In recent years, Wireless Sensor Networks (WSNs) have transitioned from being objects of academic research interest to a technology that is frequently being employed in real-life applications and rapidly being commercialized. The performance of a WSN is largely affected by high quality deployment and precise localization of sensor nodes. This article deliberates autonomous deployment of sensor nodes from an Unmanned Aerial Vehicle (UAV). This kind of deployment has importance in emergency applications, such as disaster monitoring and battlefield surveillance. The goal is to deploy the nodes only in the terrains of interest, which are distinguished by segmentation of the images captured by a camera on board the UAV. In this article we propose an improved variant of a very powerful real parameter optimizer, called Differential Evolution (DE) for image segmentation and for distributed localization of the deployed nodes. Image segmentation for autonomous deployment and distributed localization are designed as multidimensional optimization problems and are solved by the proposed algorithm. Performance of the proposed algorithm is compared with other prominent adaptive DE-variants like SaDE and JADE as well as a powerful variant of the Particle Swarm optimization (PSO) algorithm, called CLPSO. Simulation results indicate that the proposed algorithm performs image segmentation faster than both types of algorithm for optimal thresholds. Moreover in case of localization it gives more accurate results than the compared algorithms. So by using the proposed variant of Differential Evolution improvement has been achieved both in the case of speed and accuracy.