基于代际差向量的大规模多目标优化三熵结构优化器

IF 8.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuhan Xu, Yu Zhang, Wang Hu
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引用次数: 0

摘要

近年来,工程和科学领域中大规模多目标优化问题日益复杂,对算法的计算效率提出了更高的要求。本文提出了一种基于代际差矢量的三熵结构优化器(GDVTSO)算法,该算法是为高效执行大规模多目标优化任务而设计的。其核心思想是通过计算决策空间内的局部信息熵,分析迭代前后聚类的变化,确定更有效的搜索方向。为此,设计了三熵结构优化器(TSO),以更有效地利用信息熵进行向量更新。在此基础上,引入了代际差异矢量(GDV)机制,对各簇内矢量的搜索方向进行了指导。GDVTSO算法具有良好的兼容性和广泛的应用潜力。本研究将GDVTSO与两种已建立的大规模优化技术相结合,并通过这种方法融合提出了一种混合算法GDVTSF。此外,GDVTSF的性能对优化问题的维数敏感度较低。在标准大规模多目标优化基准上的实验结果表明,GDVTSF优于当前最先进的优化算法。此外,即使应用于具有多达10,000个决策变量的高维问题,它也能显著保持其优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Generational Difference Vector based Tri-Entropy Structure Optimizer for large-scale multiobjective optimization
The increasing complexity of large-scale multiobjective optimization problems in engineering and scientific fields in recent years has imposed higher demands on the computational efficiency of algorithms. This paper introduces a novel algorithm named the Generational Difference Vector based Tri-Entropy Structure Optimizer (GDVTSO), which is designed for the efficient execution of large-scale multi-objective optimization tasks. The core idea is to determine a more effective search direction by calculating the local information entropy within the decision space and analyzing the changes in clusters before and after iterations. To this end, the Tri-Entropy Structure Optimizer (TSO) has been designed to more efficiently utilize information entropy for vector updates. Furthermore, the Generational Difference Vector (GDV) mechanism is introduced to provide guidance on search direction for vectors within each cluster. The GDVTSO algorithm demonstrates exceptional compatibility and extensive application potential. In this study, GDVTSO is integrated with two established large-scale optimization techniques, and a hybrid algorithm designated as GDVTSF is proposed through this methodological fusion. Moreover, GDVTSF’s performance exhibits a lower sensitivity to the dimensionality of optimization problems. Experimental results on standard large-scale multiobjective optimization benchmarks demonstrate that GDVTSF outperforms the current state-of-the-art optimization algorithms. Furthermore, it remarkably maintains its superior performance even when applied to high-dimensional problems with up to 10,000 decision variables.
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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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