Proposed and application of the Dragonfly algorithm for the camera placement problem

Abstract

In this paper, a method based on Dragonfly algorithm (DA) inspired by the motion and behaviors (dynamic or static) of artificial in environment is proposed to solve the optimal camera placement (OCP) problem. Ensuring illustration coverage of the surveillance space with a maximum area and minimum number of sensors is required. To ensure the maximum visual coverage, the utilitarian and homogeneous hypotheses are determined, attracting the characteristics of the sensor. In full, six evolutionary type algorithms based on nature inspired Meta heuristic algorithms, DA, Binary dragonfly algorithm (BDA), Particle Swarm Optimization (PSO), Chaotic dragonfly algorithm (CDA), Adaptive dragonfly algorithm (ADA), and GA are adapted to solve this optimal problem of surveillance camera placement based on maximum visual coverage. The proposed algorithms are applicable for all types of surveillance areas with predefined camera locations. In pole climbing scenarios, the location is not predefined and based upon the surveillance requirements the cameras move automatically. The most important in this work is to show a new adaptation of Dragonfly algorithm for optimization (OCP). His has proven its efficiency and superiority compared too many well-experienced meta-heuristics available in the literature.