Hopping Algorithm Research Articles

Nonadiabatic Dynamics in a Laser Field: Using Floquet Fewest Switches Surface Hopping To Calculate Electronic Populations for Slow Nuclear Velocities.

We investigate two well-known approaches for extending the fewest switches surface hopping (FSSH) algorithm to periodic time-dependent couplings. The first formalism acts as if the instantaneous adiabatic electronic states were standard adiabatic states, which just happen to evolve in time. The second formalism replaces the role of the usual adiabatic states by the time-independent adiabatic Floquet states. For a set of modified Tully model problems, the Floquet FSSH (F-FSSH) formalism gives a better estimate for both transmission and reflection probabilities than the instantaneous adiabatic FSSH (IA-FSSH) formalism, especially for slow nuclear velocities. More importantly, only F-FSSH predicts the correct final scattering momentum. Finally, in order to use Floquet theory accurately, we find that it is crucial to account for the interference between wavepackets on different Floquet states. Our results should be of interest to all those interested in laser-induced molecular dynamics.

An adjustable channel hopping algorithm for multi-radio cognitive radio networks

In cognitive radio networks, achieving rendezvous for two nodes (switching to the same channel at the same time) is a premise for them to communicate. Applying a channel hopping protocol is a common solution to achieve rendezvous. Most existing channel hopping protocols work in a single-radio environment and use available channels evenly without considering the time-varying channel conditions for different channels. Such channel hopping protocols may suffer from poor performance due to inefficient channel allocation. To efficiently solve the rendezvous problem, we propose the Adjustable Multi-radio Channel Hopping (AMCH) protocol in this paper. Instead of using available channels evenly, nodes running AMCH are able to adjust the usage ratio of each available channel. Simulation results verify that such a ratio adjustable channel hopping mechanism provides an efficient way to solve the rendezvous problem.

A Hybrid Approach Using Multistage Collaborative Calibration for Wireless Sensor Network Localization in 3D Environments

Accurate localization is critical in the internet of things (IOT), especially for wireless sensor networks (WSNs). Location estimation can be affected by factors such as node density, topological diversity, and sensor coverage. As such, we propose a hybrid approach using multistage collaborative calibration for wireless sensor network localization, specifically in 3D environments. This technique integrates a Modified version of Light Gradient Boosting Model (MLGB), which is based on a regression scheme, a cooperative methodology, and a fine calibration model for collaborative fusion. These techniques were combined with quadrilateral shrunk centroid (QSC) and distance vector hop algorithms, using a multi-communication radius and an improved frog-leaping algorithm (DVMFL). In the first step, MLGB was used to correct for inhomogeneous localization estimation errors and RSSI data sparsity. As a result, the model is capable of adapting to high topological diversity (i.e., C-shape, H-shape, S-shape, and O-shape).Successive steps further improved prediction accuracy by using a screening cooperative anchor node strategy to increase node density and enhance the QSC-DVMFL fusion framework for fine position estimation. The proposed methodology was assessed in a series of validation, comparing it to other techniques. The results demonstrated a clear effectiveness and adaptability across a variety of factors that typically affect WSN localization.

Efficient Model Points Selection in Insurance by Parallel Global Optimization Using Multi CPU and Multi GPU

In the insurance sector, Asset Liability Management refers to the joint management of the assets and liabilities of a company. The liabilities mainly consist of the policies portfolios of the insurance company, which usually contain a large amount of policies. In the article, the authors mainly develop a highly efficient automatic generation of model points portfolios to represent much larger real policies portfolios. The obtained model points portfolio must retain the market risk properties of the initial portfolio. For this purpose, the authors propose a risk measure that incorporates the uncertain evolution of interest rates to the portfolios of life insurance policies, following Ferri (Optimal model points portfolio in life, 2019, arXiv:1808.00866). This problem can be formulated as a minimization problem that has to be solved using global numerical optimization algorithms. The cost functional measures an appropriate distance between the original and the model point portfolios. In order to solve this problem in a reasonable computing time, sequential implementations become prohibitive, so that the authors speed up the computations by developing a high performance computing framework that uses hybrid architectures, which consist of multi CPUs together with accelerators (multi GPUs). Thus, in graphic processor units (GPUs) the evaluation of the cost function is parallelized, which requires a Monte Carlo method. For the optimization problem, the authors compare a metaheuristic stochastic differential evolution algorithm with a multi path variant of hybrid global optimization Basin Hopping algorithms, which combines Simulated Annealing with gradient local searchers (Ferreiro et al. in Appl Math Comput 356:282–298, 2019a). Both global optimizers are parallelized in a multi CPU together with a multi GPU setting.

What Electronic Structure Method Can Be Used in the Global Optimization of Nanoclusters?

The problem of obtaining the spatial structure of nanoclusters is known to be very difficult due to the large number of local minima associated with their potential energy surfaces (isomers). In global optimization approaches, such as basin hopping and genetic algorithms, the problem is normally tackled by first using a low-level and affordable method to evaluate the energy. Afterward, the putative global minimum (and often a few others) is refined with calculations using higher level methods and larger basis sets. There is no guarantee, however, that the structure obtained at the lower level method will be the global minimum at the refined one. In this work, we have performed benchmark coupled cluster calculations at the complete basis set limit for a large number of different isomers of representative clusters of third row elements. Such calculations are then employed to check the hypothesis that lower level methods can be used in the global optimization with reliable results. For this, we have developed a methodology that allows us to compare a large number of minima obtained at different calculation levels. The results indicate that, if the global optimization is capable of reaching not only the global minimum but also a reduced number of low lying structures, most of the tested density functional theory (DFT) functionals are good choices, with emphasis on TPSSh. Besides giving a more solid ground to this commonly used approach, this work helps guiding such global optimizations. The use of the MP2 method and several scaled variants is also assessed, from where it is concluded that the scaled variants yield better results than standard MP2 or DFT approaches, except for one system where a large number of van der Waals structures exist.

Spectrum Allocation With Guaranteed Rendezvous in Asynchronous Cognitive Radio Networks for Internet of Things

The massive usage of Internet-of-Things devices in various smart applications enables spectrum scarcity issues. In order to enhance the dynamic spectrum capability, cognitive radio network (CRN) is considered as a key technology to address the spectrum scarcity problem. However, the establishment of a common communication channel in CRN by considering the unlicensed heterogeneous devices in an asynchronous environment is a challenging problem. In this paper, a novel asymmetric asynchronous channel hopping mechanism is designed, where secondary users have different sets of available channels and can enter into the network without any global clock synchronization. The proposed algorithms can guarantee the rendezvous within a small interval of time with minimum inter rendezvous intervals. Simulation results show that the designed protocol outperforms over the existing channel hopping algorithms in terms of the degree of rendezvous, average time to rendezvous and throughput.

Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3-6, 10).

Particle Swarm Optimization (PSO), a population based technique for stochastic search in a multidimensional space, has so far been employed successfully for solving a variety of optimization problems including many multifaceted problems, where other popular methods like steepest descent, gradient descent, conjugate gradient, Newton method, etc. do not give satisfactory results. Herein, we propose a modified PSO algorithm for unbiased global minima search by integrating with density functional theory which turns out to be superior to the other evolutionary methods such as simulated annealing, basin hopping and genetic algorithm. The present PSO code combines evolutionary algorithm with a variational optimization technique through interfacing of PSO with the Gaussian software, where the latter is used for single point energy calculation in each iteration step of PSO. Pure carbon and carbon containing systems have been of great interest for several decades due to their important role in the evolution of life as well as wide applications in various research fields. Our study shows how arbitrary and randomly generated small Cn clusters (n = 3–6, 10) can be transformed into the corresponding global minimum structure. The detailed results signify that the proposed technique is quite promising in finding the best global solution for small population size clusters.

A Connectivity Weighting DV_Hop Localization Algorithm Using Modified Artificial Bee Colony Optimization

Node localization is a fundamental issue in wireless sensor network (WSN), as many applications depend on the precise location of the sensor nodes (SNs). Among all localization algorithms, DV_Hop is a typical range-free localization algorithm characterized by such advantages as simple realization and low energy cost. From detailed analysis of localization error for the basic DV_Hop algorithm, we propose a connectivity weighting DV_Hop localization algorithm using modified artificial bee colony optimization. Firstly, the proposed algorithm calculates the average hop distance (AHD) of anchor nodes in terms of the minimum mean squared distance error between the estimated distances of anchor nodes and the corresponding actual distances from them. After that, a connectivity weighting method, considering the influence from both local network properties of anchor nodes and the distances from anchor nodes to unknown nodes, is designed to obtain the AHD of unknown nodes. In addition, we set up the weighting calculation proportion of anchor nodes at the same time. Finally, a modified artificial bee colony algorithm which enlarges searching space is used to optimize the execution of multilateral localization. The experimental results demonstrate that the connectivity weighting approach has better localization effect, and the AHD of unknown nodes close to true value can be obtained at a relatively large probability. Moreover, the modified artificial bee colony algorithm can reduce the probability of premature convergence, and thus the localization accuracy is further improved.

Revisiting the Recoherence Problem in the Fewest Switches Surface Hopping Algorithm.

We isolate and dissect exactly how and why Tully's fewest switches surface hopping (FSSH) algorithm fails when two wave packets come together at a crossing and "recohere". Using two different one-dimensional avoided crossing models and an initial wave function, which is a superposition of wave packets on different adiabats, we show that the key failures pertain to asymptotic nuclear momenta rather than electronic populations. Moreover, these FSSH failures stem from the fundamental assumption of independent trajectories with time-local hopping. As such, there is no possible means to correct FSSH without either (i) introducing time-nonlocal dynamics (i.e., allowing trajectories to move forward and backward in time) or (ii) requiring that trajectories interact.

Assessing the performance of trajectory surface hopping methods: Ultrafast internal conversion in pyrazine.

Trajectory surface hopping (TSH) methods have been widely used to study photoinduced nonadiabatic processes. In the present study, nonadiabatic dynamics simulations with the widely used Tully's fewest switches surface hopping (FSSH) algorithm and a Landau-Zener-type TSH (LZSH) algorithm have been performed for the internal conversion dynamics of pyrazine. The accuracy of the two TSH algorithms has been critically evaluated by a direct comparison with exact quantum dynamics calculations for a model of pyrazine. The model comprises the three lowest excited electronic states (B3u(nπ*), A1u(nπ*), and B2u(ππ*)) and the nine most relevant vibrational degrees of freedom. Considering photoexcitation to the diabatic B2u(ππ*) state, we examined the time-dependent diabatic and adiabatic electronic population dynamics. It is found that the diabatic populations obtained with both TSH methods are in good agreement with the exact quantum results. Fast population oscillations between the B3u(nπ*) and A1u(nπ*) states, which reflect nonadiabatic electronic transitions driven by coherent dynamics in the normal mode Q8a, are qualitatively reproduced by both TSH methods. In addition to the model study, the TSH methods have been interfaced with the second-order algebraic diagrammatic construction ab initio electronic-structure method to perform full-dimensional on-the-fly nonadiabatic dynamics simulations for pyrazine. It is found that the electronic population dynamics obtained with the LZSH method is in excellent agreement with that obtained by the FSSH method using a local diabatization algorithm. Moreover, the electronic populations of the full-dimensional on-the-fly calculations are in excellent agreement with the populations of the three-state nine-mode model, which confirms that the internal conversion dynamics of pyrazine is accurately represented by this reduced-dimensional model on the time scale under consideration (200 fs). The original FSSH method, in which the electronic wave function is propagated in the adiabatic representation, yields less accurate results. The oscillations in the populations of the diabatic B3u(nπ*) and A1u(nπ*) states driven by the mode Q8a are also observed in the full-dimensional dynamics simulations.

An extension of the fewest switches surface hopping algorithm to complex Hamiltonians and photophysics in magnetic fields: Berry curvature and "magnetic" forces.

We present a preliminary extension of the fewest switches surface hopping (FSSH) algorithm to the case of complex Hamiltonians as appropriate for modeling the dynamics of photoexcited molecules in magnetic fields. We make ansätze for the direction of momentum rescaling, and we account for Berry's phase effects through "magnetic" forces as applicable in the adiabatic limit. Because Berry's phase is a nonlocal, topological characteristic of a set of entangled potential energy surfaces, we find that Tully's local FSSH algorithm can only partially capture the correct physics.

Delay Tolerance and Energy Saving in Wireless Sensor Networks with a Mobile Base Station

Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile devices. A mobile device roams sensing fields and collects data from sensors through a short transmission range. Multihop communication is used to improve data gathering by reducing the tour length of the mobile device. In this paper we study the trade-off between energy saving and data gathering latency in wireless sensor networks. In particular, we examine the balance between the relay hop count and the tour length of a mobile Base Station (BS). We propose two heuristic algorithms, Adjacent Tree-Bounded Hop Algorithm (AT-BHA) and Farthest Node First-Bounded Hop Algorithm (FNF-BHA), to reduce energy consumption of sensor nodes. The proposed algorithms select groups of Collection Trees (CTs) and a subset of Collection Location (CL) sensor nodes to buffer and forward data to the mobile BS when it arrives. Each CL node receives sensing data from its CT nodes within bounded hop count. Extensive experiments by simulation are conducted to evaluate the performance of the proposed algorithms against another heuristic. We demonstrate that the proposed algorithms outperform the existing work with the mean of the length of mobile BS tour.

Improved DV-Hop Localization Algorithm Based on Dynamic Anchor Node Set for Wireless Sensor Networks

Node localization is a key issue in wireless sensor networks (WSN) area, and distance vector hop (DV-Hop) algorithm is widely adopted in WSN localization. Existing DV-Hop based algorithms employ all anchor nodes to localize the unknown node. However there is a large error of the estimated distance from the unknown node to some anchor nodes, which also results in the final unknown node localization error large. To improve the localization accuracy, we propose an improved DV-Hop algorithm based on dynamic anchor node set (DANS IDV-Hop). Differently to the existing DV-Hop based algorithms which apply total anchor nodes, DANS IDV-Hop utilizes part of anchor nodes to participate in localization. Firstly, the selection of anchor nodes is abstracted into a combinatorial optimization problem. For selecting appropriate anchor nodes, a novel binary particle coding scheme and fitness function are designed. Subsequently, the binary particle swarm optimization (BPSO) algorithm is applied to construct the dynamic anchor node set (DANS), and the localization is carried out on the DANS. Finally, the continuous particle swarm optimization (PSO) algorithm is utilized to further optimize the unknown node coordinates. Simulation results show that DANS IDV-Hop has excellent localization accuracy than that of the original DV-Hop and other DV-Hop based improved algorithms.

Development of specific DHODH inhibitors for Plasmodium and Human species

Malaria still remains one of the challenging public health issue infecting about 300-500 millions of people. The most serious and fatal malarial infections are caused by Plasmodium falciparum which has developed resistance to commonly employed therapeutics. Hence the need to develop a novel anti-malarial drug targeting Dihydroorotate dehydrogenase (DHODH), an enzyme involved in parasite growth. DHODH is present in both humans and Plasmodium falciparum. Sequence analysis and structure comparison of DHODH of both Human and Plasmodium falciparum reveals variations among them, thereby providing a chance to design a specific inhibitor. Virtual screening of existing anti-malarial drugs acting on DHODH is performed from Pubchem and BindingDB databases. Pharmacophore mapping was done for the top 20 virtual screening compounds using hip hop algorithm. The compounds thus obtained from screening, are docked with both Human and Plasmodium DHODH. Potential anti-malarial lead compounds can be developed to treat resistant strains of Plasmodium falciparum.

A comparison of surface hopping approaches for capturing metal-molecule electron transfer: A broadened classical master equation versus independent electron surface hopping.

Within a generalized Anderson-Holstein model, we investigate electron transfer rates using two different surface hopping algorithms: a broadened classical master equation (BCME) and independent electron surface hopping (IESH). We find that for large enough bandwidth and density of one electron states, and in the presence of external friction, the IESH results converge to the BCME results for impurity-bath model systems, recovering both relaxation rates and equilibrium populations. Without external friction, however, the BCME and IESH results can strongly disagree, and preliminary evidence suggests that IESH does not always recover the correct equilibrium state. Finally, we also demonstrate that adding an electronic thermostat to IESH does help drive the metallic substrate to the correct equilibrium state, but this improvement can sometimes come at the cost of worse short time dynamics. Overall, our results should be of use for all computational chemists looking to model either gas phase scattering or electrochemical dynamics at a metal interface.

Density functional theory global optimization of chemical ordering in AgAu nanoalloys

The chemical ordering in AgAu nanoalloys is determined by global optimization searches at the DFT level. A simulation code based on the Basin Hopping algorithm is developed and applied to truncated octahedral nanoalloys of 38 atoms and to icosahedral nanoalloys of 55 atoms. The optimization results show a different behaviour of these clusters for what concerns the optimal chemical ordering on the Ag-rich side. In the truncated octahedron, up to two Au atoms are accommodated in the cluster core, while in the icosahedron, all atoms are placed on the surface. When increasing Au content, string-like Au patterns form on the cluster surface. The different behaviours of the icosahedron and of the truncated octahedron are rationalized in terms of the electronic properties of these nanoalloys, which demonstrate that a subtle interplay of charge transfer and stress effects is taking place.

Nature Inspired Algorithm-Based Improved Variants of DV-Hop Algorithm for Randomly Deployed 2D and 3D Wireless Sensor Networks

Localization is a significant challenge in the area of wireless sensor networks (WSNs). Distance Vector Hop (DV-Hop) algorithm is most preferable algorithm due to its low cost, distributed nature, and its feasibility for all kinds of sensor networks, but it suffers from high localization error. In order to reduce the problem of high localization error for 2-dimensional and 3-dimensional WSNs, two Nature Inspired Algorithm based improved variants have been proposed. The first one uses Grey-Wolf optimization (GWO-DV-Hop) to identify a better estimate of average distance per hop and second one, a weighted Grey-Wolf optimization (Weighted GWO-DV-Hop), finds average distance per hop as computed by each beacon node using grey wolf algorithm and then, a weighted approach is applied by each node to get weighted average distance per hop (weights based on distance from each beacon) so as to consider impact of all types of beacons. The results prove the superiority of proposed algorithms over traditional DV-Hop in terms of localization error.

An efficient solution to the decoherence enhanced trivial crossing problem in surface hopping.

We provide an in-depth investigation of the time interval convergence when both trivial crossing and decoherence corrections are applied to Tully's fewest switches surface hopping (FSSH) algorithm. Using one force-based and one energy-based decoherence strategies as examples, we show decoherence corrections intrinsically enhance the trivial crossing problem. We propose a restricted decoherence (RD) strategy and incorporate it into the self-consistent (SC) fewest switches surface hopping algorithm [L. Wang and O. V. Prezhdo, J. Phys. Chem. Lett. 5, 713 (2014)]. The resulting SC-FSSH-RD approach is applied to general Hamiltonians with different electronic couplings and electron-phonon couplings to mimic charge transport in tens to hundreds of molecules. In all cases, SC-FSSH-RD allows us to use a large time interval of 0.1 fs for convergence and the simulation time is reduced by over one order of magnitude. Both the band and hopping mechanisms of charge transport have been captured perfectly. SC-FSSH-RD makes surface hops in the adiabatic representation and can be implemented in both diabatic and locally diabatic representations for wave function propagation. SC-FSSH-RD can potentially describe general nonadiabatic dynamics of electrons and excitons in organics and other materials.

A Diabatic Surface Hopping Algorithm Based on Time Dependent Perturbation Theory and Semiclassical Analysis

Surface hopping algorithms are popular tools to study dynamics of the quantum-classical mixed systems. In this paper, we propose a surface hopping algorithm in diabatic representations, based on time dependent perturbation theory and semiclassical analysis. The algorithm can be viewed as a Monte Carlo sampling algorithm on the semiclassical path space for piecewise deterministic path with stochastic jumps between the energy surfaces. The algorithm is validated numerically and it shows good performance in both weak coupling and avoided crossing regimes.

Frozen Gaussian approximation with surface hopping for mixed quantum-classical dynamics: A mathematical justification of fewest switches surface hopping algorithms

National Science Foundation [DMS-1312659, DMS-1454939, RNMS11-07444]; Alfred P. Sloan Foundation