针对大规模稀疏多目标优化问题的带强鲁棒稀疏算子的增强型竞争性蜂群优化器

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-22 DOI:10.1016/j.ins.2024.121569
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引用次数: 0

摘要

在现实世界中,大规模稀疏多目标问题的决策变量是高维的,大多数帕累托最优解都是稀疏的。算法的平衡性难以控制,因此在一般情况下处理这类问题具有挑战性。因此,本文提出了带强鲁棒稀疏算子的增强型竞争性蜂群优化算法(SR-ECSO)。首先,在高维决策变量中使用了强鲁棒性稀疏函数,它能加速群体中的粒子在决策空间中获得更好的稀疏性。其次,通过引入自适应随机扰动算子,保持了稀疏解的多样性,并增强了算法的收敛平衡。最后,利用蜂群优化器更新粒子状态,以提高群体竞争力。为了验证所提出的算法,我们测试了八个大规模稀疏基准问题,并以 100、500 和 1000 为例,将决策变量分为三组。实验结果表明,该算法有望解决大规模稀疏优化问题。
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An enhanced competitive swarm optimizer with strongly robust sparse operator for large-scale sparse multi-objective optimization problem
In the real world, the decision variables of large-scale sparse multi-objective problems are high-dimensional, and most Pareto optimal solutions are sparse. The balance of the algorithms is difficult to control, so it is challenging to deal with such problems in general. Therefore, An Enhanced Competitive Swarm Optimizer with Strongly Robust Sparse Operator (SR-ECSO) algorithm is proposed. Firstly, the strongly robust sparse functions which accelerate particles in the population better sparsity in decision space, are used in high-dimensional decision variables. Secondly, the diversity of sparse solutions is maintained, and the convergence balance of the algorithm is enhanced by the introduction of an adaptive random perturbation operator. Finally, the state of the particles is updated using a swarm optimizer to improve population competitiveness. To verify the proposed algorithm, we tested eight large-scale sparse benchmark problems, and the decision variables were set in three groups with 100, 500, and 1000 as examples. Experimental results show that the algorithm is promising for solving large-scale sparse optimization problems.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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