Tianwei Zhou, Wenwen Zhang, Ben Niu, Pengcheng He, Guanghui Yue
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Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning
To address the challenge of parameter adjustment in complex environments, this paper introduces a transfer learning-based parameter control framework via deep reinforcement learning for multiobjective evolutionary algorithms (MOEAs). To avoid the requirement for accurate Pareto front information, this framework is proposed with comprehensive global-state information, including basic problem features, the relative position of individuals, the distribution of fitness value, and the grid-IGD. Building on this framework, four reinforced multiobjective evolutionary algorithms (r-MOEAs) are proposed and tested on four DTLZ benchmarks and eight WFG benchmarks. The results of the comparative analyses reveal that compared with the original MOEAs, the four r-MOEAs exhibit faster convergence and stronger robustness. It is also confirmed that our proposed parameter control framework has the capability to learn knowledge from different experiences and improve the performance of MOEAs.
期刊介绍:
The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.