Abstract
Quantum-behaved particle swarm optimization (QPSO) algorithm simulates quantum mechanics among individuals. For improving the local search ability of QPSO and guiding the search, an improved QPSO algorithm based on combining the dynamic mutation and cooperative background (MCQPSO) is proposed in this paper. The dynamic Cauchy mutation strategy is introduced to enhance the global search ability. The cooperative background strategy is used to change the updating mode of the particles in order to guarantee the effectiveness and simplification. The MCQPSO algorithm keeps the diversity of the population, and increasing convergence rates. Results compared with some previous study show that the MCQPSO algorithm performs much better than the Sun Jun's Cooperative Quantum-Behaved Particle Swarm Optimization (sunCQPSO) and WQPSO algorithm in terms of the image segmentation accuracy and the computation efficiency.
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