Nowadays nature-inspired algorithm and optimization problem are two of the hottest problems in computer science. There are quite many nature-inspired algorithms like Bats, Fireflies, and Genetic Algorithm and so on, which are inspired by the behavior of animals and insects. Among these algorithms, Particle Swarm Optimization Algorithm (PSO) shows exhibited performance in solving various optimization problems. So recently quite a lot of attention has been paid to. PSO is a population-based stochastic optimization method originally attributed by James Kennedy and R. C. Eberhart in 1995. PSO algorithm is based on the social behavior of birds flying in a flock. [1] As one of these intelligence optimization algorithms, PSO has a fairly fast velocity to approximate the optimal solution, which can effectively optimize the parameters of the system. But the disadvantages of original PSO algorithm include: easy to fall into the local optimal, premature convergence, poor local optimization and so on. In this thesis the author introduced a novel optimization algorithm, called “Random Distribution Bounded chaotic PSO algorithm”. This algorithm used some chaotic maps and Random Distribution Bounded method to improve the original PSO algorithm, in order to enhance its searching performance. Through computer simulation experiments, three different versions of chaotic PSO algorithm which are implemented with 3 different chaotic maps are tested. By testing with 25 different benchmarking optimization functions, the results by the new chaotic PSO are compared with those of the original PSO. Furthermore, the performances between the Random Distribution Bounded Chaotic PSO algorithm, original PSO algorithm, Firefly algorithm and Bat algorithm are evaluated comparatively. At last the Random Distribution Bounded Chaotic Particle Swarm Optimization algorithm which has outperformed the other versions of PSO, would be put under test in a real-life optimization case study, to solve the Job-Scheduling Problem which has been attempted by many researchers. The results confirm again the efficacy of the Random Distribution Bounded Chaotic PSO algorithm.