Ahmad Hoorfar

IEEE Distinguished Lecturer

Ahmad Hoorfar is a professor of electrical and computer engineering, the ECE department’s graduate chair, and the founder and director of Antenna Research Laboratory at Villanova University. He received his B.S. in electronics engineering from the University of Tehran and the M.S. and Ph.D. degrees in electrical engineering from the University of Colorado Boulder, His research contributions over the years have covered areas in electromagnetic field theory, numerical electromagnetics, multifunction printed and low-profile antennas, metamaterial media and surfaces, inverse scattering, microwave sensing and imaging, and stochastic optimization methods. He has been a pioneer in development and applications of evolutionary and global algorithms in electromagnetics, development of electromagnetic-based techniques for through-the-wall radar imaging (TWRI) and ground penetrating radar (GPR), compressive sensing applied to GPR and TWRI, and the use of the mathematical concept of space-filling curves in design of electrically small antennas, RFID tags, artificial magnetic conductors, and metasurfaces.

Ahmad Hoorfar

IEEE Distinguished Lecturer

Ahmad Hoorfar is a professor of electrical and computer engineering, the ECE department’s graduate chair, and the founder and director of Antenna Research Laboratory at Villanova University. He received his B.S. in electronics engineering from the University of Tehran and the M.S. and Ph.D. degrees in electrical engineering from the University of Colorado Boulder, His research contributions over the years have covered areas in electromagnetic field theory, numerical electromagnetics, multifunction printed and low-profile antennas, metamaterial media and surfaces, inverse scattering, microwave sensing and imaging, and stochastic optimization methods. He has been a pioneer in development and applications of evolutionary and global algorithms in electromagnetics, development of electromagnetic-based techniques for through-the-wall radar imaging (TWRI) and ground penetrating radar (GPR), compressive sensing applied to GPR and TWRI, and the use of the mathematical concept of space-filling curves in design of electrically small antennas, RFID tags, artificial magnetic conductors, and metasurfaces.

Nature-inspired global optimization techniques have revolutionized the design of complex electromagnetic devices and structures with demanding specifications. These techniques can generally be categorized into two groups: one based on Darwinian and evolutionary principles and the other inspired by swarm intelligence, mimicking the collective behavior of species such as a swarm of bees, a flock of birds, or a school of fish. In this lecture, I will first provide an overview of the various evolutionary and swarm-intelligence optimization paradigms, along with their historical development. I will then highlight our research team's work on Evolutionary Programming (EP) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), which have demonstrated superior performance compared to Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) in numerous benchmark antenna and electromagnetic optimization problems. Unlike GAs and PSO, EP and CMA-ES employ adaptive and self-adaptive variation operators, enabling efficient parameter adaptation during the evolution process. By leveraging the underlying physics of the problem, mutation schemes can be designed to significantly enhance convergence rates toward the global solution.

This presentation will cover optimization techniques for both continuous and mixed (continuous-discrete) parameters, with illustrative examples in the design of novel antenna elements and arrays, dielectric filters, metasurfaces, and inverse profiling of subsurface objects.