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A novel chaos particle swarm optimization (PSO) and its application in pavement maintance decision

Particle swarm optimization (PSO) algorithm is a new random global optimization algorithm, and the simple PSO (SPSO) is short of high convergence speed, strong optimization ability and so on. To improve the optimization property of SPSO, a novel chaos particle swarm optimization (CPSO) algorithm is presented. The characteristics of ergodicity and randomness of chaotic variables are considered to produce the initial positions of particles. On the basis of population diversity evaluated through “distance-to-average-point”, the local search is carried out for mature individuals by chaos disturbance, which is helpful for them to jump out of the local minimum. Compared with the corresponding other PSO algorithms, the function optimization results indicate that the searching properties including searching efficiency and precision of CPSO algorithm are obviously better than other PSO algorithms. Finally, aiming at the expressway pavement maintenance, the CPSO algorithm is used to optimize the pavement maintenance decision.