Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/24855
Title: An Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization
Authors: Xue, Y
Li, M
Liu, X
Keywords: evolutionary algorithms;many-objective optimization;effectiveness;efficiency
Issue Date: 31-May-2022
Citation: Xue, Y., Li, M., Liu, X. (2022) 'An Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization', Arxiv, 0, pp. 1 - 22. doi:10.48550/arXiv.2205.15884.
Abstract: In evolutionary multi-objective 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. 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.
Description: Preprint - unpublished
URI: http://bura.brunel.ac.uk/handle/2438/24855
ISSN: 2331-8422
Other Identifiers: http://arxiv.org/abs/2205.15884v1
http://arxiv.org/abs/2205.15884v1
Appears in Collections:Dept of Computer Science Research Papers

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