Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization

Jian DONG; Xia YUAN; Meng WANG

doi:10.1631/FITEE.2100420

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Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization

Regular Papers | Updated：2022-09-29

- Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization
- 基于竞争的二进制多目标灰狼算法的快速紧凑天线拓扑优化
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 9, Pages: 1390-1406(2022)
- Affiliations：
  
  School of Computer Science and Engineering, Central South University, Changsha410083, China
- Author bio：
  
  E-mail: dongjian@csu.edu.cn
  yuan0927@csu.edu.cn
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2100420
  CLC： TN82
- Received：03 September 2021，
  
  Revised：2022-07-12，
  
  Accepted：19 April 2022，
  
  Published：23 September 2022
- Accepted：
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DONG Jian,YUAN Xia,WANG Meng.Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization^*#[J].Frontiers of Information Technology & Electronic Engineering,2022,23(09):1390-1406.
DOI：

DONG Jian,YUAN Xia,WANG Meng.Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization^*#[J].Frontiers of Information Technology & Electronic Engineering,2022,23(09):1390-1406. DOI： 10.1631/FITEE.2100420.

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。

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.

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