大规模进化多目标优化的学习引导交叉采样

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Swarm and Evolutionary Computation Pub Date : 2024-11-05 DOI:10.1016/j.swevo.2024.101763
Haofan Wang , Li Chen , Xingxing Hao , Rong Qu , Wei Zhou , Dekui Wang , Wei Liu
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引用次数: 0

摘要

在处理大规模多目标问题(LSMOPs)时,传统的子代生成器几乎是无方向地探索搜索空间,可能会浪费计算预算,从而影响许多现有算法的效率。为了解决这个问题,本文提出了一种新颖的两级大规模多目标进化算法 LMOEA-LGCS,它在第一级结合了神经网络(NN)学习引导的交叉采样用于子代生成,在第二级结合了分层竞争群优化器。具体来说,在第一层中,两个神经网络经过在线训练,分别根据帕累托集学习有前途的纵向和横向搜索方向,然后在所学方向上抽取一批候选解。学习两个明确搜索方向的好处在于,所使用的 NNs 可以专注于不同甚至相互冲突的目标,即群体的收敛性和多样性,从而在两者之间实现良好的权衡。这样,算法就能自适应地探索更有前景的搜索方向,这些方向有可能促进群体的收敛,同时保持良好的多样性。在第二层中,分层竞争性蜂群优化器被用于在整个搜索空间中对第一层中生成的解决方案进行更深入的优化,以进一步提高其多样性。在 2-12 个目标和 500-8000 个决策变量的三个 LSMOP 基准(即 LSMOP、UF 和 IMF)以及实际问题 TREE 上与六种最先进的算法进行比较,证明了所提算法的优势。
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Learning-guided cross-sampling for large-scale evolutionary multi-objective optimization
When tackling large-scale multi-objective problems (LSMOPs), the computational budget could be wasted by traditional offspring generators that explore the search space in a nearly directionless manner, impairing the efficiency of many existing algorithms. To address this issue, this paper proposes a novel two-level large-scale multi-objective evolutionary algorithm called LMOEA-LGCS that incorporates neural network (NN) learning-guided cross-sampling for offspring generation in the first level and a layered competitive swarm optimizer in the second level. Specifically, in the first level, two NNs are trained online to learn promising vertical and horizontal search directions, respectively, against the Pareto Set, and then a batch of candidate solutions are sampled on the learned directions. The merit of learning two explicit search directions is to devote the employed NNs to concentrating on separate or even conflicting targets, i.e., the convergence and diversity of the population, thus achieving a good trade-off between them. In this way, the algorithm can thus explore adaptively towards more promising search directions that have the potential to facilitate the convergence of the population while maintaining a good diversity. In the second level, the layered competitive swarm optimizer is employed to perform a deeper optimization of the solutions generated in the first level across the entire search space to increase their diversity further. Comparisons with six state-of-the-art algorithms on three LSMOP benchmarks, i.e., the LSMOP, UF, and IMF, with 2-12 objectives and 500-8000 decision variables, and the real-world problem TREE demonstrate the advantages of the proposed algorithm.
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来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
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