Impact Statement:Optimization algorithms for optimal control of switched systems can be roughly divided into the following two categories: deterministic and heuristic methods. Determinist...Show More
Abstract:
In this article, a hybrid intelligent dynamic optimization method integrating of particle swarm optimization (PSO) and gradient-based optimization (GBO) is proposed for o...Show MoreMetadata
Impact Statement:
Optimization algorithms for optimal control of switched systems can be roughly divided into the following two categories: deterministic and heuristic methods. Deterministic methods can be easily trapped into local minima, and heuristic methods fail to guarantee that the obtained solution deterministically satisfies the optimality conditions. Thus, a hybrid intelligent dynamic optimization method is proposed for switched systems, which integrates the advantages of heuristic and deterministic optimization methods. Moreover, the numerical simulation results show that the proposed hybrid intelligent dynamic optimization method is significantly better than the deterministic and heuristic methods in terms of solution accuracy and computational time.
Abstract:
In this article, a hybrid intelligent dynamic optimization method integrating of particle swarm optimization (PSO) and gradient-based optimization (GBO) is proposed for optimal control of switched systems. The method both improves the solution accuracy of PSO and avoids falling into local minima generated by the deterministic optimization. First, the PSO algorithm with ring topology is used to explore the whole search space to detect the global optimum area. Second, the GBO algorithm is deployed in the detected global optimum area to achieve faster convergence rate and higher precision solution than those of pure PSO. Finally, the simulation results show that the algorithm outperforms both PSO and GBO in terms of solution accuracy and computational cost.
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 4, Issue: 6, December 2023)