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Traditional Optimization Techniques (traditional + optimization_techniques)
Selected AbstractsIndex tracking with constrained portfoliosINTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 1-2 2007Dietmar Maringer Passive portfolio management strategies, such as index tracking, are popular in the industry, but so far little research has been done on the cardinality of such a portfolio, i.e. on how many different assets ought to be included in it. One reason for this is the computational complexity of the associated optimization problems. Traditional optimization techniques cannot deal appropriately with the discontinuities and the many local optima emerging from the introduction of explicit cardinality constraints. More recent approaches, such as heuristic methods, on the other hand, can overcome these hurdles. This paper demonstrates how one of these methods, differential evolution, can be used to solve the constrained index-tracking problem. We analyse the financial implication of cardinality constraints for a tracking portfolio using an empirical study of the Down Jones Industrial Average. We find that the index can be tracked satisfactorily with a subset of its components and, more important, that the deviation between computed actual tracking error and the theoretically achievable tracking error out of sample is negligibly affected by the portfolio's cardinality. Copyright © 2007 John Wiley & Sons, Ltd. [source] Comparison of Two Evolutionary Algorithms for Optimization of Bridge Deck RepairsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 8 2006Hatem Elbehairy These decisions, however, represent complex optimization problems that traditional optimization techniques are often unable to solve. This article introduces an integrated model for bridge deck repairs with detailed life cycle costs of network-level and project-level decisions. Two evolutionary-based optimization techniques that are capable of handling large-size problems, namely Genetic Algorithms and Shuffled Frog Leaping, are then applied on the model to optimize maintenance and repair decisions. Results of both techniques are compared on case study problems with different numbers of bridges. Based on the results, the benefits of the bridge deck management system are illustrated along with various strategies to improve optimization performance. [source] Particle swarm optimization of TMD by non-stationary base excitation during earthquakeEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 9 2008A. Y. T. Leung Abstract There are many traditional methods to find the optimum parameters of a tuned mass damper (TMD) subject to stationary base excitations. It is very difficult to obtain the optimum parameters of a TMD subject to non-stationary base excitations using these traditional optimization techniques. In this paper, by applying particle swarm optimization (PSO) algorithm as a novel evolutionary algorithm, the optimum parameters including the optimum mass ratio, damper damping and tuning frequency of the TMD system attached to a viscously damped single-degree-of-freedom main system subject to non-stationary excitation can be obtained when taking either the displacement or the acceleration mean square response, as well as their combination, as the cost function. For simplicity of presentation, the non-stationary excitation is modeled by an evolutionary stationary process in the paper. By means of three numerical examples for different types of non-stationary ground acceleration models, the results indicate that PSO can be used to find the optimum mass ratio, damper damping and tuning frequency of the non-stationary TMD system, and it is quite easy to be programmed for practical engineering applications. Copyright © 2008 John Wiley & Sons, Ltd. [source] Solving resource constrained multiple project scheduling problems by random key-based genetic algorithmELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 8 2009Ikutaro Okada Abstract In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-rkGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known as an NP-hard problem and the objective in this paper is to minimize total complete time in the project. It is difficult to treat the rc-mPSP problems with traditional optimization techniques. The new approach proposed is based on the hybrid genetic algorithm (flc-rkGA) with fuzzy logic controller (FLC) and random-key encoding. For these rc-mPSP problems, we demonstrate that the proposed flc-rkGA to solve the rc-mPSP problem yields better results than several heuristic genetic algorithms presented in the computation result. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(8): 25,35, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10101 [source] | ||||||||||