Modelling aluminium clusters with an empirical many-body potential

The article considers a novel approach to optimizing strip modal filters (MF) using the random search (RS) method. The optimization results for a 3-conductor MF with edge-side coupling and a 2-conductor MF with broad-side coupling were obtained. optimization was carried out according to the criterion of minimizing the maximum value of the output voltage. It was possible to obtain the maximum output voltage values that are comparable to optimization by evolutionary algorithms (149.1 mV for the MF with edge-side coupling and S6.9 mV for the MF with broad-side coupling). The obtained results were analyzed and revealed the distinctive features of the RS method and evolutionary algorithms for the MF optimization purposes.

The problem of reconstructing an irregularly sampled discrete-time band-limited signal with unknown sampling locations can be analyzed using both geometric and algebraic approaches. This problem can be solved using iterative and non-iterative techniques including the cyclic coordinate approach and the random search method. When the spectrum of the given signal is band-limited to L coefficients, the algebraic structure underlying the signal can be dealt using subspace techniques and a method is suggested to classify the solutions based on this approach. We numerically solve the Irregular Sampling at Unknown Locations (ISUL) problem by considering it as a combinatorial optimization problem. The exhaustive search method to determine the optimum solution is computationally intensive. The need for a more efficient optimization technique to save computational complexity leads us to propose Evolutionary Programming as a stochastic optimization technique. Evolutionary algorithms, based on the models of natural evolution were originally developed as a method to evolve finite-state machines for solving time series prediction tasks and were later extended to parameter optimization problems. The solution space is modeled as a population of individuals, and the search for the optimum solution is obtained by evolving to the best individual in the population. We propose an Evolutionary Programming (EP) based method to converge to the global optimum and obtain the set of sampling locations for the given irregularly sampled signal. The results obtained by EP are compared with the Random Search and Cyclic Coordinate descent algorithms.

We address adversarial attacks on the actuators at the joints of legged robots trained by deep reinforcement learning. The vulnerability to the joint attacks can significantly impact the safety and robustness of legged robots. In this study, we demonstrate that the adversarial perturbations to the torque control signals of the actuators can significantly reduce the rewards and cause walking instability in robots. To find the adversarial torque perturbations, we develop black-box adversarial attacks, where, the adversary cannot access the neural networks trained by deep reinforcement learning. The black box attack can be applied to legged robots regardless of the architecture and algorithms of deep reinforcement learning. We employ three search methods for the black-box adversarial attacks: random search, differential evolution, and numerical gradient descent methods. In experiments with the quadruped robot Ant-v2 and the bipedal robot Humanoid-v2, in OpenAI Gym environments, we find that differential evolution can efficiently find the strongest torque perturbations among the three methods. In addition, we realize that the quadruped robot Ant-v2 is vulnerable to the adversarial perturbations, whereas the bipedal robot Humanoid-v2 is robust to the perturbations. Consequently, the joint attacks can be used for proactive diagnosis of robot walking instability.

Experiments with new stochastic global optimization search techniques

This study explores the potential of the GPBoost approach for groundwater quality assessment in comparison to three other gradient boosting-based algorithms. Three methods, random search, grid search, and Bayesian optimization were used to find the optimal values of various hyperparameters with all four-gradient boosting-based algorithms. One hundred and two samples of Entropy weighted water quality index with 14 input parameters are used for assessing groundwater quality. The calculated EWQI values for drinking range between 80.4 and 394.96 in pre-monsoon and 39.6 to 338.79 during the post-monsoon period. Moreover, spatial distribution maps displayed that the central portions of the study area fall under medium water quality. The performances of models were compared based on multiple statistical criteria, including Correlation Coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). The results reveal that the CC value by all modeling approaches is more than 0.93, suggesting a comparable performance by all methods. Results in terms of RMSE values in predicting the EWQI values suggest GPBoost (random search) model performed better than the other three models, thus suggesting a competitive performance by GPBoost in comparison to other gradient boosting-based approaches. Relative importance analysis provided by random and grid search methods highlights the significance of NO3 −, Mg2+, TDS, EC, and TH as important input parameters for predicting EWQI.

This paper explores the optimal combination as a way to improve efficiency. Nonlinear optimization, random search, and greedy algorithm methods were used to find an optimal project combination of projects for government research institutes. According to our numerical experiments, the nonlinear programming method is not computationally efficient (i.e., requiring long computational time) to find an optimal combination. However, the greedy algorithm can produce multiple optimal project combinations in a short time window. Random search can also generates multiple optimal combinations, but it has disadvantage that computational time increases in the number of random generations. By using the proposed greedy method, multiple optimal combination can be quickly found in a situation where the number of R&D projects increases. Therefore, a research institute has a greater flexibility in selecting one among multiple optimal combinations.

Path planning field for autonomous mobile robot is an optimization problem that involves computing a collision-free path between initial location and goal location. In this paper, we present an improved artificial potential field based regression search (Improved APF-based RS) method which can obtain a global sub-optimal/optimal path efficiently without local minima and oscillations in complete known environment information. We redefine potential functions to eliminate non-reachable and local minima problems, and utilize virtual local target for robot to escape oscillations. Due to the planned path by improved APF is not the shortest/approximate shortest trajectory, we develop a regression search (RS) method to optimize the planned path. The optimization path is calculated by connecting the sequential points which produced by improved APF. Amount of simulations demonstrate that the improved APF method very easily escape from local minima and oscillatory movements. Moreover, the simulation results confirm that our proposed path planning approach could always calculate a more global optimal/near-optimal, collision-free and safety path to its destination compare with general APF. That proves our improved APF-based RS method very feasibility and efficiency to solve path planning which is a NP-hard problem for autonomous mobile robot.

Modern geodetic techniques provide valuable and near real‐time observations of volcanic activity. Characterizing the source of deformation based on these observations has become of major importance in related monitoring efforts. We investigate two random search approaches, simulated annealing (SA) and genetic algorithm (GA), and utilize them in an iterated manner. The iterated approach helps to prevent GA in general and SA in particular from getting trapped in local minima, and it also increases redundancy for exploring the search space. We apply a statistical competency test for estimating the confidence interval of the inversion source parameters, considering their internal interaction through the model, the effect of the model deficiency, and the observational error. Here, we present and test this new randomly iterated search and statistical competency (RISC) optimization method together with GA and SA for the modeling of data associated with volcanic deformations. Following synthetic and sensitivity tests, we apply the improved inversion techniques to two episodes of activity in the Campi Flegrei volcanic region in Italy, observed by the interferometric synthetic aperture radar technique. Inversion of these data allows derivation of deformation source parameters and their associated quality so that we can compare the two inversion methods. The RISC approach was found to be an efficient method in terms of computation time and search results and may be applied to other optimization problems in volcanic and tectonic environments.

Finite element model updating of structures usually ends up with a nonlinear optimization problem. An efficient optimization technique is proposed firstly, which draws together the global searching capability of chaos-based optimization technique and high searching efficiency of trust-region Newton method. This hybrid approach is demonstrated to be more efficient and prone to global minimum than conventional gradient search methods and random search methods by testifying with three test functions. The optimization problem for model updating using modal frequencies and modal shapes is formulated, and a procedure to update the boundary support parameters is presented. A modal test was conducted on a beam structure, and the identified mode frequencies are employed to formulate the optimization problem with the support parameters as the updating parameters. The discrepancy between the mode frequencies of the finite element models before and after updating is greatly reduced, and the updated support condition meet quite well with the insight to the devices that form the supports.

The Various methods of optimization or random search have been developed for locating the critical slip surface of a slope and the related minimum safety factor in the limit equilibrium stability analysis of slope. But all these methods are based on a two-dimensional (2D) method and no one had been adapted for a search of the three-dimensional (3D) critical slip surface. In this paper, a new Monte Carlo random simulating method has been proposed to identify the 3D critical slip surface, in which assuming the initial slip to be the lower part of an ellipsoid, the 3D critical slip surface in the 3D slope stability analysis is located by minimizing the 3D safety factor of limit equilibrium approach. Based on the column-based three-dimensional limit equilibrium slope stability analysis models, new Geographic Information Systems (GIS) grid-based 3D deterministic limit equilibrium models are developed to calculate the 3D safety factors. Several practical examples, of obtained minimum safety factor and its critical slip surface by a 2D optimization or random technique, are extended to 3D slope problems to locate the 3D critical slip surface and to compare with the 2D results. The results shows that, comparing with the 2D results, the resulting 3D critical slip surface has no apparent difference only from a cross section, but the associated 3D safety factor is definitely higher.

A GIS-based method for locating the critical 3D slip surface in a slope

The various methods of optimization or random search have been developed for locating the critical slip surface of a slope and the related minimum safety factor in the limit equilibrium stability analysis of slope. However, all these methods are based on a two-dimensional (2D) method and not one had been adapted for a search of the three-dimensional (3D) critical slip surface. In this paper, a new Monte Carlo random simulation method has been proposed to identify the 3D critical slip surface, in which assuming the initial slip to be the lower part of an ellipsoid, the 3D critical slip surface in the 3D slope stability analysis is located by minimizing the 3D safety factor of limit equilibrium approach. Based on the column-based 3D limit equilibrium slope stability analysis models, new geographic information systems (GIS) grid-based 3D deterministic limit equilibrium models are developed to calculate the 3D safety factors. Several practical examples, of obtained minimum safety factor and its critical slip surface by a 2D optimization or random technique, are extended to 3D slope problems to locate the 3D critical slip surface and to compare with the 2D results. The results shows that, comparing with the 2D results, the resulted 3D critical slip surface has no apparent difference only from a cross-section, but the associated 3D safety factor is definitely higher.

We explore how randomization can help asymptotic convergence properties of simple directional search-based optimization methods. Specifically, we develop a cheap, iterative randomized Hessian estimation scheme. We then apply this technique and analyse how it enhances a random directional search method. Then, we proceed to develop a conjugate-directional search method that incorporates estimated Hessian information without requiring the direct use of gradients.

We have developed an empirical many-body potential and used it to investigate the dynamical behaviours of fcc Ni-24%Fe alloy.The new many--body potential contains both two-and three-body atomic interactions.The two-body potential is expressed by the modified form of the generalized Morse potential and the three-body potential is developed based on the new two-body model potential.The parameters defining the many-body potential for Ni and Fe were computed following a procedure similar to the method defined by Akgun n and Ugur for fcc structure at the lattice constant of the alloy.The radial, tangential and three-body force constants of the alloy were calculated by the concentration averages of the computed force constants of the component metals.Finally, the phonon frequencies of the alloy along the principal symmetry directions were computed using the calculated two-and three-body force constants.The theoretical results were found to be in good agreement with the corresponding experimental values.

Analysis of load plays an important role in the operation of modern power systems due to its highly intermittent nature. This manuscript proposes the best approach by comparing results of Hold out, Cross and Re-Substitution validation from hyperparameter tuned Short Term Load Forecasting (STLF). Tree, Neural Network and GPR (Gaussian Process Regression) are three stochastic regression methods used. Each validation procedure is compared with every considered regression method, leading to 9 such combinations. Each combination is analysed with statistical parameters like RMSE (Root Mean Square Error), R Squared, MSE(Mean Square Error), MAE (Mean Absolute Error) and training time. The best approach is further optimised by modifying hyper parameters using Bayesian, Grid Search, and Random Search and most suitable method is proposed. The simulations are performed in Python and MATLAB platforms. The best combination for computation of STLF is K-fold validation with Tree Regression The statistical parameters obtained from the combination are RMSE, R Squared, MSE, MAE and training time of 0.077, 0.88, 0.0059, 0.046, 1.2 respectively. The best method for hyper-parameter tuning is found out to be Grid search with a reduced MSE of 0.0023.