Simulated annealing for generalized Skyrme models

We use a simulated annealing algorithm to find the static field configuration with the lowest energy in a given sector of topological charge for generalized SU(2) Skyrme models. These numerical results suggest that the following conjecture may hold: the symmetries of the soliton solutions of extended Skyrme models are the same as for the Skyrme model. Indeed, this is verified for two effective Lagrangians with terms of order six and order eight in derivatives of the pion fields respectively for topological charges B=1 up to B=4. We also evaluate the energy of these multi-skyrmions using the rational maps ansatz. A comparison with the exact numerical results shows that the reliability of this approximation for extended Skyrme models is almost as good as for the pure Skyrme model. Some details regarding the implementation of the simulated annealing algorithm in one and three spatial dimensions are provided.Comment: 14 pages, 6 figures, added 2 reference

GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

YesProposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problem

Simulated Annealing for Topological Solitons

The search for solutions of field theories allowing for topological solitons requires that we find the field configuration with the lowest energy in a given sector of topological charge. The standard approach is based on the numerical solution of the static Euler-Lagrange differential equation following from the field energy. As an alternative, we propose to use a simulated annealing algorithm to minimize the energy functional directly. We have applied simulated annealing to several nonlinear classical field theories: the sine-Gordon model in one dimension, the baby Skyrme model in two dimensions and the nuclear Skyrme model in three dimensions. We describe in detail the implementation of the simulated annealing algorithm, present our results and get independent confirmation of the studies which have used standard minimization techniques.Comment: 31 pages, LaTeX, better quality pics at http://www.phy.umist.ac.uk/~weidig/Simulated_Annealing/, updated for publicatio

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), using some randomly generated instances.genetic algorithm;heuristics;opportunities;maintenance optimization;memetic algorithm

Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation

In this paper, some vital methodological issues of spatial microsimulation modelling for small area estimation have been addressed, with a particular emphasis given to the reweighting techniques. Most of the review articles in small area estimation have highlighted methodologies based on various statistical models and theories. However, spatial microsimulation modelling is emerging as a very useful alternative means of small area estimation. Our findings demonstrate that spatial microsimulation models are robust and have advantages over other type of models used for small area estimation. The technique uses different methodologies typically based on geographic models and various economic theories. In contrast to statistical model-based approaches, the spatial microsimulation model-based approaches can operate through reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different iterative algorithms and that their properties also vary. The study also points out a new method for spatial microsimulation modellingBayesian prediction approach; combinatorial optimisation; GREGWT; microdata; small area estimation; spatial microsimulation