A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm

The role of optimization in both engineering analysis and designis continually expanding. As such, faster and more powerful optimization algorithms are in constant demand. In this dissertation, motivated by problems from engineering analysis and design, new Sequential Quadratic Programming (SQP) algorithms generating feasible iterates are described and analyzed. What distinguishes these algorithms from previous feasible SQP algorithms is a dramatic reduction in the amount of computation required to generate a new iterate while still enjoying the same global and fast local convergence properties.First, a basic algorithm which solves the standard smooth inequality constrained nonlinear programming problem is considered. The main idea involves a simple perturbation of the Quadratic Program (QP) for the standard SQP search direction. The perturbation has the property that a feasible direction is always obtained and fast local convergence is preserved. An extension of the basic algorithm is then proposed which solves the inequality constrained mini-max problem. The algorithm exploits the special structure of the problem and is shown to have the same global and local convergence properties as the basic algorithm.Next, the algorithm is extended to efficiently solve problems with very many objective and/or constraint functions. Such problems often arise in engineering design as, e.g., discretized Semi-Infinite Programming (SIP) problems. The key feature of the extension is that only a small subset of the objectives and constraints are used to generate a search directionat each iteration. The result is much smaller QP sub-problems and fewer gradient evaluations.The algorithms all have been implemented and tested. Preliminary numericalresults are very promising. The number of iterations and function evaluations required to converge to a solution are, on average, roughly the same as for a widely available state-of-the-art feasible SQP implementation, whereas the amount of computation required per iteration is much less. The ability of the algorithms to effectively solve real problems from engineering design is demonstrated by considering signal set design problems for optimal detection in the presence of non-Gaussian noise

Optimal passive filter design for effective utilization of cables and transformers under non-sinusoidal conditions

Transformers and cables have overheating and reduced loading capabilities under non-sinusoidal conditions due to the fact that their losses increases with not only rms value but also frequency of the load current. In this paper, it is aimed to employ passive filters for effective utilization of the cables and transformers in the harmonically contaminated power systems. To attain this goal, an optimal passive filter design approach is provided to maximize the power factor definition, which takes into account frequency-dependent losses of the power transmission and distribution equipment, under non-sinusoidal conditions. The obtained simulation results show that the proposed approach has a considerable advantage on the reduction of the total transmission loss and the transformer loading capability under non-sinusoidal conditions when compared to the traditional optimal filter design approach, which aims to maximize classical power factor definition. On the other hand, for the simulated system cases, both approaches lead to almost the same current carrying (or loading) capability value of the cables. © 2014 IEEE.This work is supported by Turkish Republic Ministry of Science, Industry and Technology and BEST Transformers Co. under the project number of 01008.STZ.2011 - 2

Comparison of different optimization criteria for optimal sizing of hybrid active power filters parameters

Praise Worthy Prize granted a permission for Brunel University London to archive this article in BURA.Harmonic distortion in power systems has increased considerably due to the increasing use of nonlinear loads in industrial firms and elsewhere. This distortion can give rise to overheating in all sectors of the power system, leading to reduced efficiency, reliability, operational life and sometimes failure. This article seeks to propose a new methodology for the optimal sizing of hybrid active power filter (HPF) parameters in order to overcome the difficulties in hybrid power filters design when estimating the preliminary feasible values of the parameters. Sequential Quadratic Programming based on FORTRAN subroutines is used to find out the planned filter size in two different optimization criteria depending on design concerns. The first criterion is to minimize the total voltage harmonic distortion. The second one is to maximize the load power factor, while taking into account compliance with IEEE standard 519-1992 limits for the total voltage harmonic distortion and the power factor.The effectiveness of the proposed filter is discussed using four exemplary case

Study of hybrid strategies for multi-objective optimization using gradient based methods and evolutionary algorithms

Most of the optimization problems encountered in engineering have conflicting objectives. In order to solve these problems, genetic algorithms (GAs) and gradient-based methods are widely used. GAs are relatively easy to implement, because these algorithms only require first-order information of the objectives and constraints. On the other hand, GAs do not have a standard termination condition and therefore they may not converge to the exact solutions. Gradient-based methods, on the other hand, are based on first- and higher-order information of the objectives and constraints. These algorithms converge faster to the exact solutions in solving single-objective optimization problems, but are inefficient for multi-objective optimization problems (MOOPs) and unable to solve those with non-convex objective spaces. The work in this dissertation focuses on developing a hybrid strategy for solving MOOPs based on feasible sequential quadratic programming (FSQP) and nondominated sorting genetic algorithm II (NSGA-II). The hybrid algorithms developed in this dissertation are tested using benchmark problems and evaluated based on solution distribution, solution accuracy, and execution time. Based on these performance factors, the best hybrid strategy is determined and found to be generally efficient with good solution distributions in most of the cases studied. The best hybrid algorithm is applied to the design of a crushing tube and is shown to have relatively well-distributed solutions and good efficiency compared to solutions obtained by NSGA-II and FSQP alone

Volatility Transmission in Emerging European Foreign Exchange Markets

This paper studies the dynamics of volatility transmission between Central European currencies and euro/dollar foreign exchange using model-free estimates of daily exchange rate volatility based on intraday data. We formulate a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates. We find evidence of statistically significant intra-regional volatility spillovers among the Central European foreign exchange markets. With the exception of the Czech currency, we find no significant spillovers running from euro/dollar to the Central European foreign exchange markets. To measure the overall magnitude and evolution of volatility transmission over time, we construct a dynamic version of the Diebold-Yilmaz volatility spillover index, and show that volatility spillovers tend to increase in periods characterized by market uncertainty.foreign exchange markets, volatility, spillovers, intraday data, nonlinear dynamics

Reduction method with system analysis for multiobjective optimization-based design

An approach for reducing the number of variables and constraints, which is combined with System Analysis Equations (SAE), for multiobjective optimization-based design is presented. In order to develop a simplified analysis model, the SAE is computed outside an optimization loop and then approximated for use by an operator. Two examples are presented to demonstrate the approach