Improved Electromagnetics Optimization: The covariance matrix adaptation evolutionary strategy.

Micah D. Gregory, Spencer V. Martin, Douglas H. Werner

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


The covariance matrix adaptation evolutionary strategy (CMA-ES) is explored here as an improved alternative to well-established algorithms used in electromagnetic (EM) optimization. In the past, methods such as the genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) have commonly been used for EM design. In this article, we examine and compare the performance of CMA-ES, PSO, and DE when applied to test functions and several challenging EM design problems. Of particular interest is demonstrating the ability of the relatively new CMA-ES to more quickly and more reliably find acceptable solutions compared with those of the more classical optimization strategies. In addition, it will be shown that due to its self-Adaptive scheme, CMA-ES is a more user-friendly algorithm that requires less knowledge of the problem for preoptimization configuration.

Original languageEnglish (US)
Article number7163535
Pages (from-to)48-59
Number of pages12
JournalIEEE Antennas and Propagation Magazine
Issue number3
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Electrical and Electronic Engineering


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