Beschreibung

This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.

Highlights recent research on Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms

Provides an overview of the different archiving methods which allow convergence of Multi-objective evolutionary algorithms in a stochastic sense

Presents theory as well as applications

Inhalt
Introduction.- Multi-objective Optimization.- The Framework.- Computing the Entire Pareto Front.- Computing Gap Free Pareto Fronts.- Using Archivers within MOEAs.- Test Problems.

Produktinformationen

Titel:	Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
Autor:	Carlos Hernández   Oliver Schütze
EAN:	9783030637729
ISBN:	3030637727
Format:	Fester Einband
Herausgeber:	Springer International Publishing
Genre:	Allgemeines & Lexika
Anzahl Seiten:	248
Gewicht:	541g
Größe:	H241mm x B160mm x T19mm
Jahr:	2021
Untertitel:	Englisch
Auflage:	1st ed. 2021