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

被引：1

作者：

Li, Yilun ^{[1
]}

Xie, Zhengwei ^{[1
]}

Yang, Shiyou ^{[2
]}

Ren, Zhuoxiang ^{[3
]}

机构：

[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China

[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China

[3] Sorbonne Univ, Grp Elect & Elect Engn Paris, Paris PARIS, France

来源：

IEEE TRANSACTIONS ON MAGNETICS | 2025年 / 61卷 / 02期

关键词：

Optimization; Heuristic algorithms; Polynomials; Diversity reception; Sorting; Search problems; Electromagnetic devices; Hypercubes; Flowcharts; Fires; Evolutionary algorithms; multi-objective optimization (MOO); TEAM22; benchmark; topology optimization;

D O I：

10.1109/TMAG.2024.3519202

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：4

共 9 条

[1] SMES optimization benchmark extended:: Introducing Pareto optimal solutions into TEAM22 [J].

Alotto, P. ;

Baumgartner, U. ;

Freschi, F. ;

Jaindl, M. ;

Koestinger, A. ;

Magele, Ch. ;

Renhart, W. ;

Repett, A. .

IEEE TRANSACTIONS ON MAGNETICS, 2008, 44 (06) :1066-1069

[2] Recent Versions and Applications of Sparrow Search Algorithm [J].

Awadallah, Mohammed A. ;

Al-Betar, Mohammed Azmi ;

Doush, Iyad Abu ;

Makhadmeh, Sharif Naser ;

Al-Naymat, Ghazi .

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2023, 30 (05) :2831-2858

[3]

Coello CAC, 2002, IEEE C EVOL COMPUTAT, P1051, DOI 10.1109/CEC.2002.1004388

[4] A fast and elitist multiobjective genetic algorithm: NSGA-II [J].

Deb, K ;

Pratap, A ;

Agarwal, S ;

Meyarivan, T .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197

[5] Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems [J].

Deb, Kalyanmoy .

EVOLUTIONARY COMPUTATION, 1999, 7 (03) :205-230

[6] A Multi-Objective Topology Optimization Methodology and its Application to Electromagnetic Actuator Designs [J].

Li, Yilun ;

Liu, Lei ;

Yang, Shiyou ;

Ren, Zhuoxiang ;

Ma, Yanhong .

IEEE TRANSACTIONS ON MAGNETICS, 2020, 56 (02)

[7] A COMPARISON OF THREE METHODS FOR SELECTING VALUES OF INPUT VARIABLES IN THE ANALYSIS OF OUTPUT FROM A COMPUTER CODE [J].

MCKAY, MD ;

BECKMAN, RJ ;

CONOVER, WJ .

TECHNOMETRICS, 1979, 21 (02) :239-245

[8] Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm [J].

Mirjalili, Seyedali .

KNOWLEDGE-BASED SYSTEMS, 2015, 89 :228-249

[9] A novel swarm intelligence optimization approach: sparrow search algorithm [J].

Xue, Jiankai ;

Shen, Bo .

SYSTEMS SCIENCE & CONTROL ENGINEERING, 2020, 8 (01) :22-34

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