The Thorup-Zwick (TZ) compact routing scheme is the first generic stretch-3 routing scheme delivering a nearly optimal per-node memory upper bound. Using both direct analysis and simulation, we derive the stretch distribution of this routing scheme on Internet-like inter-domain topologies. By investigating the TZ scheme on random graphs with power-law node degree distributions, P/sub k//spl sime/k/sup -/spl gamma//, we find that the average TZ stretch is quite low and virtually independent of /spl gamma/. In particular, for the Internet inter-domain graph with /spl gamma//spl sime/2.1, the average TZ stretch is around 1.1, with up to 70% of all pairwise paths being stretch-1 (shortest possible). As the network grows, the average stretch slowly decreases. We find routing table sizes to be very small (around 50 records for 104-node networks), well below their theoretical upper bounds. Furthermore, we find that both the average shortest path length (i.e. distance) d~ and width of the distance distribution /spl sigma/ observed in the real Internet inter-AS graph have values that are very close to the minimums of the average stretch in the d~- and /spl sigma/ -directions. This leads us to the discovery of a unique critical point of the average TZ stretch as a function of d~ and /spl sigma/. The Internet's distance distribution is located in a close neighborhood of this point. This is remarkable given the fact that the Internet inter-domain topology has evolved without any direct attention paid to properties of the stretch distribution. It suggests the average stretch function may be an indirect indicator of the optimization criteria influencing the Internet's inter-domain topology evolution.
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.
Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this chapter, we carry out a critical analysis of these SI-based algorithms and other nature-inspired algorithms by analyzing the ways they mimic evolutionary operators. We also analyze ways of achieving exploration and exploitation in algorithms by using mutation, crossover, and selection. In addition, we study these algorithms using dynamic systems, self-organization, and the Markov chain framework. Finally, we provide some discussion and topics for further research.
Modelling, simulation and optimization form an integrated part of modern design practice in engineering and industry. Tremendous progress has been observed for all three components over the last few decades. However, many challenging issues remain unresolved, and the current trends tend to use nature-inspired algorithms and surrogate-based techniques for modelling and optimization. This 4th workshop on Computational Optimization, Modelling and Simulation (COMS 2013) at ICCS 2013 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry. In this review paper, we will analyse the recent trends in modelling and optimization, and their associated challenges. We will discuss important topics for further research, including parameter-tuning, large-scale problems, and the gaps between theory and applications.
Computer-aided full-wave electromagnetic (EM) analysis has been used in microwave engineering for the past decade. Initially, its main application area was design verification. Today, EM-simulation-driven optimization and design closure become increasingly important due to the complexity of microwave structures and increasing demands for accuracy. In many situations, theoretical models of microwave structures can only be used to yield the initial designs that need to be further fine-tuned to meet given performance requirements. In addition, EM-based design is a must for a growing number of microwave devices such as ultra-wideband (UWB) antennas, dielectric resonator antennas and substrate-integrated circuits. For circuits like these, no design-ready theoretical models are available, so design improvement can only be obtained through geometry adjustments based on repetitive, time-consuming simulations. On the other hand, various interactions between microwave devices and their environment, such as feeding structures and housing, must be taken into account, and this is only possible through full-wave EM analysis. Electromagnetic simulations can be highly accurate, but they tend to be computationally expensive. Therefore, practical design optimization methods have to be computationally efficient, so that the number of CPU-intensive high-fidelity EM simulations is reduced as much as possible during the design process. For the same reasons, techniques for creating fast yet accurate models of microwave structures become crucially important. In this edited book, the authors strive to review the state-of-the-art simulation-driven microwave design optimization and modeling. A group of international experts specialized in various aspects of microwave computer-aided design summarize and review a wide range of the latest developments and real-world applications. Topics include conventional and surrogate-based design optimization techniques, methods exploiting adjoint sensitivity, simulation-based tuning, space mapping, and several modeling methodologies, such as artificial neural networks and kriging. Applications and case studies include microwave filters, antennas, substrate integrated structures and various active components and circuits. The book also contains a few introductory chapters highlighting the fundamentals of optimization and modeling, gradient-based and derivative-free algorithms, metaheuristics, and surrogate-based optimization techniques, as well as finite difference and finite element methods. Readership: Graduates, lecturers, and researchers in electrical engineering, as well as engineers who use numerical optimization in their design work. This book will be of great interest to researchers in the fields of microwave engineering, antenna design, and computational electromagnetics.
It is now five years since the launch of the International Journal of Bio-Inspired Computation (IJBIC). At the same time, significant new progress has been made in the area of bio-inspired computation. This review paper summarizes the success and achievements of IJBIC in the past five years, and also highlights the challenges and key issues for further research.
Purpose – Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. Design/methodology/approach – The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence. Findings – A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO. Originality/value – A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.
Many geophysical processes can be modelled as interconnected networks. The small‐world network model has recently attracted much attention in physics and applied sciences. In this paper, we modify and apply the small‐world network theory to model geophysical processes such as diffusion and transport in disordered porous rocks. We develop an analytical approach as well as numerical simulations to characterize the properties of small‐world networks in geophysics using system saturation time and fractal dimensions.
Nature-inspired algorithms can be flexible and efficient for solving optimization problems. There are a wide spectrum of nature-inspired algorithms in the literature, and most of such algorithms are based on swarm intelligence. This chapter provides an overview of some widely used algorithms for optimization. Their main characteristics will be discussed in comparison with traditional algorithms such as gradient-based algorithms. Some open problems concerning swarm intelligence and nature-inspired computation will be highlighted.
Mathematical Foundations Algorithmic Complexity Ordinary Differential Equations Partial Differential Equations Roots of Nonlinear Equations Numerical Integration Computational Linear Algebra Interpolation Finite Difference Methods for ODEs Finite Difference Methods for PDEs Finite Volume Method Finite Element Method Mathematical Optimization Mathematical Programming Stochastic Models Data Modeling Metaheuristic Methods Bee Algorithms Swarm Optimization.
A nonlinear small-world network model has been presented to investigate the effect of nonlinear interaction and time delay on the dynamic properties of small-world networks. Both numerical simulations and analytical analysis for networks with time delay and nonlinear interaction show chaotic features in the system response when nonlinear interaction is strong enough or the length scale is large enough. In addition, the small-world system may behave very differently on different scales. Time-delay parameter also has a very strong effect on properties such as the critical length and response time of small-world networks.
Hybrid flowshop scheduling problems include the generalization of flowshops with parallel machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard. Hence, researchers have proposed many heuristics and metaheuristic algorithms to tackle such challenging tasks. In this letter, a recently developed discrete firefly algorithm is extended to solve hybrid flowshop scheduling problems with two objectives. Makespan and mean flow time are the objective functions considered. Computational experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm outperforms many other metaheuristics in the literature.
No abstract is provided for this article.
This technical note reports the results of a set of tests of software toolboxes for optimisation and uncertainty evaluation using finite element models.
