A probability-based coevolving multi-objective algorithm for antenna array synthesis

The synthesis of the antenna is a multi-objective problem which contains large-scale decision variables including but not limited to the elements' locations, amplitudes, and phases. Numerous researches have introduced heuristic algorithms on the synthesis optimization, and some of them employed multi-objective strategies which are proved to provide better optimal solutions for deciders. Moreover, since more complex relationships and a larger number of decision variables have appeared in nowadays array antennas, the study simultaneously considers high-dimensional decision space, as well as the multiple requirements is essential for highly directive patterns. However, no existing literature can be found in the corresponding field to the best of our knowledge. In this paper, a probability-based coevolving particle swarm optimization (PCMOPSO) is proposed for multi-objective optimization. In PCMOPSO, decision variables are allocated into subgroups based on the cooperative coevolution framework, and then optimized through the probability-based learning strategy to accelerate convergence simultaneously with maintaining diversity. Meanwhile, a grouping penalty (GP) technique is proposed to improve the grouping technique in PCMOPSO. The proposed algorithm has been tested for efficacy on several benchmark functions under different decision dimensions. The experimental results exhibit that PCMOPSO has superior performance relative to other similar methods. Furthermore, PCMOPSO is applied to optimize the array parameters under several antenna synthesis environments, including large array design cases. Significant improvement was obtained compared to other state-of-the-art multi-objective algorithms and known optimal solutions in the literature.