Abstract: A hybrid of Genetic Algorithm (GA) & PSO for associate optimized path designing for known atmosphere containing obstacles is conferred. The goal of our project is to seek out a best answer with best price & minimum distance to achieve destination. There are two sorts of atmosphere that are known and unknown atmosphere. Our project is based on known atmosphere. Earlier projects were supported single objective however when it involves real world,several parameters affects like temperature, humidity etc. We apply hybrid of GA with PSO. We have implemented the algorithms described in this paper and finally our approach can perform in various known environment with minimum energy and minimum distance. For the PSO parameter we have taken 500 of maximum iteration, 150 No of population (SWARM size), 1 inertia weight, 0.98 Inertia Weight Damping Ratio, c1 =1.5 Personal Learning Coefficient, c2=1.5 Global Learning Coefficient and with best cost we get result after the simulation in matlab using hybridization of GA & PSO.
Keywords: PSO, GA , Hybrid, Known Atmosphere, Single Objective, Damping Ratio, Global Learning Coefficient.