Engineering Applications of Artificial Intelligence
Advances in Engineering Software
Engineering Applications of Artificial Intelligence
A New Clustering Algorithm Based on Artificial Immune System
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
A new multiobjective simulated annealing based clustering technique using symmetry
Pattern Recognition Letters
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
Development of immunized PSO algorithm and its application to Hammerstein model identification
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improved identification of Hammerstein plants using new CPSO and IPSO algorithms
Expert Systems with Applications: An International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch
IEEE Transactions on Evolutionary Computation
Engineering Applications of Artificial Intelligence
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Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.