Pattern Search Method Research Articles

A general and accurate pattern search method for various scenarios

As chip designs grow in complexity, the optical proximity correction (OPC) process becomes increasingly time-consuming. As a result, pattern search technology is becoming a foundation stone of many tasks for manufacturing, such as lithography simulation, hotspot detection, mask optimization, and so on. The most difficult challenge of pattern search is to locate clips by specific patterns within the layout accurately and efficiently. In this paper, we present a generalized pattern search method capable in diverse scenarios, including patterns with hollow shapes, shifting edges, and multi-layer situations. Experimental results show that our method outperforms commercial tools in pattern locating accuracy and handling search problems involving complex patterns which is not directly support by commercial tools yet.

Parameters identification and contact interaction control of redundant robot based on dynamic model

Human-redundant robot contact interaction control based on joint current is conducted. This is a sensorless control method. The key issues involved are dynamic parameters identification and interaction control strategy of robot. Firstly, kinematics model and linear dynamic model of robot are established and dynamic parameter matrix to be identified is obtained. Pattern search method is adopted to optimize motion trajectory of robot. Dynamic parameters is obtained by using least square method. The results indicate that the calculated current has a consistent trend with actual measured value, and the current error is small. In order to achieve independent control in null space, motion decoupling model of robot is established, and impedance control is applied to null space. When the force is applied to robot, based on impedance control and joint current changes, robot can move along the force direction, so the motion in null space can be controlled. Aiming at null space contact control during end force control task, comprehensive interaction control based on force sensor and joint current is conducted. The results show that robot can also move in compliance with human hand and achieve compliant interaction of null space when force is applied to the joint space.

Robust optimization of hypoid gear contact performance considering tooth form error: Design sensitivity and Pareto front

Optimization of hypoid gear contact performance is challenging due to its nonconvex nature and the handling of non-linear constraints involving complex gear contact behavior. In addition, the contact performance in the existing optimization schemes is highly reliant on the manufacturing quality, for example, the uncertain tooth form errors induced by machining tolerances and blade wear are usually ignored. To tackle these problems, a robust optimization methodology is proposed for hypoid gear contact performance. A finite element/contact mechanics model is established, which considers the stochastic tooth form errors following a Gaussian distribution model. The non-linear constraints are incorporated to avoid edge contact under loaded conditions, and tooth surface interference due to error. This methodology guarantees the feasible solution space and reduces the optimization complexity by the independent identification model. The identification model of the optimal solution is flexible in the parameter selection while keeping tooth depth. This non-deterministic problem is optimized based on the pattern search method. The Pareto front and design sensitivity analysis demonstrate the effectiveness and robustness of the proposed optimization methodology.

A sine cosine algorithm guided by elite pool strategy for global optimization

When handling global optimization problems by metaheuristic algorithms (MAs), an important yet difficult assignment is to keep a tradeoff between the swarm’s diversity and convergence. Hence, this paper develops an enhanced sine cosine algorithm called EPSCA to achieve the above target. In EPSCA, to balance diversity and convergence, the elite pool strategy and Brownian motion are introduced to modify the position updating formula of the original SCA. Then the pattern search method serves as a local search tool to reinforce the quality of the current best solution. Additionally, an efficient mutation operator is devised to discourage premature convergence. The comparative analysis between the developed EPSCA and other state-of-the-art techniques is conducted on 30 CEC2017 benchmarks. Besides, EPSCA is employed to identify the core parameters of photovoltaic (PV) systems. The experimental evidence based on 30 independent runs demonstrated that EPSCA can perform better than four state-of-the-art SCA variants and five other well-known methodologies. The source code of EPSCA is publicly available at https://github.com/denglingyun123/EPSCA.

Decentralised [formula omitted] robust control of MTMDs for mitigating vibration of a slender MDOF floor configuration

In this research, a novel robust decentralised H (H∞ and H2) design combined with a pattern search method (PSM), incorporating considerations of multiple modal dynamics, was developed to address the vibration issue in slender structures. To verify the effectiveness of the concept, a full-scale laboratory reconfigurable multiple degrees of freedom (MDOF) test-bed floor panel structure was selected, equipping with a passive multiple-tuned mass damper (MTMD) as a coupling system. The optimal parameters of the MTMDs were tailored using the decentralised H design and the PSM, considering the dynamic engagement of multiple modes. Furthermore, a crucifix-type configuration of 2DOF MTMDs was proposed to mitigate the vibration of the floor panel structure. Full-scale forced vibration testing (FVT) was conducted to validate the dynamic performance of the 2DOF MTMDs. The test results provide satisfactory verification and high-quality curve-fitting testing data for parameter study and dynamic simulation. By examining the field data from the 2DOF MTMDs and performing various numerical simulations and analyses, the control performance robustness of the MTMDs was comprehensively evaluated, considering practical engineering variations such as the number and stiffness of MTMDs, as well as the stiffness and mass of the floor panel structure. As a result, the proposed H control strategies for the MTMDs demonstrated robust control capabilities by effectively dissipating significant amounts of externally applied energy.

Optimizing the Location of Supports under a Monolithic Floor Slab

Monolithic reinforced concrete floor slabs are one of the most common types of building structures, and their optimization is an urgent task. The article presents the methodology for finding the optimal position of point supports under a reinforced concrete floor slab of arbitrary configuration at arbitrary load. The slab is considered thin, elastic and isotropic, with constant over-the-area stiffness, that is, the reinforcement is not taken into account or is constant. The solution is performed using the finite element method in combination with the nonlinear optimization methods. Finite element analysis is implemented by authors in MATLAB (R2024a) environment in such a way that the location of the columns may not coincide with the nodes of the finite element mesh of the slab. This allows to significantly increase the efficiency of solving the optimization problem compared to previously used algorithms, including the Monte Carlo method. Boundary conditions are taken into account using the Lagrange multiplier method. As an optimization criterion, the maximum deflection value is used, as well as the value of the potential strain energy. The effectiveness of six nonlinear optimization methods is compared in the example of a square slab under the action of a uniformly distributed load. For solutions obtained using the pattern search, simulated annealing and internal point methods, the maximum deflections are at least 1.2 times higher than for solutions obtained using the particle swarm method and genetic algorithm. An example of real object optimization is also presented. By changing the position of seven columns, it was possible to reduce the maximum deflection of the floor slab by 1.6 times.

Parameter optimization of vibration control system for adjacent building structures based on negative stiffness inerter damper

Building structures are subjected to strong earthquakes, which result in lateral collisions between them. Such collisions often cause severe structural damage and exacerbate the seismic hazard risk of building structures during earthquake events. This paper discusses the application of vibration control devices based on negative stiffness inerter damper in single-story adjacent building structures. The dynamic equations of the vibration control system containing different types of negative stiffness inerter damper under seismic excitation are established as a unified model. The H2 norm theory and Monte Carlo pattern search method are used to optimize the design parameters to improve the vibration control performance of the system, and the dynamic characteristics of the system are investigated. The results demonstrate that attaching negative stiffness inerter damper to adjacent building structures can effectively improve the overall seismic capacity reserve of the building and reduce the risk of collision of adjacent building structures; improve the robustness and stability of the system, and better reduce the displacement response of the building structure under seismic excitation. In addition, the potential of NSID-based vibration control devices to convert seismic energy into usable electricity has been investigated.

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

DETERMINATION OF CONCRETE RHEOLOGICAL PARAMETERS USING NONLINEAR OPTIMIZATION METHODS

The article proposes a method for processing concrete creep curves based on the nonlinear equation of V.M. Bondarenko. The experimental data of A.V. Yashin is used. The problem of finding rheological parameters and the nonlinearity function is posed as a nonlinear optimization problem. The objective function represents the sum of the squared deviations of the experimental values of the creep strain from the theoretical values for all creep curves for one concrete at different stress levels. The minimum of the objective function is found using the interior point method, the surrogate optimization method, the pattern search method, the genetic algorithm, and the particle swarm method. It has been established that the first of these methods has the greatest efficiency. The proposed approach provides high quality approximation of experimental curves at all stress levels. It is shown that for concrete the nonlinearity of creep deformations is more pronounced than the nonlinearity of instantaneous deformations, and the same function cannot be used to describe these two types of nonlinearity.

A Pattern Search Method to Optimize Mars Exploration Trajectories

The Korean National Space Council recently released “Mars Exploration 2045” as part of its future strategic plan. The operations for a Mars explorer can be defined based on domestically available capabilities, such as ground operations, launch, in-space transport and deep space link. Accordingly, all of our exploration scenarios start from the Naro space center, and the pathway to Mars is optimized using an objective function that minimizes the required ∆V. In addition, the entire phase of Mars orbit insertion should remain in contact with our deep space antennas, a measure that is imposed as an operational constraint. In this study, a pattern search method is adopted, as it can handle a nonlinear problem without relying on the derivatives of the objective function, and optimal trajectories are generated on a daily basis for a 15-day launch period. The robustness of this direct search method is confirmed by consistently converged solutions showing, in particular, that the ascending departure requires slightly less ∆V than the descending departure on the order of 10 m/s. Subsequently, mass estimates are made for a Mars orbiter and a kick stage to determine if the desired ∆V is achievable with our eco-friendly in-space propulsion system when launched from our indigenous launch vehicle, KSLV-II.

Variable Velocity Coating Thickness Distribution Model for Super-Large Planar Robot Spraying

This study proposed a dynamic optimization method for spray gun flow to address the issue of uneven film thickness in the acceleration and deceleration section of the z-shaped spray trajectory in large plane ship segments. The method takes into account the acceleration and deceleration section of the spray trajectory and improves the film thickness distribution. The study first established a static vertical spray coating growth model of the spray gun without considering the acceleration and deceleration of the spray trajectory. Then, we used an integral method to optimize the coating uniformity and established a film thickness distribution model. The optimization of spray speed and spray spacing is crucial in the use of variable flow spray guns. It is assumed that the acceleration and deceleration sections of the spraying trajectory are linear and symmetrical, which forms the basis for establishing a mathematical model for the variable flow spray gun. To determine the flow of the spray gun at different speeds in the acceleration and deceleration sections, the pattern search method was adopted. A section of a ship was used as an example for this study. In this study, we utilized numerical simulations to compare the uniformity of paint film thickness when using a spray gun with a constant flow versus variable flow. The results indicate that when taking into account the acceleration and deceleration section of the spray trajectory, the film thickness was more uniform with a variable flow. These findings demonstrate the feasibility and effectiveness of the proposed method.

Implementation of the Turbo Boyer Moore Method in Searching Thesis Titles at the Website-Based Faculty of Economics

Every year, students are required to complete a final project in the form of a thesis as one of the graduation requirements. However, in determining a thesis title that is relevant to the chosen scientific field, students often experience difficulties. The number of thesis titles available in libraries and websites can confuse students in choosing the right title. One method that can be used to achieve this goal is the Turbo Boyer-Moore pattern search method. Turbo Boyer-Moore is one of the efficient pattern matching algorithms to search for certain words or phrases in text. From the results of the tests carried out, it can be concluded that the implementation of the designed program in searching and detecting the same title with a similarity percentage.

Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

Real Time Classification and Detection of Apple Leaf Diseases at Early Stage by Employing Enhanced Paddy Field Pattern Search Method (EPF-PSM) with 2LC

Investigating the solution behavior of CO2 in ester based on a hybrid of ANN technology and PS method

ABSTRACT Artificial neural networks (ANNs) are artificial intelligence tools, and pattern search (PS) is a method for solving optimization problems. Although these two technologies have been extensively used in the chemical and engineering fields, they have limitations. This study proposes a methodology based on a hybrid of ANN technology and PS method to investigate the solution behavior of carbon dioxide (CO2). Five CO2-ester binary systems are selected as the model systems to demonstrate the point of interest. The results reveal that this methodology can overcome the defects of the two technologies and develop their advantages, thereby providing a satisfactory description of the solubility data of CO2 in these esters. The proposed methodology can be applied to a series of CO2 binary systems and is helpful in selecting a suitable absorbent for CO2 capture technology.

Multi-objective acoustic sensor placement optimization for crack detection of compressor blade based on reinforcement learning

Nowadays, acoustic sensors have been widely applied for structural health monitoring and crack detection of compressor blades. As the detection accuracy is mainly affected by signal quality, the optimal sensor placement (OSP) is significant for crack detection. To search the OSP for reliable signals, the multi-objective acoustic sensor placement optimization method is proposed based on information field fitting and optimization with reinforcement learning. First, the improved ridge and least squares support vector regression (R-LS-SVR) method is presented to fit information field of acoustic signal. Besides, the multi-objective function is constructed based on the compressor operation conditions, sound quality and crack damage observation. In addition, the Pareto front is obtained to realize multi-objective optimization (MOO) based on non-dominated sorting genetic algorithms (NSGA-II). Furtherly, the reinforcement learning-based pattern search method (RL-PSM) is proposed to quickly search the OSP of acoustic sensor. The compressor experiments are implemented to test the proposed method, and it can detect crack with the accuracy of 99.17%, which is superior to other locations. Comparing with other fitting and optimization methods, the advantage of the proposed method is validated for OSP optimization and crack detection.

The Alternating Direction Search Pattern Method for Solving Constrained Nonlinear Optimization Problems

We adopt the alternating direction search pattern method to solve the equality and inequality constrained nonlinear optimization problems. Firstly, a new augmented Lagrangian function with a nonlinear complementarity function is proposed to transform the original constrained problem into a new unconstrained problem. Under appropriate conditions, it has been proven that there is a 1-1 correspondence between the local and global optimal solutions of the new unconstrained problem and the original constrained problem. In this way, the optimal solution of the original problem can be obtained by solving the new unconstrained optimization problem. Furthermore, based on the characteristics of the new problem, the alternating direction pattern search method was designed and its convergence was demonstrated. Numerical experiments were implemented to illustrate the availability of the new augmented Lagrangian function and the algorithm.

Determination of the power smoothing effect in a photovoltaic-hydrokinetic system by experimental analysis and pattern search

Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5 % and 2.3 % with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32 %, this result represents an annual cost savings of 42 % on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method.

A Comparison of Faulty Antenna Detection Methodologies in Planar Array

Broadcasting, radar, sonar and space telecommunication systems use phased arrays to produce directed signals to be transmitted at the desired angle. This system requires a large number of antenna elements. The presence of faulty element(s) in an array causes asymmetry, which results in a deformed radiation pattern with higher sidelobe levels. Higher sidelobe levels indicate waste of energy by transmitting and receiving signals in unwanted directions. Hence, it is important to develop a method that detects faulty elements and corrects the radiation pattern. To correct the failed radiation pattern, failed elements in an array must be identified first. There have been various studies conducted on linear array failed radiation pattern correction and the finding of faulty elements, but investigation on the planar array is limited. Further, the optimization suggested for linear arrays does not necessarily work for the planar array. In this study, planar array faulty antenna detection was developed with pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO) methods by reducing the Signal to Noise Ratio (SNR) as the objective function. The analysis was varied for 8 × 8 and 6 × 6 planar arrays with different types of failures. The results were compared to find the best method to identify the faulty element’s location in a planar array. The pattern search method produced outstanding results in finding the faulty element’s locations by providing 100% accuracy for all types of failure, while other methods failed to do the same.

Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells.

Deformation prediction of functionally graded materials in laser directional energy deposition using forward-inverse calibration of the inherent strain in multi-scanning directions.

This paper presents the study of the residual deformation of functional gradient materials (FGMs) constructed by laser directed energy deposition (L-DED) and proposes a forward and reverse framework for inherent strain calibration, considering the effect of scan directions. First, the inherent strain and the corresponding residual deformation in the scanning strategies with the 0°, 45°, and 90° directions are calculated from the multi-scale model of the forward process, respectively. Then the inherent strain is calibrated inversely using the pattern search method based on the residual deformation of experiments by L-DED. The final inherent strain calibrated in the direction of 0° can be achieved through the rotation matrix and averaging. Finally, the final calibrated inherent strain is applied to the model of the rotational scanning strategy. The predicted trend of the residual deformation is highly consistent with the experiments in the verification stage. This work can provide a reference for the prediction of the residual deformation of FGMs.