Computers and Electrical Engineering 58 (2017) 126–143 Contents lists available at ScienceDirect Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng Search space-based multi-objective optimization evolutionary algorithm  Darshan Vishwasrao Medhane, Arun Kumar Sangaiah ∗ School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India a r t i c l e i n f o Article history: Received 7 October 2016 Revised 26 January 2017 Accepted 26 January 2017 Keywords: Evolutionary multi-objective optimization Search space Search space convergence Search space diversity Evolutionary algorithms a b s t r a c t Evolutionary multi-objective optimization (EMO) algorithms are actively used for answer- ing optimization problems with multiple contradictory objectives and scheming inter- pretable and precise real-time applications. A majority of existing EMO algorithms per- forms better on two or three objectives non-dominated problems; however, they meet complications in managing and maintaining a set of optimal solutions to multi-objective optimization problems. This paper proposes a search space-based multi-objective evolu- tionary algorithm (SSMOEA) for multi-objective optimization problems. To accomplish the potential of the search space-based method for solving multi-objective optimization prob- lems and to reinforce the selection procedure toward the ideal direction while sustain- ing an extensive and uniform distribution of solutions is our key objective. To the best of our knowledge, this paper is the first attempt to propose a search space-based multi- objective evolutionary algorithm for multi-objective optimization. The experimental setup used showed that the proposed algorithm is good and competitive in comparison to the existing EMO algorithms from the viewpoint of finding a scattered and estimated solu- tion set in multi-objective optimization problems. SSMOEA can achieve a good trade-off between search space convergence and search space diversity in the appropriate experi- mental setup. © 2017 Elsevier Ltd. All rights reserved. 1. Introduction In several practical applications, evolutionary algorithms are treated as one of the efficient way for discovering multi- objective optimization problems (MOP). In MOP, there are more than three objectives to be optimized for the same instance of time. Generally, there is no distinct optimal solution for MOP due to the contradictory nature of objectives; however, there is a set of alternate solutions, and such alternative solutions are known as the Pareto set for multi-objective opti- mization problems. Evolutionary algorithms (EAs) have been accepted to be appropriately well-matched for MOPs because of their population-based property of attaining an approximation of the Pareto set in a particular run [1]. Over the past few years, several advanced evolutionary multi-objective optimization (EMO) algorithms have been suggested by researchers and practitioners. All of these algorithms converge at two objectives – reducing the distance of possible results to the optimum front, which is treated as convergence, and making the best use of the broadcasting of possible solutions over the optimal front, which is termed as diversity.  Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. R. Varatharajan. ∗ Corresponding author. E-mail addresses: darshan.medhane@gmail.com (D.V. Medhane), arunkumarsangaiah@gmail.com, sarunkumar@vit.ac.in (A.K. Sangaiah). http://dx.doi.org/10.1016/j.compeleceng.2017.01.025 0045-7906/© 2017 Elsevier Ltd. All rights reserved.