Applying Chaotic Particle Swarm Optimization to the Template Matching Problem
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Research on improved QPSO algorithm based on cooperative evolution with two populations
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Chaotic hybrid algorithm and its application in circle detection
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Expert Systems with Applications: An International Journal
An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection
Computers & Mathematics with Applications
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Particle swarm optimization is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. But it’s easy to be trapped into local optimum. Based on the chaos theory, the random function is introduced to Tent map, and the improved Tent map is introduced to PSO. Update the velocity and position of the particle by the improved Tent map instead of the random parameters. Eliminate the particle whose position is the farthest to the optimal solution after iterating certain steps, and reestablish the position of new particle according to average value of the positions of all the particles to search again. The algorithm has faster convergence and better global optimization capability. The improved Tent PSO is applied to the investment optimization, and the result of simulation shows better optimization function.