An improved evolutionary algorithm for handling many-objective optimization problems

• Published in: Applied soft computing 2017-03, Vol.52, p.1239-1252
• Main Authors: Mohammadi, S., Monfared, M.A.S., Bashiri, M.
• Format: Article
• Language: English
• Subjects: Evolutionary algorithm; Many-objective optimization; NSGA-II; Reference point
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2016.08.042

[Display omitted] •A new composite fitness evaluation function is proposed for many-objective cases.•The convergence, diversity and user preferences are considered in this function.•The proposed fitness function guides the search to the preferred region of the Pareto front.•Using the proposed function in an EMO algorithm led to better results.•The algorithm has comparable performance with a reasonable amount of computing. It has been shown that the multi-objective evolutionary algorithms (MOEAs) act poorly in solving many-objective optimization problems which include more than three objectives. The research emphasis, in recent years, has been put into improving the MOEAs to enable them to solve many-objective optimization problems efficiently. In this paper, we propose a new composite fitness evaluation function, in a novel way, to select quality solutions from the objective space of a many-objective optimization problem. Using this composite function, we develop a new algorithm on a well-known NSGA-II and call it FR-NSGA-II, a fast reference point based NSGA-II. The algorithm is evaluated for producing quality solutions measured in terms of proximity, diversity and computational time. The working logic of the algorithm is explained using a bi-objective linear programming problem. Then we test the algorithm using experiments with benchmark problems from DTLZ family. We also compare FR-NSGA-II with four competitive algorithms from the extant literature to show that FR-NSGA-II will produce quality solutions even if the number of objectives is as high as 20.