An active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy

Xiaofang Guo; Yuping Wang; Haonan Zhang; Xiaofang Guo; Yuping Wang; Haonan Zhang

doi:10.3934/mbe.2023878

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 11: 19839-19857. doi: 10.3934/mbe.2023878

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An active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy

School of Sciences, Xi'an Technological University, Xi'an 710000, China

Academic Editor: Vladimir Mityushev

Received: 24 June 2023 Revised: 19 October 2023 Accepted: 24 October 2023 Published: 31 October 2023

The inverse model based multi-objective evolutionary algorithm (IM-MOEA) generates offspring by establishing probabilistic models and sampling by the model, which is a new computing schema to replace crossover in MOEAs. In this paper, an active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy (ALGM-MOEA) is proposed. To properly cope with multi-objective problems with different shapes of Pareto front (PF), a novel population guided weight vector evolution strategy is proposed to dynamically adjust search directions according to the distribution of generated PF. Moreover, in order to enhance the search efficiency and prediction accuracy, an active learning based training sample selection method is designed to build Gaussian process based inverse models, which chooses individuals with the maximum amount of information to effectively enhance the prediction accuracy of the inverse model. The experimental results demonstrate the competitiveness of the proposed ALGM-MOEA on benchmark problems with various shapes of Pareto front.
- active learning,
- multi-objective evolutionary algorithm,
- weight vector adjustment,
- Gaussian regression model,
- Pareto front
Citation: Xiaofang Guo, Yuping Wang, Haonan Zhang. An active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19839-19857. doi: 10.3934/mbe.2023878

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Abstract

The inverse model based multi-objective evolutionary algorithm (IM-MOEA) generates offspring by establishing probabilistic models and sampling by the model, which is a new computing schema to replace crossover in MOEAs. In this paper, an active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy (ALGM-MOEA) is proposed. To properly cope with multi-objective problems with different shapes of Pareto front (PF), a novel population guided weight vector evolution strategy is proposed to dynamically adjust search directions according to the distribution of generated PF. Moreover, in order to enhance the search efficiency and prediction accuracy, an active learning based training sample selection method is designed to build Gaussian process based inverse models, which chooses individuals with the maximum amount of information to effectively enhance the prediction accuracy of the inverse model. The experimental results demonstrate the competitiveness of the proposed ALGM-MOEA on benchmark problems with various shapes of Pareto front.

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