A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization

Shouyong Jiang, Shengxiang Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

115 Citations (Scopus)

Abstract

This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most existing approaches for dynamic multiobjective optimization, the proposed algorithm detects environmental changes and responds to them in a steady-state manner. If a change is detected, it reuses a portion of outdated solutions with good distribution and relocates a number of solutions close to the new Pareto front based on the information collected from previous environments and the new environment. This way, the algorithm can quickly adapt to changing environments and thus is expected to provide a good tracking ability. The proposed algorithm is tested on a number of bi-and three-objective benchmark problems with different dynamic characteristics and difficulties. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization in comparison with state-of-the-art methods.

Original languageEnglish
Article number7527677
Pages (from-to)65-82
Number of pages18
JournalIEEE Transactions on Evolutionary Computation
Volume21
Issue number1
Early online date1 Aug 2016
DOIs
Publication statusPublished - Feb 2017

Keywords

  • Change detection
  • change response
  • dynamic multiobjective optimization
  • Steady-state and generational evolutionary algorithm

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