An adaptive interval many-objective evolutionary algorithm with information entropy dominance

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2024-10-03 DOI:10.1016/j.swevo.2024.101749

Zhihua Cui , Conghong Qu , Zhixia Zhang , Yaqing Jin , Jianghui Cai , Wensheng Zhang , Jinjun Chen

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引用次数: 0

Abstract

Interval many-objective optimization problems (IMaOPs) involve more than three conflicting objectives with interval parameters. Various real-world applications under uncertainty can be modeled as IMaOPs to solve, so effectively handling IMaOPs is crucial for solving practical problems. This paper proposes an adaptive interval many-objective evolutionary algorithm with information entropy dominance (IMEA-IED) to tackle IMaOPs. Firstly, an interval dominance method based on information entropy is proposed to adaptively compare intervals. This method constructs convergence entropy and uncertainty entropy related to interval features and innovatively introduces the idea of using global information to regulate the direction of local interval comparison. Corresponding interval confidence levels are designed for different directions. Additionally, a novel niche strategy is designed through interval population partitioning. This strategy introduces a crowding distance increment for improved subpopulation comparison and employs an updated reference vector method to adjust the search regions for empty subpopulations. The IMEA-IED is compared with seven interval optimization algorithms on 60 interval test problems and a practical application. Empirical results affirm the superior performance of our proposed algorithm in tackling IMaOPs.

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具有信息熵优势的自适应区间多目标进化算法

区间多目标优化问题（IMaOPs）涉及三个以上带有区间参数的冲突目标。不确定条件下的各种实际应用都可以建模为 IMaOPs 来解决，因此有效处理 IMaOPs 对解决实际问题至关重要。本文提出了一种具有信息熵优势的自适应区间多目标进化算法（IMEA-IED）来解决 IMaOPs。首先，本文提出了一种基于信息熵的区间优势方法，用于自适应地比较区间。该方法构建了与区间特征相关的收敛熵和不确定性熵，并创新性地引入了利用全局信息调节局部区间比较方向的思想。针对不同的方向，设计了相应的区间置信度。此外，还通过区间种群划分设计了一种新颖的利基策略。该策略引入了拥挤距离增量以改进子群比较，并采用更新的参考向量方法来调整空子群的搜索区域。在 60 个区间测试问题和一个实际应用中，IMEA-IDD 与七种区间优化算法进行了比较。实证结果表明，我们提出的算法在处理 IMaOPs 方面表现出色。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.