Numerical optimization is a powerful and necessary tool in human modern life. In mathematics and computer Science, optimization refers to choosing the best option between available choices. Nowadays, optimization is used in many aspects of human life including engineering, medicine, agriculture and economy. For example many engineering design problems can also be expressed as optimization problems. Due to growing number of optimization problems and growing complexity of them, it seems that we need to improve and develop theoretical and practical optimization methods. Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm iired by social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multi-modal functions. In this thesis, two new methods for improving PSO have been introduced. The first method has utilized a new operator which called Plowing Operator for effectively initializing PSO. The plowing operator tries to improve exploration ability of the algorithm in early search steps by combining random search with it. The second method which has been named Light Adaptive Particle Swarm Oprtimization uses plowing operator, genetic algorithm’s selection operator and a new operator which is named adaptive mutation operator to improve PSO. The selection operator has been used to increase convergence speed of the algorithm. Adaptive mutation operator is a kind of mutation operator which adds a guassian noise to the components of the solution, the amplitude of this noise decreases over the time so this operator helps exploration in early phases and facilitate exploitation in last phases. Moreover, a simple control system with fuzzy approach has been utilized to use adjunct operators effectively. Our approaches are validated using some common complex unimodal/multi-modal benchmark functions and results have been compared with results of genetic algorithm, basic PSO and nine variations of the PSO. The simulation results demonstrate that the proposed approaches are successful in improving PSO algorithm results in global optimization problems. Keywords: Particle Swarm Optimization, Computational Swarm Intelligence, Numerical Optimization, Premature Convergence