Multiobjective Optimization Based on Rough Set Theory: Methodology and Application par Abd Elsameea Hussein, Mohamed, Allah A. Mousa, Abd, A. Elsaw: New paperback (2016)

A propos de cet article

New. SHIPS FROM MULTIPLE LOCATIONS.

N� de r�f. du vendeur D8S0-3-M-3659846856-6

Contacter le vendeur

Signaler cet article

D�tails bibliographiques

Titre: Multiobjective Optimization Based on Rough Set Theory: Methodology and Application
�diteur: LAP LAMBERT Academic Publishing
Date de parution: 2016
Langue: anglais
ISBN � 10 chiffres: 3659846856
ISBN � 13 chiffres: 9783659846854
Reliure: paperback
�tat de l'article: New
Type de livre: book

A propos de ce titre

Synopsis

In this book we present a new optimization algorithm, the proposed algorithm operates in two phases: in the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems (Economic Environmental Dispatch of Power Systems).

Pr�sentation de l'�diteur

In this book we present a new optimization algorithm, the proposed algorithm operates in two phases: in the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems (Economic Environmental Dispatch of Power Systems).

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.