Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.

Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and $\epsilon$ feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for $\epsilon$ constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

Various constrained multiobjective evolutionary optimization algorithms (CMOEAs) have been proposed for constrained multiobjective optimization problems (CMOPs). However, their reproduction operators often fail to effectively utilize constraint information, leading to inefficient exploration of feasible regions and premature convergence near boundaries of feasible regions. In light of this, we propose a novel population image convolution (PIC) method to improve information sharing among population individuals. Three column-oriented convolutional kernels are designed to participate in population reproduction, which can quickly locate the feasible region and thoroughly searching within it. Furthermore, our method features adaptive updating of the scope and control parameters for applying convolutional kernels to multiple subpopulations. To validate the effectiveness and superiority, we conducted comparisons against 11 state-of-the-art CMOEAs across five test suites. The results demonstrate the robust performance of our approach in diverse optimization scenarios. In addition, we showcase the practical applicability of our method to the dispatch optimization of integrated coal mine energy systems.

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.