Optimization of surveillance cost of a neighborhood using soft computing techniques

Abstract

This work presents a genetic algorithm for the optimization of surveillance cost of a neighborhood. For a given map view (Google map view, satellite view, etc.) of any neighborhood, the aim is to estimate the minimum number of CCTV monitoring poles and their position required for complete surveillance. We will demonstrate how this problem models as a minimum vertex cover problem (MVCP). As MVC problem is NP-Hard problem, to deal efficiently with this optimization problem, we will apply a genetic based approach. Performance of the proposed genetic algorithm is also compared with the clever greedy algorithm and the natural heuristic for vertex cover. The modeling approach is illustrated with a college campus named Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand-826004.