AN INTELLIGENT PARTICLE SWARM OPTIMIZATION(PSO) ALGORITHM TO OPTIMIZE EXPLOITATION (LOCAL SEARCH) AND EXPLORATION (GLOBAL SEARCH) TO ELECTIVE THE BEST LEADER.

Abstract

The searching of local search (exploitation) and global search (exploration) to find optimal results to improve optimality for a solution achieve by Swarm Intelligence algorithms are one of the category of Nature Inspired Computing algorithms. These SI are the best model for solving computational problems to find better optimal solution with appropriate results for a given problem. These Swarm Intelligence algorithms are including features are exploration and exploitation. The efficiency of these features influences the swarm intelligence metaheuristics algorithms. In existing research there is no principles and methods for better balancing of these two major features. In our proposed paper proposing balancing between exploration and exploitation of SI. To improve the performance for simulation tested by benchmark functions were performed by various dimensions 100,500 and 1000....n. In this paper SI algorithms are high capability of searching strategy of exploitation (local search) and exploration (global search) for finding optimum results to reach optimality. In this paper balance between exploitation and exploration using SI algorithms. We are considering a PSO (particle swarm optimization) algorithm is a SI algorithm to elective of best leaders among the group using by fitness function to balance exploitation and exploration to find best search strategy for an elective of best leader among the group.