一种新型混合多目标优化算法及其在电磁器件设计中的应用

IF 2.1 3区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Magnetics Pub Date : 2024-12-19 DOI:10.1109/TMAG.2024.3519202

Yilun Li;Zhengwei Xie;Shiyou Yang;Zhuoxiang Ren

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引用次数: 0

摘要

本文将改进的麻雀搜索算法（SSA）与改进的非支配排序遗传算法（NSGA-II）相结合，提出了一种新的混合多目标优化算法（MOO）。引入蛾焰优化（MFO）算法的种群更新机制，采用自适应突变对原有的SSA进行改进；同时，利用拉丁超立方体采样和交叉变异算子的动态选择机制增强NSGA-II。利用标准测试函数验证了所提混合算法的性能，并将其应用于TEAM22基准问题的多目标优化设计和电磁作动器原型拓扑优化问题。数值结果表明了该算法的有效性和优越性。

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A Novel Hybrid Multi-Objective Optimization Algorithm and Its Application to Designs of Electromagnetic Devices

In this article, a novel hybrid multi-objective optimization (MOO) algorithm is proposed by combining an improved sparrow search algorithm (SSA) with an improved non-dominated sorting genetic algorithm (NSGA-II). The original SSA is improved by the introduction of population updating mechanism of moth-flame optimization (MFO) algorithm and by adopting adaptive mutation; meanwhile, NSGA-II is enhanced by using Latin hypercube sampling and dynamical selection mechanism of crossover and mutation operators. The performance of the proposed hybrid algorithm is verified using standard test functions and it is applied to the multi-objective optimal designs of TEAM22 benchmark problem and topology optimization problem of an electromagnetic actuator prototype. Numerical results demonstrate the effectiveness and superiority of the proposed algorithm.

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来源期刊

IEEE Transactions on Magnetics 工程技术-工程：电子与电气

CiteScore

4.00

自引率

14.30%

发文量

565

审稿时长

4.1 months

期刊介绍： Science and technology related to the basic physics and engineering of magnetism, magnetic materials, applied magnetics, magnetic devices, and magnetic data storage. The IEEE Transactions on Magnetics publishes scholarly articles of archival value as well as tutorial expositions and critical reviews of classical subjects and topics of current interest.