Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table

Single temperature for Monte Carlo optimization on complex landscapes

We propose a new strategy for Monte Carlo (MC) optimization on rugged multidimensional landscapes. The strategy is based on querying the statistical properties of the landscape in order to find the temperature at which the mean first passage time across the current region of the landscape is minimized. Thus, in contrast to other algorithms such as simulated annealing (SA), we explicitly match the temperature schedule to the statistics of landscape irregularities. In cases where this statistics is approximately the same over the entire landscape, or where non-local moves couple distant parts of the landscape, single-temperature MC will outperform any other MC algorithm with the same move set. We also find that in strongly anisotropic Coulomb spin glass and traveling salesman problems, the only relevant statistics (which we use to assign a single MC temperature) is that of irregularities in low-energy funnels. Our results may explain why protein folding in nature is efficient at room temperatures.Comment: 5 pages, 3 figure

Accurate ionic forces and geometry optimization in linear-scaling density-functional theory with local orbitals

Linear scaling methods for density-functional theory (DFT) simulations are formulated in terms of localized orbitals in real space, rather than the delocalized eigenstates of conventional approaches. In local-orbital methods, relative to conventional DFT, desirable properties can be lost to some extent, such as the translational invariance of the total energy of a system with respect to small displacements and the smoothness of the potential-energy surface. This has repercussions for calculating accurate ionic forces and geometries. In this work we present results from onetep, our linear scaling method based on localized orbitals in real space. The use of psinc functions for the underlying basis set and on-the-fly optimization of the localized orbitals results in smooth potential-energy surfaces that are consistent with ionic forces calculated using the Hellmann-Feynman theorem. This enables accurate geometry optimization to be performed. Results for surface reconstructions in silicon are presented, along with three example systems demonstrating the performance of a quasi-Newton geometry optimization algorithm: an organic zwitterion, a point defect in an ionic crystal, and a semiconductor nanostructure.<br/

The Coupled Electron-Ion Monte Carlo Method

In these Lecture Notes we review the principles of the Coupled Electron-Ion Monte Carlo methods and discuss some recent results on metallic hydrogen.Comment: 38 pages, 6 figures, Lecture notes for the International School of Solid State Physics, 34th course: "Computer Simulation in Condensed Matter: from Materials to Chemical Biology", 20 July-1 August 2005 Erice (Italy). To appear in Lecture Notes in Physics (2006

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Cartesian genetic programming for trading: a preliminary investigation

In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and trading strategies using the evolved predictors are evaluated along three dimensions (return, maximum drawdown and recovery factor) and against four different financial datasets (the Euro/US dollar exchange rate and the Dow Jones Industrial Average during periods from 2006 and 2010). We show that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns