Learning-Aided Evolution for Optimization

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...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 27; číslo 6; s. 1794 - 1808
Hlavní autoři: Zhan, Zhi-Hui, Li, Jian-Yu, Kwong, Sam, Zhang, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3232776