Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Introduction to global optimization techniques

Introduction to global optimization techniques

For access to this article, please select a purchase option:

Buy chapter PDF
£10.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Nonlinear Optimization in Electrical Engineering with Applications in MATLAB® — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In previous chapters, we addressed a number of optimization techniques for solving unconstrained and constrained optimization problems. All these techniques obtain a local minimum of the problem. This minimum may not be the best possible solution. The optimization problem may have a better minimum with an improved value of the objective function. To illustrate this case, consider the objective function: f(x) = - sin (x) / x This objective function has an infinite number of local minima. A number of these local minima are shown in Figure 9.1. This problem has only one global minimum at the point x = 0. The value of the objective function at this optimal point is f * = -1.0, which is lowest value over all other values of the parameter x. While in some problems, finding a local minimum with a reasonable value of the objective function is acceptable, in other applications it is mandatory to find the global minimum of the problem. Over the years, many techniques were developed for finding the global minimum of a nonlinear optimization problem. These techniques include Statistical Optimization [3], Simulated Annealing [4], Genetic Algorithms [5], Particle Swarm Optimization (PSO) [6], Weed Invasive Optimization [7], Wind Optimization [8], and Ant Colony Optimization [9], just to mention a few. All of these techniques introduce an element of randomness in the iterations to escape local minima. Some of these techniques are inspired by nature, which is always able to find the global minimum of its optimization problems.

Chapter Contents:

  • 9.1 Introduction
  • 9.2 Statistical optimization
  • 9.3 Nature-inspired global techniques
  • 9.3.1 Simulated annealing
  • 9.3.2 Genetic algorithms
  • 9.3.3 Particle swarm optimization
  • A9.1 Least pth optimization of filters
  • A9.2 Pattern recognition
  • References
  • Problems

Inspec keywords: particle swarm optimisation

Other keywords: nonlinear optimization problem; global optimization techniques; genetic algorithms; unconstrained optimization problems; objective function; particle swarm optimization; ant colony optimization; simulated annealing; optimal point; wind optimization; invasive optimization; statistical optimization; constrained optimization problems; PSO

Subjects: Optimisation techniques; Optimisation techniques; Optimisation

Preview this chapter:
Zoom in
Zoomout

Introduction to global optimization techniques, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbsp008e/PBSP008E_ch9-1.gif /docserver/preview/fulltext/books/pc/pbsp008e/PBSP008E_ch9-2.gif

Related content

content/books/10.1049/pbsp008e_ch9
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address