Paper Title
Hybrid Model of Optimization Algorithms in Efficient Design Filters

Abstract
This paper deals with the involvement of hybrid algorithm and its efficient optimizing techniques in the modern and upcoming technologies. It finds its varied application in various optimization tools. The Particle Swarm Optimization (PSO) algorithm utilizes a number of particle vectors kinetically circumventing in the solution space probing for the optimist solution. Every particle in the algorithm acts as a point in the N-dimensional space. Each particle keeps the information in the solution space for every iteration and the best solution is calculated that has obtained by that particle is called personal best (pbest). The Genetic Algorithm (GA) is based on the Darwin theory of “Survival of The Fittest” wherein the fittest species among the group or population are crossed with respect to the fitness function and the mutations among the reproduced individuals helps in achieving the desired optimized result. This solution is obtained according to the personal experiences of each particle vector. Here in this paper we are using “MATLAB Software” to achieve the desired optimization using PSO-GA. The objective equation is coded in the MATLAB interface where we have taken two input variables whose values have to be optimized After the MATLAB code is run, The inbuilt optimization tool in MATLAB Software provides us the best possible value of the input entities or variables of the objective equation. The best finest value of the input entities or variables of the equation achieved using PSO-GA technique can be used to provide accurate and best possible results. Keywords - Crossover, Finest, Optimization, Selection