This thesis proposes a new hybrid particle swarm optimizer, which employs landscape estimation and the cooperative behavior of different particles to significantly improve the performance of the original algorithm. The landscape estimation is to explore the landscape of the function in order to predict whether the function is unimodal or multimodal. Then we can decide how to optimize the function accordingly. The cooperative behavior is achieved by using two swarms, in which one swarm explores only a single dimension at a time, and the other explores all dimensions simultaneously. Furthermore, we also propose a movement tracking-based strategy to adjust the maximal velocity of the particles. This strategy can control the exploration and exploitation abilities of the swarm efficiency. Finally, we testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other recent variants of the PSO. The results show that our approach outperforms other methods in most of the benchmark problems.