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This work presents an improved edge detection algorithm using particle swarm optimization based on vector order statistics. The algorithm addressed the performance of edge detection in coloured images, with a view to minimizing broken, false and thick edges whilst reducing the presence of noise as well as computational time. A collection scheme based on step and ramp edges was developed for the edge detection algorithm, which explores a larger area in the images in order to reduce false and broken edges. The algorithm was also applied to facial and remotely sensed images in order to test the algorithm on real life images. The Pratt Figure of Merit (PFOM) was used as a quantitative comparison between the developed algorithm and the proven edge detection algorithms such as the roberts, prewitt, canny and laplacian edge detection algorithms.