-of-the-art algorithms on 39 problems (with 266 test instances in total), including one real-world problem (with 36 instances) in search-based software engineering. As shown by the experimental results, the new algorithm performs best on the majority of these problems, particularly on those with the aforementioned features. In summary, the suggested algorithm provides an effective way of handling constrained multi-objective optimization problems.
Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.
In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.
