PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

• We propose a new algorithm to lower the sidelobes in collaborative beamforming.
• We achieved up to 100% improvement in sidelobe reduction.
• Variable parameter tuning is simplified in the proposed algorithm.
• The proposed algorithm successfully avoids the problem of premature convergence.
• The proposed method does not increase the computational complexity of the system.

A conventional collaborative beamforming (CB) system suffers from high sidelobes due to the random positioning of the nodes. This paper introduces a hybrid metaheuristic optimization algorithm called the Particle Swarm Optimization and Gravitational Search Algorithm-Explore (PSOGSA-E) to suppress the peak sidelobe level (PSL) in CB, by the means of finding the best weight for each node. The proposed algorithm combines the local search ability of the gravitational search algorithm (GSA) with the social thinking skills of the legacy particle swarm optimization (PSO) and allows exploration to avoid premature convergence. The proposed algorithm also simplifies the cost of variable parameter tuning compared to the legacy optimization algorithms. Simulations show that the proposed PSOGSA-E outperforms the conventional, the legacy PSO, GSA and PSOGSA optimized collaborative beamformer by obtaining better results faster, producing up to 100% improvement in PSL reduction when the disk size is small.

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