A fitness approximation assisted competitive swarm optimizer for large scale expensive optimization problems

  • Chaoli Sun
  • Jinliang Ding
  • Jianchao Zeng
  • Yaochu Jin
Regular Research Paper

Abstract

Surrogate assisted meta-heuristic algorithms have received increasing attention over the past years due to the fact that many real-world optimization problems are computationally expensive. However, most existing surrogate assisted meta-heuristic algorithms are designed for small or medium scale problems. In this paper, a fitness approximation assisted competitive swarm optimizer is proposed for optimization of large scale expensive problems. Different from most surrogate assisted evolutionary algorithms that use a computational model for approximating the fitness, we estimate the fitness based on the positional relationship between individuals in the competitive swarm optimizer. Empirical study on seven widely used benchmark problems with 100 and 500 decision variables show that the proposed fitness approximation assisted competitive swarm optimizer is able to achieve competitive performance on a limited computational budget.

Keywords

Surrogate assisted meta-heuristic algorithms Large scale expensive optimization problems Competitive swarm optimizer Fitness approximation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Chaoli Sun
    • 1
    • 2
    • 3
  • Jinliang Ding
    • 3
  • Jianchao Zeng
    • 4
  • Yaochu Jin
    • 2
    • 5
  1. 1.Department of Computer Science and TechnologyTaiyuan University of Science and TechnologyTaiyuanPeople’s Republic of China
  2. 2.Department of Computer ScienceUniversity of SurreyGuildfordUK
  3. 3.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangPeople’s Republic of China
  4. 4.School of Computer Science and Control EngineeringNorth University of ChinaTaiyuanPeople’s Republic of China
  5. 5.Department of Computer Science and TechnologyTaiyuan University of Science and TechnologyTaiyuanPeople’s Republic of China

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