Implementation techniques for the SCFO experimental optimization framework

The material presented in this document is intended as a comprehensive, implementation-oriented supplement to the experimental optimization framework presented in a companion document. The issues of physical degradation, unknown Lipschitz constants, measurement/estimation noise, gradient estimation, sufficient excitation, and the handling of soft constraints and/or a numerical cost function are all addressed, and a robust, implementable version of the sufficient conditions for feasible-side global convergence is proposed.Comment: supplementary document; 66 page

Analytical gradient-based optimization of offshore wind turbine substructures under fatigue and extreme loads

Design optimization of offshore wind turbine support structures is an expensive task due to the highly-constrained, non-convex and non-linear nature of the design problem. A good depth of detail in the problem formulation can give useful insights in the practical design process, but may also compromise the efficiency. This paper presents an analytical gradient-based method to solve the problem in an effective and efficient way. The design sensitivities of the objective and constraint functions are evaluated analytically, while the optimization procedure is performed in the time domain, subjected to sizing, eigenfrequency, extreme load and fatigue load constraints. A case study on the OC4 and UpWind jacket substructures show that the method was reliable and consistent in delivering superior efficiency and accuracy in the optimization study, as compared with the conventional finite difference approach. The global optimum was probably achieved in the design optimization process, where the large number of design constraints implemented can possibly be the blessing in disguise, as they seem to enable the optimizer to find the global optimum. Both the buckling and fatigue load constraints had significant influence over the design of tubular members and joints, while each component is oriented to maximize the utilization against the prescribed limit state functions.© 2016 Published by Elsevier Ltd. Open Access

An Effective Routability-driven Placer for Mixed-size Circuit Designs

We propose a routability-driven analytical placer that aims at distributing pins evenly. This is accomplished by including a group of pin density constraints in its mathematical formulation. Moreover, for mixed-size circuits, we adopt a scaled smoothing method to cope with fixed macro blocks. As a result, we have fewer cells overlapping with fixed blocks after global placement, implying that the optimization of the global placement solution is more accurate and that the global placement solution resembles a legal solution more. Routing solutions obtained by a commercial router show that for most benchmark circuits, better routing results can be achieved on the placement results generated by our pin density oriented placer

Constraint-based Query Distribution Framework for an Integrated Global Schema

Distributed heterogeneous data sources need to be queried uniformly using global schema. Query on global schema is reformulated so that it can be executed on local data sources. Constraints in global schema and mappings are used for source selection, query optimization,and querying partitioned and replicated data sources. The provided system is all XML-based which poses query in XML form, transforms, and integrates local results in an XML document. Contributions include the use of constraints in our existing global schema which help in source selection and query optimization, and a global query distribution framework for querying distributed heterogeneous data sources.Comment: The Proceedings of the 13th INMIC 2009), Dec. 14-15, 2009, Islamabad, Pakistan. Pages 1 - 6 Print ISBN: 978-1-4244-4872-2 INSPEC Accession Number: 11072575 Date of Current Version : 15 January 201

Comparison of Direct Multiobjective Optimization Methods for the Design of Electric Vehicles

"System design oriented methodologies" are discussed in this paper through the comparison of multiobjective optimization methods applied to heterogeneous devices in electrical engineering. Avoiding criteria function derivatives, direct optimization algorithms are used. In particular, deterministic geometric methods such as the Hooke & Jeeves heuristic approach are compared with stochastic evolutionary algorithms (Pareto genetic algorithms). Different issues relative to convergence rapidity and robustness on mixed (continuous/discrete), constrained and multiobjective problems are discussed. A typical electrical engineering heterogeneous and multidisciplinary system is considered as a case study: the motor drive of an electric vehicle. Some results emphasize the capacity of each approach to facilitate system analysis and particularly to display couplings between optimization parameters, constraints, objectives and the driving mission

Thermal food processing computation software

The objective of this research consisted of developing the two following thermal food processing software: “F-CALC” is software developed to carry out thermal process calculations based on the well-known Ball's formula method, and “OPT-PROx” is software for thermal food processing optimization based on variable retort temperature processing and global optimization technique. Time-temperature data loaded from Excel-file is used by “F-CALC” software to evaluate the heat penetration parameters jh and fh, as well as to compute process lethality for given process time or vice versa. The possibility of computing the process time and lethality for broken heating curves is included. The diversity of thermal food processing optimization problems with different objectives and required constraints are solvable by “OPT-PROx” software. The adaptive random search algorithm coupled with penalty functions approach, and the finite difference method with cubic spline approximation are utilized by “OPT-PROx” for simulation and optimization thermal food processes. The possibility of estimating the thermal diffusivity coefficient based on the mean squared error function minimization is included. The “OPT-PROx” software was successfully tested on the real thermal food processing problems, namely in the case of total process time minimization with a constraint for average and surface retentions the “OPT-PROx” demonstrates significant advantage over the traditional constant temperature processes in terms of process time and final product quality. The developed user friendly dialogue and used numerical procedures make the “F-CALC” and “OPT-PROx” software extremely useful for food scientists (research and education) and engineers (real thermal food process evaluation and optimization)