Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial immune multi-objective SAR image segmentation with fused complementary features
Information Sciences: an International Journal
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Decentralized Output-Feedback Neural Control for Systems With Unknown Interconnections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Quantum-Inspired Immune Clonal Algorithm for Global Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks
IEEE Transactions on Neural Networks
Adaptive Evolutionary Artificial Neural Networks for Pattern Classification
IEEE Transactions on Neural Networks
| Hi-index | 0.89 |
The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms.