Linear Regression-based Autonomous Intelligent Optimization for Constrained Multi-objective Problems

It is very challenging to autonomously generate algorithms suitable for constrained multi-objective optimization problems due to the diverse performance of existing algorithms. In this paper, we propose a linear regression-based autonomous intelligent optimization method. It first extracts typical features of a constrained multi-objective optimization problem by focused sampling to form a feature vector. Then, a linear regression model is designed to learn the relationship between optimization problems and intelligent optimization algorithms. Finally, the trained model autonomously generates a suitable intelligent optimization algorithm by inputting the feature vector. The proposed method is applied to six constrained multi-objective benchmark test sets with various characteristics and compared with seven popular optimization algorithms. The experimental results verify the effectiveness of the proposed method. In addition, the proposed method is used to solve the operation optimization problems of an integrated coal mine energy system, and the experimental results show its practicability.