Adaptive hybrid annealing particle swarm optimization algorithm

To avoid premature convergence and improve its speed and accuracy of the particle swarm optimization (PSO) algorithm, an adaptive hybrid annealing PSO algorithm is proposed. A Sigmoid function is used to control the inertia weight to balance its global and local optimization capability. A hyperbolic tangent function is applied to control the acceleration coefficients to balance the self and social cognition capability of the proposed algorithm to improve its accuracy. A simulated annealing operator is used to ensure the capability of the proposed algorithm to jump out from the local optimal solution. At the last stage of the algorithm, a hybrid variation operator is used to increase its population diversity, hence further improving its accuracy. The performance of the proposed algorithm is verified based on three standard test functions and compared with typical PSO algorithms. The results show that the proposed algorithm has a great improvement in accuracy and convergence speed. Finally, the proposed algorithm is applied to array pattern synthesis, showing a better performance than existing algorithms. © 2022 Chinese Institute of Electronics. All rights reserved.