Beampattern Optimization of Collaborative Beamforming in Wireless Sensor Network Using Evolutionary Algorithms: A Comparison

The beampattern of the collaborative beamforming (CB) in wireless sensor network (WSN) suffers from high maximum sidelobe level (SLL) due to the distribution of sensor nodes in random manner. The high SLL will cause high interference which is unreliable for wireless communication. Thus, this paper proposes a method of optimizing inter-element spacing of sensor nodes in the form of linear antenna array configuration based on Single and Multiple Objective Function with Constraint (SMOFC). The proposed method is optimized by evolutionary algorithm (EA) optimizers which are Imperialist Competitive Algorithm (ICA), Backtracking Search Algorithm (BSA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The beampattern is optimized in terms of peak SLL suppression, control first null beam width (FNBW) and null placement in unintended directions. The algorithms are evaluated in different cases and successfully produce desired beampattern. The results show that all algorithms managed to control FNBW size in five cases, while reducing peak SLL up to 40% (ICA), 33% (PSO), 30% (GA) and 25% (BSA).