Convex hull ranking algorithm for multi-objective evolutionary algorithms | Zendy

Mansoor Davoodi | Zendy; Ali Mohades | Zendy; Jafar Rezaei | Zendy

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Convex hull ranking algorithm for multi-objective evolutionary algorithms

Author(s) -

Mansoor Davoodi,

Ali Mohades,

Jafar Rezaei

Publication year - 2011

Publication title -

scientia iranica

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.299

H-Index - 51

eISSN - 2345-3605

pISSN - 1026-3098

DOI - 10.1016/j.scient.2011.08.017

Subject(s) - convex hull , algorithm , evolutionary algorithm , benchmark (surveying) , ranking (information retrieval) , output sensitive algorithm , sorting , computer science , mathematical optimization , convex combination , regular polygon , mathematics , convex optimization , machine learning , geometry , geodesy , geography

Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity depends on finding non-dominated fronts, this paper introduces a new method for ranking the solutions of an evolutionary algorithm’s population. First, we investigate the relation between the convex hull and non-dominated solutions, and discuss the complexity time of the convex hull and non-dominated sorting problems. Then, we use convex hull concepts to present a new ranking procedure for multi-objective evolutionary algorithms. The proposed algorithm is very suitable for convex multi-objective optimization problems. Finally, we apply this method as an alternative ranking procedure to NSGA-II for non-dominated comparisons, and test it using some benchmark problems

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