Application And Implementation Of Cloud Computing Task Scheduling Of Chaos Particle Swarm Chicken Swarm Fusion Optimization Algorithm

The world is rapidly flying into a great age, namely, cloud computing age. Cloud computing has become the core of the whole world ICT(Information and Communication Technology) industry. The task scheduling of cloud computing is one of the important topics, many researchers have paid energy.In 1995, Doctor Eberhart and Doctor Kennedy proposed Particle Swarm Optimization(PSO) algorithm. In Particle Swarm Optimization(PSO) algorithm, because the process of the particle initialization and evolutionary are randomly. It makes the update of the gbest and the pbest having certain blindness and affects the evolution of convergence. Using the advantages of the chaos, Liu B proposed a new algorithm: Chaotic Particle Swarm Optimization(CPSO). The algorithm improves the ability of the Particle Swarm Optimization(PSO) algorithm easy to get rid of local extremum and improves the convergence precision and speed of the algorithm.In 2014, Xianbing Meng, Yu Liu, Xiaozhi Gao and Hengzhen Zhang proposed a new bionics algorithm: Chicken Swarm Optimization(CSO). The Chicken Swarm Optimization(CSO) is a new bionics algorithm proposed for optimizing applications. Simulating hierarchical levels and behaviors of the chicken, include roosters, hens and chicks, chicken can be effectively used to solve the chicken swarm intelligence optimization problems.In order to achieve the implementation and application of task scheduling on the cloud platform, the paper presents the Chaos Particle Swarm Chicken Swarm Fusion Optimization(CPSCSFO) algorithm. The advantage of chaos algorithm is that using chaotic variables to search usually has more advantages than random searching disorderly. The Particle Swarm Optimization(PSO) algorithm has the advantage of searching speed, high efficiency, suitable for real value type. The Chicken Swarm Optimization(CSO) is a typical multiple populations algorithm with good accuracy and robustness. The Chaos Particle Swarm Chicken Swarm Fusion Optimization(CPSCSFO) algorithm integrates the above three algorithms related advantages.The Chaos Particle Swarm Chicken Swarm Fusion Optimization(CPSCSFO) algorithm uses the chaotic optimization ideology in the initialization process of the population, it makes the particles have the nature of random. The Chaos Particle Swarm Chicken Swarm Fusion Optimization(CPSCSFO) uses the multiple group advantage of the Chicken Swarm Optimization(CSO) to update the position of the population. It makes the population of different individuals according by different learning strategies to update speed and locations.It ensures that each of different groups can keep self-reliance and excellence.It reduces space and time complexity of the algorithm. The Chaos Particle Swarm Chicken Swarm Fusion Optimization(CPSCSFO) algorithm uses the chaotic optimization ideology to optimize the position of the populations. It helps the inert particles get rid of the local minimum point and quickly find the optimal solution.Finally the paper uses the Cloud Sim simulator to simulate and compare the performances. Comparing the performances of the Chaos Particle Swarm Chicken Swarm Fusion Optimization(CPSCSFO) algorithm proposed by the paper with Round-Robin Scheduling(RRS) algorithm, Particle Swarm Optimization(PSO) algorithm and Chaos Particle Swarm Optimization(CPSO) algorithm. The main two goals of comparing the performances of the algorithm are task total execution time and the total degree of load balancing. The experimental results show that the Chaos Particle Swarm Chicken Swarm Fusion Optimization(CPSCSFO) algorithm proposed by the paper comparing with Round-Robin Scheduling(RRS) algorithm, Particle Swarm Optimization(PSO) algorithm and Chaos Particle Swarm Optimization(CPSO) algorithm on the degree of task execution time, and load balancing has certain advantages.