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A Novel Many-Objective Optimization Algorithm Based on the Hybrid Angle-Encouragement Decomposition

  • Yuchao Su
  • Jia Wang
  • Lijia Ma
  • Xiaozhou Wang
  • Qiuzhen Lin
  • Jianyong Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

For many-objective optimization problems (MaOPs), the problem of balancing the convergence and the diversity during the search process is often encountered but very challenging due to its vast range of searching objective space. To solve the above problem, we propose a novel many-objective evolutionary algorithm based on the hybrid angle-encouragement decomposition (MOEA/AD-EBI). The proposed MOEA/AD-EBI combines two types of decomposition approaches, i.e., the angle-based decomposition and the encouragement-based boundary intersection decomposition. By coordinating the above two decomposition approaches, MOEA/AD-EBI is expected to effectively achieve a good balance between the convergence and the diversity when solving various kinds of MaOPs. Extensive experiments on some well-known benchmark problems validate the superiority of MOEA/AD-EBI over some state-of-the-art many-objective evolutionary algorithms.

Keywords

MaOPs MOEA Hybrid angle-encouragement decomposition 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yuchao Su
    • 1
  • Jia Wang
    • 1
  • Lijia Ma
    • 1
  • Xiaozhou Wang
    • 1
  • Qiuzhen Lin
    • 1
  • Jianyong Chen
    • 1
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China

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