A Non-dominated Sorting based Evolutionary Algorithm for Many-objective Optimization Problems

Document Type : Article

Authors

Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur Dist., AP, India

Abstract

The optimization problems with more than three objectives are many-objective optimization problems exist in various scientific and engineering domains. The existing multi-objective evolutionary algorithmic approaches primarily developed to address problems up to three objectives. Such multi-objective evolutionary algorithms do not found effective to address the many-objective optimization problems. The limitations of existing multi-objective evolutionary algorithms initiated the need to develop a specific algorithm which efficiently solves the many-objective optimization problems. The proposed work presents the design of the MaOHDE to address MaOPs. Initially, NS-MODE & NS-MOPSO algorithms developed by incorporating the non-dominated sorting approach from NSGA-II, the ranking approach, weight vector, and reference points. The widely used Tchebycheff – a decomposition-based approach applied to decompose the MaOPs. The MaOHDE algorithm developed by hybridizing the NS-MODE with NS-MOPSO. The presented approach’s strength is revealed using 20 instances of DTLZ functions. The effectiveness and efficiency are verified by comparing with MaOJaya, RD-EMO, NSGA-III, MOEA/D, MOEA/DD, RVEA, and MOEA/D-M2M algorithms. From the results, it is observed that the hybridization of NS-MODE and NS-MOPSO as MaOHDE responds better than its competitors for most of the test instances or it is competitive. The convergence rate is also good as compared with other state-of-art algorithms.

Keywords

References

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Scientia Iranica
Volume 28, Issue 6 - Serial Number 6
Transactions on Computer Science & Engineering and Electrical Engineering (D)
November and December 2021
Pages 3293-3314

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History

  • Receive Date: 25 February 2019
  • Revise Date: 06 December 2020
  • Accept Date: 17 May 2021

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