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A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy

Liu, , Gong, D, Sun, J and Jin, Yaochu (2017) A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy IEEE Transactions on Cybernetics, 47 (9). pp. 2689-2702.

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Most existing multi-objective evolutionary algorithms experience difficulties in solving many-objective optimization problems due to their incapability to balance convergence and diversity in the high-dimensional objective space. In this paper, we propose a novel many-objective evolutionary algorithm using a one-by-one selection strategy. The main idea is that in the environmental selection, offspring individuals are selected one by one based on a computationally efficient convergence indicator to increase the selection pressure towards the Pareto optimal front. In the one-by-one selection, once an individual is selected, its neighbors are de-emphasized using a niche technique to guarantee the diversity of the population, in which the similarity between individuals is evaluated by means of a distribution indicator. In addition, different methods for calculating the convergence indicator are examined and an angle-based similarity measure is adopted for effective evaluations of the distribution of solutions in the high-dimensional objective space. Moreover, corner solutions are utilized to enhance the spread of the solutions and to deal with scaled optimization problems. The proposed algorithm is empirically compared with eight state-of-the-art many-objective evolutionary algorithms on 90 instances of 16 benchmark problems. The comparative results demonstrate that the overall performance of the proposed algorithm is superior to the compared algorithms on the optimization problems studied in this work.

Item Type: Article
Subjects : Computing Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Gong, D
Sun, J
Date : 9 January 2017
DOI : 10.1109/TCYB.2016.2638902
Copyright Disclaimer : (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords : Many-objective optimization, evolutionary multi-objective optimization, performance indicator, cosine similarity, convergence, diversity
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 27 Jan 2017 16:26
Last Modified : 16 Jan 2019 17:11

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