An adaptive many-objective evolutionary algorithm based on decomposition with two archives and an entropy trigger

Li Cao, Maocai Wang, Massimiliano Vasile, Guangming Dai, Huanqin Wu

Research output: Contribution to journalArticlepeer-review

Abstract

This article proposes two novel mechanisms to improve the performance of many-objective evolutionary algorithms based on Chebyshev scalarization. One mechanism improves the efficiency and effectiveness of the adaptation of the descent directions in criteria space, while the other ensures that extreme solutions are preserved. Weight adaptation via WS-transformation has shown promising results, but its performance is dependent on the choice of the start of the adaptation process. In order to overcome this limitation, in this article an efficient entropy-based trigger is proposed with fast calculation of the entropy that scales favourably with the number of dimensions. The novel entropy-based method is complemented by a dual-archiving mechanism that preserves extreme solutions. The dual-archiving strategy mitigates the possibility to discard those critical individuals whose loss affects the whole evolutionary process. The new algorithm proposed in this article (called aMOEA/D-2A-ET) is compared against a set of state-of-the-art MOEAs and shown to have competitive performance.
Original languageEnglish
Number of pages41
JournalEngineering Optimization
Early online date11 Dec 2023
DOIs
Publication statusE-pub ahead of print - 11 Dec 2023

Keywords

  • MOEA/D
  • adaptive weights
  • entropy calculation
  • entropy trigger
  • two archives

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