Optimization in Electromagnetics

The Particle Swarm Optimization algorithm mimics the natural processes observed in bird flocks while searching for food

The Particle Swarm Optimization algorithm mimics the natural processes observed in bird flocks while searching for food

Global optimization techniques have a wide range of applications in engineering, science, and mathematics. They comprise a small subset of optimization techniques (or metaheuristics) which can attempt to optimize a given problem without any a priori knowledge. This is particularly important in the field of electromagnetics, where Maxwell’s equations describe the operation of devices such as antennas and microwave circuits. In general, Maxwell’s equations can be categorized as coupled vector partial differential equations, leading to very complicated function landscapes. From an optimization perspective, the resulting fitness functions describing antenna and microwave circuit performance are typically multimodal, non-differentiable, highly dimensional, non-convex, nonlinear, discontinuous, and ill-conditioned functions. Basically, optimization problems in electromagnetics fall within the hardest class of optimization problems to solve. To make matters worse, engineers usually have very little knowledge about the best place to start searching for good designs. Among the various algorithms in literature, my primary experience has been with the usage of nature-inspired optimization techniques, which attempt to mimic naturally-occurring processes observed by scientists. One such example is the Particle Swarm Optimization algorithm. This algorithm attempts to mimic a flock of birds searching for food in a given landscape, as illustrated in the figure to the right. Other techniques that I have used include Genetic Algorithms and Covariance Matrix Adaptation Evolution Strategies. My primary interest in these algorithms is their application in achieving excellent designs for a given application and also in their usage to give devices intelligence.

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