Wireless sensor networks (WSNs) are composed of cooperating sensor nodes that can perceive thernenvironment to monitor physical phenomena and events of interest. Sensor deployment is arnfundamental issue in a WSNs to maximize coverage and quality of service with limited number ofrnsensor nodes. In order to maximize area coverage, sensors need to be placed in a position such thatrnthe sensing capability of the network reach at high quality. Coverage is one of the main problemsrnin WSNs deployment. Previous research works on sensor deployment mainly focused on TwornDimensional (2D) plane or in Three Dimensional (3D) volume coverage. But now, these studiesrnon sensor deployment extended to 3D surfaces or terrain, to achieve the highest overall sensingrnquality. In our thesis, we worked to develop an optimal WSNs deployment on 3D surfaces tornmaximize area coverage under constrained number of nodes.rnResearchers have used different methods and algorithms to make sensor deployment. Populationbasedrnoptimization algorithms find near-optimal solutions to the difficult optimization inspired byrnnatural probabilistic evolution. In our research work, Genetic Algorithm (GA) and Particle SwarmrnOptimization (PSO) are selected to form Hybrid Algorithm (HA) to find optimal locations ofrnsensors based on a fitness function. We have selected the two algorithms to exploit the best featuresrnof the algorithms in combination.rnWe have used two typical surfaces, rough and smooth, to compare the results of the GA, PSO andrnHA in the optimal deployment of sensors. The fitness function used in the algorithms is calculatedrnbased on coverage of all sensors in the region of interest (ROI). A simulating program for bothrnsurface types and all the three algorithms has been developed using MATLAB.rnIn all the three PSO, GA and HA evaluations, we found that the HA has exceeded PSO with arnpercentage of 26.12% and GA with 1.58% on rough surface. Similarly when we also comparedrnthe results of these algorithms on smooth surface, HA has exceeded PSO with a percentage ofrn22.24% and GA with 3.42%. Next to the HA, GA has a very good performance than PSO.rnKey words: Particle Swarm Optimization, Genetic Algorithm, WSNs, 3-D terrain,rnSensor Deployment.