Particle Swarm Optimization Algorithm Based on Predatory Search Strategy and Its Application

According to the intelligent behavior of social population, a novel particle swarm optimization algorithm based on predatory search strategy (PS-CPSO) is proposed by introducing the centroid of particle swarm in the standard particle swarm optimization model to improve global optimum efficiency and accuracy of algorithm. Results of Benchmark function simulation and material balance computation (MBC) in alumina production show that the new method not only improves the local searching efficiency and global searching performance greatly, but also has faster convergence speed and higher precision, and can avoid the premature convergence problem effectively.