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.

L'autore:

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.

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