Publication: A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
Date
2016
Authors
Darzi S.
Sieh Kiong T.
Tariqul Islam M.
Rezai Soleymanpour H.
Kibria S.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
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). � 2016 Elsevier B.V.
Description
Algorithms; Artificial intelligence; Beamforming; Benchmarking; Heuristic algorithms; Iterative methods; Learning algorithms; Particle swarm optimization (PSO); Adaptive Beamforming; Gravitational search algorithm (GSA); Gravitational search algorithms; Heuristic optimization algorithms; Minimum variance distortionless response; Optimal trajectories; Optimization problems; Real-world optimization; Optimization