On Multi-objective Optimization Based on Ant Colony Optimization | Developing an Ant Colony Optimization Algorithm for Engineering Applications

Rizk Masoud Rizk Allah (u. a.)

ISBN 10: 3659553220 / ISBN 13: 9783659553226

Published by LAP LAMBERT Academic Publishing, 2014

About this Item

On Multi-objective Optimization Based on Ant Colony Optimization | Developing an Ant Colony Optimization Algorithm for Engineering Applications | Rizk Masoud Rizk Allah (u. a.) | Taschenbuch | 176 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659553226 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 105217323

Bibliographic Details

• Title: On Multi-objective Optimization Based on Ant Colony Optimization | Developing an Ant Colony Optimization Algorithm for Engineering Applications
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3659553220
• ISBN 13: 9783659553226
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 12D millimeters 220 millimeters width by 150 millimeters height by 12 millimeters depth
• Weight: 280 grams
• Seller catalogs:
  • Bücher

About this title

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.

About the Author

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.