ETRI Journal

  • 제44권5호
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  • Pages.805-815
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  • 2022
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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A random forest-regression-based inverse-modeling evolutionary algorithm using uniform reference points

  • Gholamnezhad, Pezhman (Faculty of Computer Engineering and Information Technology, Shahid Sattari University of Aeronautical Sciences and Technology) ;
  • Broumandnia, Ali (Department of Computer, Soth Tehran Branch, Islamic Azad University) ;
  • Seydi, Vahid (Department of Computer, Soth Tehran Branch, Islamic Azad University)
  • 투고 : 2021.12.16
  • 심사 : 2022.06.23
  • 발행 : 2022.10.10
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초록

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).

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참고문헌

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