The nature of statistical learning theory
The nature of statistical learning theory
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
A Neural Network Based Approach to Automated E-Mail Classification
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
A Multi-Faceted Approach towards Spam-Resistible Mail
PRDC '05 Proceedings of the 11th Pacific Rim International Symposium on Dependable Computing
Information Sciences: an International Journal
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Intelligent Detection Approaches for Spam
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
GPU-based parallel particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An immunological filter for spam
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Multiple-Point bit mutation method of detector generation for SNSD model
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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Compared to conventional PSO algorithm, particle swarm optimization algorithms inspired by immunity-clonal strategies are presented for their rapid convergence, easy implementation and ability of optimization. A novel PSO algorithm, clonal particle swarm optimization CPSO algorithm, is proposed based on clonal principle in natural immune system. By cloning the best individual of successive generations, the CPSO enlarges the area near the promising candidate solution and accelerates the evolution of the swarm, leading to better optimization capability and faster convergence performance than conventional PSO. As a variant, an advance-and-retreat strategy is incorporated to find the nearby minima in an enlarged solution space for greatly accelerating the CPSO before the next clonal operation. A black hole model is also established for easy implementation and good performance. Detailed descriptions of the CPSO algorithm and its variants are elaborated. Extensive experiments on 15 benchmark test functions demonstrate that the proposed CPSO algorithms speedup the evolution procedure and improve the global optimization performance. Finally, an application of the proposed PSO algorithms to spam detection is provided in comparison with the other three methods.