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DOI

  • Pezhman Gholamnezhad
    shahid sattari University of Aeronautical Sciences and Technology
  • Ali Broumandnia
    Islamic Azad University
  • Vahid Seydi
    Islamic Azad University
The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).
Original languageEnglish
Pages (from-to)709-874
JournalElectronics and Telecommunications Research Institute
Volume44
Issue number5
Early online date24 Oct 2022
DOIs
Publication statusPublished - 31 Oct 2022
Externally publishedYes
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