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ABSTRACT: The image segmentation based on 2D histogram which considered gray information as well as spatial neighbor information between pixels of an image, was an efficient image segmentation method yet requires a large amount of computing time. By convergence analysis and simulation, the optimal value to study factor of second-order oscillating particle swarm optimization (SOPSO) algorithm was proposed firstly and it was different from standard PSO. The algorithm was applied to 2D maximum-entropy thresholding image segmentation. The simulation results showed that the algorithm could find the optimum 2D thresholding of an image rapidly and stably and the segmentation results of the Lena picture was ideal.
Natural Computation, 2009. ICNC '09. Fifth International Conference on; 01/2009
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ABSTRACT: Particle swarm optimization combined with simulated annealing algorithm (PSOCSA) was an improved particle swarm optimization algorithm which introduced the simulated annealing (SA) strategy in particle swarm optimization (PSO). It was proposed to solve a mathematical model which is built for aircraft departure sequencing problem in this paper. The correlative implementation techniques and detailed design process of the algorithm were presented. Then the simulation is performed to solve a representative problem using PSOCSA, PSO, and SA. The comparison showed that the PSOCSA algorithm was rational and feasible and more easily converge to the global optimal solution of aircraft departure sequencing problem. Method described in this paper will curtail the consumption of aircraft departure effectively, so it is worth researching it further in the field of airport operations and air traffic control.
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on; 07/2008
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Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008, June 1-6, 2008, Hong Kong, China; 01/2008
