Solving many-objective optimization problems by a Pareto-based evolutionary algorithm with preprocessing and a penalty mechanism

Yuan Liu, N. Zhu, Miqing Li

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It is known that the Pareto-based approach is not well suited for optimization problems with a large number of objectives, even though it is a class of mainstream methods in multiobjective optimization. Typically, a Pareto-based algorithm comprises two parts: 1) a Pareto dominance-based criterion and 2) a diversity estimator. The former guides the selection toward the optimal front, while the latter promotes the diversity of the population. However, the Pareto dominance-based criterion becomes ineffective in solving optimization problems with many objectives (e.g., more than 3) and, thus, the diversity estimator will determine the performance of the algorithm. Unfortunately, the diversity estimator usually has a strong bias toward dominance resistance solutions (DRSs), thereby failing to push the population forward. DRSs are solutions that are far away from the Pareto-optimal front but cannot be easily dominated. In this article, we propose a new Pareto-based algorithm to resolve the above issue. First, to eliminate the DRSs, we design an interquartile range method to preprocess the solution set. Second, to balance convergence and diversity, we present a penalty mechanism of alternating operations between selection and penalty. The proposed algorithm is compared with five state-of-the-art algorithms on a number of well-known benchmarks with 3–15 objectives. The experimental results show that the proposed algorithm can perform well on most of the test functions and generally outperforms its competitors.
Original languageEnglish
Pages (from-to)5585-5594
Number of pages10
JournalIEEE Transactions on Cybernetics
Issue number11
Early online date20 May 2021
Publication statusPublished - Nov 2021

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Dominance resistance solutions (DRSs)
  • evolutionary algorithm
  • many-objective optimization

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


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