On Multi-objective Optimization Based on Ant Colony Optimization: Developing an Ant Colony Optimization Algorithm for Engineering Applications - Couverture souple

Masoud Rizk Allah, Rizk; Mousa, Abd Allah A.

 
9783659553226: On Multi-objective Optimization Based on Ant Colony Optimization: Developing an Ant Colony Optimization Algorithm for Engineering Applications
Couverture souple
ISBN 10 : 3659553220 ISBN 13 : 9783659553226
Editeur : LAP LAMBERT Academic Publishing, 2014

Synopsis

Ant Colony Optimization (ACO) is a meta-heuristic algorithm which has been successfully applied to tackle various combinatorial optimization problems, but its ability to cope with multi-objective optimization problems is yet to be explored widely. Since most real-world search and optimization problems are naturally posed as non-linear programming problems having multi-objective problems. Therefore, the principal goal of this work aims to implement a specialized version of the ant colony optimization algorithm capable of finding a set of solutions for multi-objective optimization problems. Features relevant to ant colony optimization include a highly efficient form of best-path exploitation (pheromone detection), and a sensible mechanism for exploration (probabilistic path selection). The results demonstrate superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems and engineering applications.

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Présentation de l'éditeur

Ant Colony Optimization (ACO) is a meta-heuristic algorithm which has been successfully applied to tackle various combinatorial optimization problems, but its ability to cope with multi-objective optimization problems is yet to be explored widely. Since most real-world search and optimization problems are naturally posed as non-linear programming problems having multi-objective problems. Therefore, the principal goal of this work aims to implement a specialized version of the ant colony optimization algorithm capable of finding a set of solutions for multi-objective optimization problems. Features relevant to ant colony optimization include a highly efficient form of best-path exploitation (pheromone detection), and a sensible mechanism for exploration (probabilistic path selection). The results demonstrate superiority of the proposed algorithm and confirm its potential to solve the multi-objective problems and engineering applications.

Biographie de l'auteur

Rizk M. Rizk Allah obtained his Ph.D in Engineering Mathematics from Faculty of Engineering ,Menoufia University, Egypt. He has been with the Basic Engineering Sciences Department, Faculty of Engineering ,Menoufia University, since 2005. He is especially interested in swarm intelligence techniques,evolutionary algorithms and operations research.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2014
Langue
anglais
ISBN 10 
3659553220
ISBN 13 
9783659553226
Reliure
Broché
Nombre de pages
176
Coordonnées du fabricant
non disponible
Personne responsable
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