This research work presents an improved edge detection algorithm using particle swarm optimization based on vector order statistics. The proposed algorithm was implemented using MATLAB 2013 script. The algorithm addressed the performance of edge detection in 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 efficiency of this algorithm was tested on two Berkeley benchmark images in clean and noisy environments with a view to comparing results, both visually and quantitatively, with those obtained using proven edge detection algorithms such as the Sobel, Prewitt, Roberts, Laplacian and Canny edge detection algorithms. The algorithm was also applied to facial and remotely sensed images with a view to testing 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. The benchmark value for the PFOM is between 0-1, which shows efficient detection of edges as the value tends towards 1. The quantitative results obtained using PFOM on the test images in clean environment for the Sobel, Prewitt, Roberts, Laplacian, Canny and the proposed edge detection algorithms are 0.4209, 0.4195, 0.4181, 0.7048, 0.8421 and 0.8480, respectively. This showed that the proposed algorithm detected more edges in clean environment as the value obtained is nearest to 1. The PFOM on the test images in noisy environment for the Sobel, Prewitt, Roberts, Laplacian, Canny and the proposed edge detection algorithms are 0.4191, 0.4191, 0.2807, 0.2811, 0.5606 and 0.8458 respectively. This showed that the proposed algorithm detected more edges in noisy environment as the value obtained is nearest to 1. The proposed algorithm achieved a PeakSignal-to Noise Ratio (PSNR) of 57.7320dB in environment containing ≤ 33% of noise level. This result signifies 3% improvement in detection of edges in noisy environment as compared with the proven traditional edge detection algorithms which achieved an average PSNR of 22-35dB.