Optimization approaches, inspired by different metaphors, have recently attracted the interest of the scientist community. On the other hand, circle detection over digital images has received considerable attention from the computer vision community over the last few years as tremendous efforts have been directed towards seeking for an optimal detector. This chapter presents an algorithm for the automatic detection of circular shapes embedded into cluttered and noisy images with no consideration of conventional Hough transform techniques. The approach is based on a physics-inspired technique known as the Electromagnetism-like Optimization (EMO). It follows the Electromagnetism principle regarding a attraction-repulsion mechanism which manages particles towards an optimal solution. Each particle represents a solution by holding a charge which is related to the objective function to be optimized. The algorithm uses the encoding of three non-collinear points embedded into the edge map as candidate circles. Guided by the values of the objective function, the set of encoded candidate circles (charged particles) are evolved using the EMO algorithm so that they can fit into actual circular shapes over the edge map. Experimental evidence from several tests on synthetic and natural images which provide a varying range of complexity validates the efficiency of our approach regarding accuracy, speed and robustness.