In this article, we introduce an efficient global-minimum structural search program named Tsinghua Global Minimum 2 (TGMin-2), which is the successor of the original TGMin algorithm that was developed in our group in 2011. We have introduced a number of new features and improvements into TGMin-2, including a symmetric structure generation algorithm that can produce good initial seeds for small-and medium-size clusters, the duplicated structure identification algorithm, and the improved structure adaption algorithm that was implemented in the original TGMin code. To predict the simulated photoelectron spectrum (PE spectrum) automatically, we also implemented a standalone program named AutoPES (Auto Photoelectron Spectroscopy), which can be used to simulate PE spectra and compare them with experimental results automatically. We have demonstrated that TGMin-2 and AutoPES are powerful tools for studying free and surface-supported molecules, clusters, and nanoclusters.

Abstract-A low complexity user scheduling algorithm based on a novel adaptive Markov chain Monte Carlo (AMCMC) method is proposed to achieve the maximal sum capacity in an uplink multiple-input multiple-output (MIMO) multiuser system. Compared with the existing scheduling algorithms, our algorithm is not only more efficient but also converges to within 99% of the optimal capacity obtained by exhaustive search. We demonstrate the convergence of the proposed scheduling algorithm and study the tradeoff between its complexity and performance.Index Terms-Adaptive Markov chain Monte Carlo (AM-CMC), multiple-input multiple-output (MIMO), multiuser selection, scheduling, sum capacity.

No abstract

No abstract

Abstract. We propose a novel generalized algorithmic framework to utilize particle filter for optimization incorporated with the swarm move method in particle swarm optimization (PSO). In this way, the PSO update equation is treated as the system dynamic in the state space model, while the objective function in optimization problem is designed as the observation/measurement in the state space model. Particle filter method is then applied to track the dynamic movement of the particle swarm and therefore results in a novel stochastic optimization tool, where the ability of PSO in searching the optimal position can be embedded into the particle filter optimization method. Finally, simulation results show that the proposed novel approach has significant improvement in both convergence speed and final fitness in comparison with the PSO algorithm over a set of standard benchmark problems.