PSO has a few parameters to adjust such as inertia weight, velocity and constant factors. Among these parameters, inertia weight is very important and has a great potential to develop. During the last decade, various methods like fuzzy, constant, linear methods were proposed to adjust an inertia weight. This paper proposes a new strategy to calculate inertia weight based on decreasing exponential method. Our method merely uses an iteration to make an inertia weight and it is fast and has highly accurate results rather than other strategies. Our results are tested on well-known benchmarks. Numerical results demonstrated our claim.Particle swarm optimization (PSO) is a population based technique first introduced by Kennedy and Eberhart in 1995[1]. PSO is based on the simulation of simplified animal social behaviors such as fish schooling, bird flocking, etc. Instead of using evolutionary operators to manipulate the individuals, PSO relies on the exchange of information between individuals [2]. The PSO method is becoming very popular due to its simplicity of implementation and ability to quickly to reach a reasonable solution [3]. Much of the work in PSO cares about adjusting inertia weight [3]. Shi at al proposed linearly decreasing inertia weight strategy, fuzzy inertia weight strategy and random inertia weight strategy.Results show that inertia weight is an important parameter in PSO algorithm. Great values of inertia weight could improve global exploration and small ones could improve local exploration. Unlike a GA, PSO does not use an evolution operator such as crossover and mutation [4]. We present a new strategy based on time varying using an iteration and compare with other strategy and numerical result show that this strategy is good in low generation and high accuracy.The rest of this paper is organized as follow: the next section introduce Basic PSO. A time varying strategy of inertia weight and mathematical proof of inertia weight presented in section 3. In section 4 new strategy and its description is presented, we describe the test functions and the numerical results and comparison with other methods in section 5, finally in section 6 we present summarization and conclude of our work. 2-Basic PSOIn PSO, each individual called particle a number in the swarm, called a particle and represent a potential solution that is a point in the search space the global optimum is regarded as the location of food .all particle named swarm. firstly , the PSO algorithm generate the particle of the population and initial velocity of each particle randomly then it modify the velocity and the best fitness value of each particle iteratively to search the optimum of the problem .each particle record two values, record the optimum by the particle itself and records the optimum of all particles in the population. each particle has a fitness value and a velocity to adjust its flying direction according to the best experiences of the swarm to