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Learning-Driven Algorithm With Dual Evolution Patterns for Solving Large-Scale Multiobjective Optimization Problems
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DOI：10.1109/ACCESS.2025.3541271.png)
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摘要
En 中文
Recently, a series of evolutionary algorithms have been proposed to enhance the search efficiency when handling large-scale multiobjective optimization problems (LSMOPs). Among them, learning-driven evolutionary algorithms have shown to be promising by learning the related evolutionary pattern with a specific neural network. However, the learned evolutionary patterns primarily focus on convergence while ignoring diversity, which may potentially cause an inadequate search. In this paper, we propose a learning-driven algorithm with dual evolution patterns (DEPLA) for solving LSMOPs. The main idea is to learn two evolution patterns in the evolutionary process, one focusing on convergence and the other on diversity, which would be helpful to produce promising solutions efficiently. To be specific, DEPLA first divides the population into two parts to be updated with different strategies. A half of the solutions with good convergence and distribution will be updated with the learned evolution pattern focusing on convergence, while the rest of the solutions are repositioned by another learned evolution pattern towards diversity. Through learning the dual evolution patterns in both convergence and diversity, DEPLA is capable of efficiently tackling LSMOPs. Abundant experiments are conducted on the well-known LSMOPs test suites with up to 10,000 decision variables. The experimental results demonstrate that our DEPLA outperforms six state-of-the-art large-scale multiobjective evolutionary algorithms in handling LSMOPs.
Keyword:
Convergence
Search problems
Training
Neural networks
Vectors
Optimization
Marine vehicles
Indexes
Focusing
Evolutionary computation
Large-scale multiobjective optimization
evolutionary pattern learning
learning-aided evolution
differential evolution
neural network
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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