Abstract
We propose a competitive binary multi-objective grey wolf optimizer (CBMOGWO) to reduce the heavy computational burden of conventional multi-objective antenna topology optimization problems. This method introduces a population competition mechanism to reduce the burden of electromagnetic (EM) simulation and achieve appropriate fitness values. Furthermore, we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary multi-objective grey wolf optimizer (BMOGWO) to achieve a good balance between exploration and exploitation. Then, the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems (MOTPs) and four multi-objective knapsack problems (MOKPs) by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization (BMOPSO). Finally, the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output (MIMO) antenna with high-dimensional mixed design variables and multiple objectives. The experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods, which indicates that our method is highly efficient for complex antenna topology optimization problems. It provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms (MOEAs) in a flexible and efficient manner.
摘要
为降低传统多目标天线拓扑优化问题的计算量, 本文提出一种基于竞争的二进制多目标灰狼优化算法 (CBMOGWO). 该方法引入种群竞争机制, 以减轻电磁 (EM) 仿真的负担并获取适当的适应度值. 此外, 我们引入余弦振荡函数来改进原始二进制多目标灰狼优化算法 (BMOGWO) 的线性收敛因子, 以在探索和开发之间达到良好平衡. 然后, 通过与原始BMOGWO和传统二进制多目标粒子群优化 (BMOPSO) 在12个多目标优化测试问题 (MOTPs) 和4个多目标背包问题 (MOKPs) 上比较, 验证了CBMOGWO的性能. 最后, 通过具有高维混合设计变量和多个目标的紧凑型高隔离双频多输入多输出 (MIMO) 天线的示例, 验证了我们的方法在降低计算成本方面的有效性. 实验结果表明, 与传统方法相比, CBMOGWO节省近一半的计算成本, 这表明我们的方法对于复杂天线拓扑优化问题是高效的. 它为基于多目标进化算法 (MOEA) 以灵活高效的方式探索新的和意想不到的天线结构提供了新思路.
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References
Aldhafeeri A, Rahmat-Samii Y, 2019. Brain storm optimization for electromagnetic applications: continuous and discrete. IEEE Trans Antenn Propag, 67(4):2710–2722. https://doi.org/10.1109/TAP.2019.2894318
Balanis CA, 2016. Antenna Theory: Analysis and Design (4th Ed.). John Wiley & Sons, Hoboken, USA.
Bataineh M, Marler T, 2017. Neural network for regression problems with reduced training sets. Neur Netw, 95:1–9. https://doi.org/10.1016/j.neunet.2017.07.018
Bin F, Wang F, Chen S, et al., 2020. Pareto-optimal design of UHF antenna using modified non-dominated sorting genetic algorithm II. IET Microw Antenn Propag, 14(12):1404–1410. https://doi.org/10.1049/iet-map.2020.0121
Carvalho R, Saldanha RR, Gomes BN, et al., 2012. A multi-objective evolutionary algorithm based on decomposition for optimal design of Yagi-Uda antennas. IEEE Trans Magn, 48(2):803–806. https://doi.org/10.1109/tmag.2011.2174348
Chen YK, Wang CF, 2012. Synthesis of reactively controlled antenna arrays using characteristic modes and DE algorithm. IEEE Antenn Wirel Propag Lett, 11:385–388. https://doi.org/10.1109/lawp.2012.2191584
Chirikov R, Rocca P, Manica L, et al., 2013. Innovative GA-based strategy for polyomino tiling in phased array design. Proc 7th European Conf on Antennas and Propagation, p.2216–2219.
Coello CAC, Pulido GT, Lechuga MS, 2004. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput, 8(3):256–279. https://doi.org/10.1109/tevc.2004.826067
Dhaliwal BS, Pattnaik SS, 2017. BFO-ANN ensemble hybrid algorithm to design compact fractal antenna for rectenna system. Neur Comput Appl, 28(1):917–928. https://doi.org/10.1007/s00521-016-2402-9
Ding K, Gao C, Qu DX, et al., 2017. Compact broadband MIMO antenna with parasitic strip. IEEE Antenn Wirel Propag Lett, 16:2349–2353. https://doi.org/10.1109/LAWP.2017.2718035
Dong J, Li QQ, Deng LW, 2018. Design of fragment-type antenna structure using an improved BPSO. IEEE Trans Antenn Propag, 66(2):564–571. https://doi.org/10.1109/TAP.2017.2778763
Dong J, Li YJ, Wang M, 2019a. Fast multi-objective antenna optimization based on RBF neural network surrogate model optimized by improved PSO algorithm. Appl Sci, 9(13): 2589. https://doi.org/10.3390/app9132589
Dong J, Qin WW, Wang M, 2019b. Fast multi-objective optimization of multi-parameter antenna structures based on improved BPNN surrogate model. IEEE Access, 7:77692–77701. https://doi.org/10.1109/ACCESS.2019.2920945
Du YJ, Wu XP, Sidén J, et al., 2020. Design of ultra-wideband antenna with high-selectivity band notches using fragment-type etch pattern. Microw Opt Technol Lett, 62(2):912–918. https://doi.org/10.1002/mop.32103
Emary E, Zawbaa HM, Hassanien AE, 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172:371–381. https://doi.org/10.1016/j.neucom.2015.06.083
Gupta N, Saxena J, Bhatia KS, 2020. Optimized metamaterial-loaded fractal antenna using modified hybrid BF-PSO algorithm. Neur Comput Appl, 32(11):7153–7169. https://doi.org/10.1007/s00521-019-04202-z
Ishibuchi H, Masuda H, Tanigaki Y, et al., 2015. Modified distance calculation in generational distance and inverted generational distance. Proc 8th Int Conf on Evolutionary Multi-Criterion Optimization, p.110–125. https://doi.org/10.1007/978-3-319-15892-1_8
Jehangir SS, Sharawi MS, 2020. A compact single-layer four-port orthogonally polarized Yagi-like MIMO antenna system. IEEE Trans Antenn Propag, 68(8):6372–6377. https://doi.org/10.1109/tap.2020.2969810
Jia XN, Lu GZ, 2019. A hybrid Taguchi binary particle swarm optimization for antenna designs. IEEE Antenn Wirel Propag Lett, 18(8): 1581–1585. https://doi.org/10.1109/LAWP.2019.2924247
Kaur J, Nitika, Panwar R, 2019. Design and optimization of a dual-band slotted microstrip patch antenna using differential evolution algorithm with improved cross polarization characteristics for wireless applications. J Electromagn Waves Appl, 33(11): 1427–1442. https://doi.org/10.1080/09205071.2019.1612283
Kim Y, Walton EK, 2006. Automobile conformal antenna design using non-dominated sorting genetic algorithm (NSGA). IEE Proc Microw Antenn Propag, 153(6):579–582. https://doi.org/10.1049/ip-map:20050055
Koziel S, Bekasiewicz A, 2016. Fast multi-objective surrogate-assisted design of multi-parameter antenna structures through rotational design space reduction. IET Microw Antenn Propag, 10(6):624–630. https://doi.org/10.1049/iet-map.2015.0631
Koziel S, Ogurtsov S, 2013. Multi-objective design of antennas using variable-fidelity simulations and surrogate models. IEEE Trans Antenn Propag, 61(12):5931–5939. https://doi.org/10.1109/TAP.2013.2283599
Kumar J, 2016. Compact MIMO antenna. Microw Opt Technol Lett, 58(6):1294–1298. https://doi.org/10.1002/mop.29843
Li CM, Li Z, Jun X, et al., 2020. The impact of data quality on neural network models. Proc Int Conf on Cyber Security Intelligence and Analytics, p.657–665. https://doi.org/10.1007/978-3-030-15235-2_91
Li QQ, Chu QX, Chang YL, et al., 2020a. Tri-objective compact log-periodic dipole array antenna design using MOEA/D-GPSO. IEEE Trans Antenn Propag, 68(4):2714–2723. https://doi.org/10.1109/tap.2019.2949705
Li QQ, Chu QX, Chang YL, 2020b. Design of compact high-isolation MIMO antenna with multiobjective mixed optimization algorithm. IEEE Antenn Wirel Propag Lett, 19(8): 1306–1310. https://doi.org/10.1109/LAWP.2020.2997874
Li R, Xu L, Hu W, et al., 2017. Low-cross-polarisation synthesis of conformal antenna arrays using a balanced dynamic differential evolution algorithm. IET Microw Antenn Propag, 11(13):1853–1860. https://doi.org/10.1049/iet-map.2017.0461
Li YL, Shao W, You L, et al., 2013. An improved PSO algorithm and its application to UWB antenna design. IEEE Antenn Wirel Propag Lett, 12:1236–1239. https://doi.org/10.1109/lawp.2013.2283375
Lin ZQ, Yao ML, Shen XW, 2012. Sidelobe reduction of the low profile multi-subarray antenna by genetic algorithm. AEU-Int J Electron Commun, 66(2):133–139. https://doi.org/10.1016/j.aeue.2011.06.006
Marler RT, Arora JS, 2004. Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim, 26(6):369–395. https://doi.org/10.1007/s00158-003-0368-6
Marler RT, Arora JS, 2009. Multi-objective Optimization: Concepts and Methods for Engineering. VDM Publishing. https://doi.org/10.1142/9789812779670_0004
Mirjalili S, Mirjalili SM, Lewis A, 2014a. Grey wolf optimizer. Adv Eng Softw, 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mirjalili S, Mirjalili SM, Yang XS, 2014b. Binary bat algorithm. Neur Comput Appl, 25(3–4):663–681. https://doi.org/10.1007/s00521-013-1525-5
Mirjalili S, Saremi S, Mirjalili SM, et al., 2016. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl, 47:106–119. https://doi.org/10.1016/j.eswa.2015.10.039
Panduro MA, Covarrubias DH, Brizuela CA, et al., 2005. A multi-objective approach in the linear antenna array design. AEU-Int J Electron Commun, 59(4):205–212. https://doi.org/10.1016/j.aeue.2004.11.017
Panduro MA, Brizuela CA, Garza J, et al., 2013. A comparison of NSGA-II, DEMO, and EM-MOPSO for the multi-objective design of concentric rings antenna arrays. J Electromagn Waves Appl, 27(9):1100–1113. https://doi.org/10.1080/09205071.2013.801040
Pietrenko-Dabrowska A, Koziel S, Al-Hasan M, 2020. Cost-efficient bi-layer modeling of antenna input characteristics using gradient Kriging surrogates. IEEE Access, 8:140831–140839. https://doi.org/10.1109/ACCESS.2020.3013616
Ren ZY, Zhao AP, 2019. Dual-band MIMO antenna with compact self-decoupled antenna pairs for 5G mobile applications. IEEE Access, 7:82288–82296. https://doi.org/10.1109/ACCESS.2019.2923666
Sharawi MS, Numan AB, Khan MU, et al., 2012. A dual-element dual-band MIMO antenna system with enhanced isolation for mobile terminals. IEEE Antenn Wirel Propag Lett, 11:1006–1009. https://doi.org/10.1109/LAWP.2012.2214433
Tian Y, Cheng R, Zhang XY, et al., 2017. PlatEMO: a MATLAB platform for evolutionary multi-objective optimization [Educational Forum]. IEEE Comput Intell Mag, 12(4):73–87. https://doi.org/10.1109/MCI.2017.2742868
Zhang L, Wang X, He SQ, 2019. Topology optimization of antenna for maximum bandwidth design. Proc IEEE Int Conf on Computational Electromagnetics, p.1–3. https://doi.org/10.1109/COMPEM.2019.8779201
Zhang QF, Li H, 2007. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput, 11(6):712–731. https://doi.org/10.1109/TEVC.2007.892759
Zhang QF, Zhou AM, Zhao SZ, et al., 2009. Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition. Technical Report CES-487.
Zhu SH, Yang XS, Wang J, et al., 2019. Design of MIMO antenna isolation structure based on a hybrid topology optimization method. IEEE Trans Antenn Propag, 67(10): 6298–6307. https://doi.org/10.1109/TAP.2019.2920295
Zitzler E, Thiele L, 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput, 3(4):257–271. https://doi.org/10.1109/4235.797969
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Xia YUAN designed the research. Xia YUAN and Jian DONG processed the data. Xia YUAN drafted the paper. Jian DONG helped organize the paper. Jian DONG and Meng WANG revised and finalized the paper.
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Jian DONG, Xia YUAN, and Meng WANG declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (Nos. 61801521 and 61971450), the Natural Science Foundation of Hunan Province, China (No. 2018JJ2533), and the Fundamental Research Funds for the Central Universities, China (Nos. 2018gczd014 and 20190038020050)
List of supplementary materials
1 Gery wolf optimizer and multi-objective grey wolf optimizer
2 Exploration and exploitation analysis
3 Procedure 1
4 Multi-objective test problems and multi-objective knapsack problems
Table S1 Bi-objective MOTPs
Table S2 Tri-objective MOTPs
Table S3 Five-objective MOTPs
Table S4 Statistical results for IGD on UF1 to UF12 on MOTPs
Fig. S1 Boxplot of the statistical results for IGD on UF1 to UF12 on MOTPs
Fig. S2 Evolution of the average IGD value on UF1 to UF12 on MOTPs
Table S5 Statistical results for HV on UF1 to UF12 on MOTPs
Fig. S3 Boxplot of the statistical results for HV on UF1 to UF12 on MOTPs
Fig. S4 Evolution of the average HV value on UF1 to UF12 on MOTPs
Table S6 Statistical results for HV on MOKPs
Fig. S5 Boxplot of the statistical results for HV on MOKPs
Fig. S6 Evolution of the average HV value on MOKPs
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Dong, J., Yuan, X. & Wang, M. Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization. Front Inform Technol Electron Eng 23, 1390–1406 (2022). https://doi.org/10.1631/FITEE.2100420
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DOI: https://doi.org/10.1631/FITEE.2100420
Key words
- Antenna topology optimization
- Multi-objective grey wolf optimizer
- High-dimensional mixed variables
- Fast design