Authors:
Ghizlane Aboulbaroud
1
and
Driss Mentagui
2
Affiliations:
1
Applied Mathematics Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra, Moroccoo, Morocco
;
2
Applied Mathematics Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra, Moroccoo, Applied Mathematics Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco
Keyword(s):
Evolutionary algorithm, many-objective optimization, decomposition, Pareto front, MOEA/D, IPBI approach.
Abstract:
In real life, multiobjective evolutionary algorithms have many areas of applications, such as intelligence transportations systems, management problems, data mining, data-analysis and so on. Due to the importance of these problems, researchers have investigated several approaches to deal with them. Decomposition is one of the basic strategies used in multiobjective evolutionary optimization. In this paper, a modified iMOEA/D evolutionary algorithm based decomposition is suggested. This proposition allows dealing with Many-objective optimization problems with complicated Pareto fronts. The performance of this algorithm is demonstrated using a set of benchmark problems in comparison with other recently proposed algorithms.