Quantum Behaved Multi-objective PSO and ACO Optimization for Multi-level Thresholding

In this paper, two quantum behaved multi-objective optimization techniques, based on Binary Particle Swarm Optimization and Ant Colony Optimization, have been introduced. The proposed approaches are used to search optimal threshold values of gray scale images, by optimizing the non-dominated solutions using Li´s method as objective function. These approaches coalesce the meta-heuristic algorithms with the intrinsic features of quantum theory to make the techniques more effective. The best fitness values, the set of optimal thresholds and the computation times at different level of thresholding have been reported both for the proposed techniques and their equivalent classical counterparts. The superiority of the techniques presented in this paper, are established in terms of computational time. Thereafter, the mean fitness and the standard deviation of the objective values prove that the proposed techniques are more effectual of than others. Finally, the performance of each technique has been evaluated by determining the PSNR values of the test images. It was found that the proposed techniques have better PSNR values as compared to their corresponding components. Hence, quality of thresholding is established in favour of the proposed techniques.