Many kinds of particle swarm optimization (PSO) technique are now available and various efforts have been made to solve linear and non linear problems as well as one dimensional and multidimensional problem of geophysical data. Particle swarm optimization is a Meta heuristic optimization method that requires the intelligent guess and suitable selection of controlling parameters (i.e. Inertia weight and acceleration coefficient) for better convergence at global minima. The proposed technique

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... d–PSO is an improved technique of PSO, in which effort has been made for choosing the controlling parameters and these parameters have selected after analysing the response of various possible exercises using synthetic gravity anomalies over various geological sources. The applicability and efficacy of the proposed method is tested and also validated using synthetic gravity anomalies over various source geometries. Finally Tuned-PSO is applied over field residual gravity anomalies of two different geological terrains to find out the model parameters namely amplitude coefficient factor (A), shape factor (q) and depth (z). The analysed results have been compared with published results obtained by different methods that show a significantly excellent agreement with real model parameters. The results also show that the proposed approach is not only superior to the other methods but also shows that the strategy has enhanced the exploration capability of proposed method. Thus Tuned–PSO is an efficient and more robust technique to achieve optimal solution with minimal error.