Librería Portfolio Librería Portfolio

Búsqueda avanzada

TIENE EN SU CESTA DE LA COMPRA

0 productos

en total 0,00 €

EXTREMAL OPTIMIZATION: FUNDAMENTALS, ALGORITHMS, AND APPLICATIONS
Título:
EXTREMAL OPTIMIZATION: FUNDAMENTALS, ALGORITHMS, AND APPLICATIONS
Subtítulo:
Autor:
LU, Y
Editorial:
CRC PRESS
Año de edición:
2016
Materia
ALGORITMOS
ISBN:
978-1-4987-0565-3
Páginas:
334
179,50 €

 

Sinopsis

Features

Uses analytical comparisons between EO and other popular evolutionary computations
Describes major EO algorithms with software workflows and simulation results
Introduces fundamentals and solutions clearly and makes complicated concepts and problems as simple as possible
Adds more numerical and benchmark examples to introduce problem maps and solutions step by step
Includes more industrial application cases to describe how to apply EO in solving real-world problems
Summary

Extremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors' recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as ´NP-hard´ and the ´phase transitions´ occurring on the search landscape.

Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO with a focus on self-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors' recent research results. Finally, it discusses applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling.

The authors present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. They also focus on the development of MEO and its applications. This book can be used as a reference for graduate students, research developers, and practical engineers who work on developing optimization solutions for those complex systems with hardness that cannot be solved with mathematical optimization or other computational intelligence, such as evolutionary computations.



Table of Contents

FUNDAMENTALS, METHODOLOGY, AND ALGORITHMS

General Introduction
Introduction
Understanding Optimization: From Practical Aspects
Phase Transition and Computational Complexity
CI-Inspired Optimization
Highlights of EO
Organization of the Book

Introduction to Extremal Optimization
Optimization with Extremal Dynamics
Multidisciplinary Analysis of EO
Experimental and Comparative Analysis on the Traveling Salesman Problems
Summary

Extremal Dynamics-Inspired Self-Organizing Optimization
Introduction
Analytic Characterization of COPs
Self-Organized Optimization
Summary

MODIFIED EO AND INTEGRATION OF EO WITH OTHER SOLUTIONS TO COMPUTATIONAL INTELLIGENCE

Modified Extremal Optimization
Introduction
Modified EO with Extended Evolutionary Probability Distribution
Multistage EO
Backbone-Guided EO
Population-Based EO
Summary

Memetic Algorithms with Extremal Optimization
Introduction to MAs
Design Principle of MAs
EO-LM Integration
EO-SQP Integration
EO-PSO Integration
EO-ABC Integration
EO-GA Integration
Summary

Multiobjective Optimization with Extremal Dynamics
Introduction
Problem Statement and Definition
Solutions to Multiobjective Optimization
EO for Numerical MOPs
Multiobjective 0/1 Knapsack Problem with MOEO
Mechanical Components Design with MOEO
Portfolio Optimization with MOEO
Summary

APPLICATIONS

EO for Systems Modeling and Control
Problem Statement
Endpoint Quality Prediction of Batch Production with MA-EO
EO for Kernel Function and Parameter Optimization in Support Vector Regression
Nonlinear Model Predictive Control with MA-EO
Intelligent PID Control with Binary-Coded EO
Summary

EO for Production Planning and Scheduling
Introduction
Problem Formulation
Hybrid Evolutionary Solutions with the Integration of GA and EO
Summary

References