Abstract
Currently, energy saving is increasingly important. During the production procedure, energy saving can be achieved if the operational method and machine infrastructure are improved, but it also increases the complexity of flow-shop scheduling. Actually, as one of the data mining technologies, Grey Wolf Optimization Algorithm is widely applied to various mathematical problems in engineering. However, due to the immaturity of this algorithm, it still has some defects. Therefore, we propose an improved multiobjective model based on Grey Wolf Optimization Algorithm related to Kalman filter and reinforcement learning operator, where Kalman filter is introduced to make the solution set closer to the Pareto optimal front end. By means of reinforcement learning operator, the convergence speed and solving ability of the algorithm can be improved. After testing six benchmark functions, the results show that it is better than that of the original algorithm and other comparison algorithms in terms of search accuracy and solution set diversity. The improved multiobjective model based on Grey Wolf Optimization Algorithm proposed in this paper is conducive to solving energy saving problems in flow-shop scheduling problem, and it is of great practical value in engineering and management.
Highlights
Many mathematical problems in scientific research and practical engineering essentially belong to multiobjective optimization problem. e analysis of constrained multiobjective optimization algorithm has become a research hotspot in recent years.Different theories exist in the literature regarding optimization algorithm such as the Improved Multiobjective Grey Wolf Optimizer (IMOGWO) that hybridize with the fast nondominated sorting strategy [1]
Erefore, we propose an improved Multiobjective Grey Wolf Optimizer related to Kalman filtering and reinforcement learning (MKGWO) in this paper. e main innovation of the algorithm is that Kalman filter facilitates the convergence from solution set to Pareto optimal front end introduced into the static multiobjective algorithm
Traditional Multiobjective Grey Wolf Optimizer is a grey wolf group predation was inspired by multiobjective optimization algorithm, using a fixed external file to store nondominated solution, simple multiobjective grey wolves optimizer in solving static multiobjective problem, because without a good promotion strategy, lead to being not close to the Pareto-optimal front end, and the diversity of solution set is not high [39]
Summary
Many mathematical problems in scientific research and practical engineering essentially belong to multiobjective optimization problem. e analysis of constrained multiobjective optimization algorithm has become a research hotspot in recent years. Some scholars proposed a differential evolution algorithm based on two-population search mechanism, which randomly deletes one of the two individuals with the smallest Euclidean distance [3] In this way, the boundary solution may be lost, and the diversity of solution set may be affected. Some scholars proposed a constrained multiobjective optimization algorithm based on adaptive e truncation strategy, which can improve the diversity of solution sets and give consideration to the convergence [8]. E main innovation of the algorithm is that Kalman filter facilitates the convergence from solution set to Pareto optimal front end introduced into the static multiobjective algorithm It combines the characteristics of Kalman filter with the robustness, reliability, and high efficiency of the reinforcement learning system when solving problems [13]. Optimization Algorithm to give consideration to energysaving problems in engineering. e results show that the algorithm can solve the Pareto front end problem in flowshop scheduling successfully, and it is of great practical value in engineering and management
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