Task scheduling takes an important role in cloud computing. The result using traditional Particle Swarm Optimization (PSO) algorithm is local optimization. In this paper CPSO algorithm is proposed. The algorithm based on Tent mapping can achieve global optimization by using chaotic optimization search technique and PSO. It is implemented based on fast convergence of PSO algorithm, as well as, ergodictiy and randomness of chaotic algorithm. It generates initial values through chaotic assignment during the initialization of particles whose inertial weights are adjusted on the basis of their adaptive values. Particles with local optimum are updated using chaotic algorithm to achieve global optimum. Experimental results on Cloud Sim simulation platform show that CPSO algorithm is effective in reducing execution time of tasks, and has better real-time and optimization capabilities.
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