Two variants of particle swarm optimization, called personal best PSO(PPSO) and simplified PPSO(SPPSO), are proposed. Empirical studies demonstrate that PPSO usually outperforms PSO both in computation quality and onvergent speed. Moreover, without incurring any new computational and logical operations, PPSO and SPPSO are simple and easy to implement. A first-order difference equation is developed to characterize the behaviors of PPSO and SPPSO. Theoretical analysis depicted that a particle stochastically moves within a region in real space. On each dimension, the center of the region approximately equals to a random weighted mean of the best positions found by an individual and its neighbors. This phenomenon is observed and verified from the trajectory profiles. The feasibility and capability of PPSO and SPPSO are tested on several high dimensional benchmark functions. They are also verified by applied to economic power dispatch problems with various kinds of cost functions as well as different constraints. Experimental results demonstrate that, for most of the problems, PPSO and SPPSO indeed can obtain better solutions than PSO.
In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.
Identify, analyze, and improve poorly performing queries that damage user experience and lead to lost revenue for your business. This book will help you make query tuning an integral part of your daily routine through a multi-step process that includes monitoring of execution times, identifying candidate queries for optimization, analyzing their current performance, and improving them to deliver results faster and with less overhead. Author Jesper Krogh systematically discusses each of these steps along with the data sources and the tools used to perform them. MySQL 8 Query Performance Tuning aims to help you improve query performance using a wide range of strategies. You will know how to analyze queries using both the traditional EXPLAIN command as well as the new EXPLAIN ANALYZE tool. You also will see how to use the Visual Explain feature to provide a visually-oriented view of an execution plan. Coverage of indexes includes indexing strategies and index statistics, and you will learn how histograms can be used to provide input on skewed data distributions that the optimizer can use to improve query performance. You will learn about locks, and how to investigate locking issues. And you will come away with an understanding of how the MySQL optimizer works, including the new hash join algorithm, and how to change the optimizer's behavior when needed to deliver faster execution times. You will gain the tools and skills needed to delight application users and to squeeze the most value from corporate computing resources. What You Will Learn Monitor query performance to identify poor performersChoose queries to optimize that will provide the greatest gainAnalyze queries using tools such as EXPLAIN ANALYZE and Visual ExplainImprove slow queries through a wide range of strategiesProperly deploy indexes and histograms to aid in creating fast execution plansUnderstand and analyze locks to resolve contention and increase throughput Who This Book Is For Database administrators and SQL developers who are familiar with MySQL and need to participate in query tuning. While some experience with MySQL is required, no prior knowledge of query performance tuning is needed.
PThis book provides an introduction into the least squares resolution of nonlinear inverse problems. The first goal is to develop a geometrical theory to analyze nonlinear least square (NLS) problems with respect to their quadratic wellposedness, i.e. both wellposedness and optimizability. Using the results, the applicability of various regularization techniques can be checked. The second objective of the book is to present frequent practical issues when solving NLS problems. Application oriented readers will find a detailed analysis of problems on the reduction to finite dimensions, the algebraic determination of derivatives (sensitivity functions versus adjoint method), the determination of the number of retrievable parameters, the choice of parametrization (multiscale, adaptive) and the optimization step, and the general organization of the inversion code. Special attention is paid to parasitic local minima, which can stop the optimizer far from the global minimum: multiscale parametrization is shown to be an efficient remedy in many cases, and a new condition is given to check both wellposedness and the absence of parasitic local minima./P PFor readers that are interested in projection on non-convex sets, Part II of this book presents the geometric theory of quasi-convex and strictly quasi-convex (s.q.c.) sets. S.q.c. sets can be recognized by their finite curvature and limited deflection and possess a neighborhood where the projection is well-behaved./P PThroughout the book, each chapter starts with an overview of the presented concepts and results./P
Genetic Algorithms (GA) as a tool for a search and optimizing methodology has now reached a mature stage. It has found many useful applications in both the scientific and engineering arenas. The main reason for this success is undoubtedly due to the advances that have been made in solid-state microelectronics fabrication that have, in turn, led to the proliferation of widely available, low cost, and speedy computers. The GA works on the Darwinian principle of natural selection for which the noted English philosopher, Herbert Spencer coined the phrase 'Survival of the fittest'. As a numerical optimizer, the solutions obtained by the GA are not mathematically oriented. Instead, GA possesses an intrinsic flexibility and the freedom to choose desirable optima according to design specifications. Whether the criteria of concern be nonlinear, constrained, discrete, multimodal, or NP hard, the GA is entirely equal to the challenge. In fact, because of the uniqueness of the evolutionary process and the gene structure of a chromosome, the GA processing mechanism can take the form ofparallelism and multiobjective. These provide an extra dimension for solutions where other techniques may have failed completely. It is, therefore, the aim ofthis booktogather together relevant GA materialthat has already been used and demonstrated in various engineering disciplines.
The Definitive Solutions-Oriented Guide to DB2 for z/OS: Now Fully Updated for Both v9 and v10! DB2 Developer's Guide is the world's #1 go-to source for on-the-job information on programming and administering DB2. Now, three-time IBM Information Champion Craig S. Mullins has thoroughly updated this classic for the newest versions of DB2 for z/OS: DB2 V9 andV10. This Sixth Edition builds on the unique approach that has made previous editions so valuable. It brings together condensed, easy-to-read coverage of all essential topics: information otherwise scattered through dozens of IBM and third-party documents. Throughout, Mullins offers focused drill-down on the key details DB2 developers need to succeed, with expert, field-tested implementation advice and realistic examples. Extensive updates address IBM's latest DB2 for z/OS innovations and best practices. Mullins introduces DB2's newest data types, performance and security enhancements, pureXML support, and much more. Whether you're a professional DB2 developer, DBA, sysadmin, or advanced user, this book will make you more productive, effective, and successful. Coverage includes . Modern DB2 SQL tools, tips, and tricks . Best practices for data definition, indexing, and change management . Large objects and object/relational databases . Temporal data support . DB2 security, authorization, and auditing . Dynamic SQL programming and DB2 stored procedures . 'Under the hood' with the DB2 Optimizer and Catalog . Performance monitoring in-depth: EXPLAIN, object monitoring, and RTS . REORG, RUNSTATS, REBIND: superior approaches to managing DB2 access path changes . DB2 tuning: environment, components, and resource governing . Optimizing DB2 utilities and commands
An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author's 'do, not study' approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization: * Describes and develops a variety of algorithms, including gradient based (such as Newton's, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization * Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values * Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling * Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for 'making the best choices' will find value in this introductory resource.