An effective and efficient evolutionary algorithm for many-objective optimization

Yani Xue*, Miqing Li, Xiaohui Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also diversifying them over the front. This is not an easy job, particularly for optimization problems with more than three objectives, dubbed many-objective optimization problems. In such problems, classic Pareto-based algorithms fail to provide sufficient selection pressure towards the Pareto front, whilst recently developed algorithms, such as decomposition-based ones, may struggle to maintain a set of well-distributed solutions on certain problems (e.g., those with irregular Pareto fronts). Another issue in some many-objective optimizers is rapidly increasing computational requirement with the number of objectives, such as hypervolume-based algorithms and shift-based density estimation (SDE) methods. In this paper, we aim to address this problem and develop an effective and efficient evolutionary algorithm (E3A) that can handle various many-objective problems. In E3A, inspired by SDE, a novel population maintenance method is proposed to select high-quality solutions in the environmental selection procedure. We conduct extensive experiments and show that E3A performs better than 11 state-of-the-art many-objective evolutionary algorithms in quickly finding a set of well-converged and well-diversified solutions.
Original languageEnglish
Pages (from-to)211-233
Number of pages23
JournalInformation Sciences
Volume617
Early online date20 Oct 2022
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Evolutionary algorithms
  • Many-objective optimization
  • Effectiveness
  • Efficiency

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