Learning-Aided Evolution for Optimization

• Published in: IEEE transactions on evolutionary computation Vol. 27; no. 6; pp. 1794 - 1808
• Main Authors: Zhan, Zhi-Hui, Li, Jian-Yu, Kwong, Sam, Zhang, Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Artificial neural network (ANN); Artificial neural networks; Benchmark testing; differential evolution (DE); Evolution (biology); Evolutionary computation; evolutionary computation (EC); Global optimization; Human performance; Learning systems; learning-aided evolution; many-objective optimization; multiobjective optimization; Multiple objective analysis; Optimization; particle swarm optimization (PSO); Problem solving; single-objective optimization
• ISSN: 1089-778X; 1941-0026; 1941-0026
• DOI: 10.1109/TEVC.2022.3232776

Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this article proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on EC competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.