This paper introduces a memory-based version of gravitational search algorithm (MBGSA) to improve the beamforming performance by preventing loss of optimal trajectory. The conventional gravitational search algorithm (GSA) is a memory-less heuristic optimization algorithm based on Newton's laws of gravitation. Therefore, the positions of agents only depend on the optimal solutions of previous iteration. In GSA, there is always a chance to lose optimal trajectory because of not utilizing the best solution from previous iterations of the optimization process. This drawback reduces the performance of GSA when dealing with complicated optimization problems. However, the MBGSA uses the overall best solution of the agents from previous iterations in the calculation of agents’ positions. Consequently, the agents try to improve their positions by always searching around overall best solutions. The performance of the MBGSA is evaluated by solving fourteen standard benchmark optimization problems and the results are compared with GSA and modified GSA (MGSA). It is also applied to adaptive beamforming problems to improve the weight vectors computed by Minimum Variance Distortionless Response (MVDR) algorithm as a real world optimization problem. The proposed algorithm demonstrates high performance of convergence compared to GSA and Particle Swarm Optimization (PSO).
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