Local Optimization Methods Research Articles

Acquiring elastic properties of thin composite structure from vibrational testing data

Abstract The paper is devoted to a problem of acquiring elastic properties of a composite material from the vibration testing data with a simplified experimental acquisition scheme. The specimen is considered to abide by the linear elasticity laws and subject to viscoelastic damping. The boundary value problem for transverse movement of such a specimen in the frequency domain is formulated and solved with finite-element method. The correction method is suggested for the finite element matrices to account for the mass of the accelerometer. The problem of acquiring the elastic parameters is then formulated as a nonlinear least-square optimization problem. The usage of the automatic differentiation technique for stable and efficient computation of the gradient and hessian allows to use well-studied first and second order local optimization methods. We also explore the possibility of generating initial guesses for local minimization by heuristic global methods. The results of the numerical experiments on simulated data are analyzed in order to provide insights for the following real life experiments.

Robot kinematics analysis and trajectory planning based on artificial potential field method

Abstract In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

Localization for Surface Microseismic Monitoring based on Local Equivalent Path and Virtual Field Optimization Method

Localization for Surface Microseismic Monitoring based on Local Equivalent Path and Virtual Field Optimization Method

Optical color routing enabled by deep learning.

Nano-color routing has emerged as an immensely popular and widely discussed subject in the realms of light field manipulation, image sensing, and the integration of deep learning. The conventional dye filters employed in commercial applications have long been hampered by several limitations, including subpar signal-to-noise ratio, restricted upper bounds on optical efficiency, and challenges associated with miniaturization. Nonetheless, the advent of bandpass-free color routing has opened up unprecedented avenues for achieving remarkable optical spectral efficiency and operation at sub-wavelength scales within the area of image sensing applications. This has brought about a paradigm shift, fundamentally transforming the field by offering a promising solution to surmount the constraints encountered with traditional dye filters. This review presents a comprehensive exploration of representative deep learning-driven nano-color routing structure designs, encompassing forward simulation algorithms, photonic neural networks, and various global and local topology optimization methods. A thorough comparison is drawn between the exceptional light-splitting capabilities exhibited by these methods and those of traditional design approaches. Additionally, the existing research on color routing is summarized, highlighting a promising direction for forthcoming development, delivering valuable insights to advance the field of color routing and serving as a powerful reference for future endeavors.

Improving modularity score of community detection using memetic algorithms

<abstract> <p>With the growth of online networks, understanding the intricate structure of communities has become vital. Traditional community detection algorithms, while effective to an extent, often fall short in complex systems. This study introduced a meta-heuristic approach for community detection that leveraged a memetic algorithm, combining genetic algorithms (GA) with the stochastic hill climbing (SHC) algorithm as a local optimization method to enhance modularity scores, which was a measure of the strength of community structure within a network. We conducted comprehensive experiments on five social network datasets (Zachary's Karate Club, Dolphin Social Network, Books About U.S. Politics, American College Football, and the Jazz Club Dataset). Also, we executed an ablation study based on modularity and convergence speed to determine the efficiency of local search. Our method outperformed other GA-based community detection methods, delivering higher maximum and average modularity scores, indicative of a superior detection of community structures. The effectiveness of local search was notable in its ability to accelerate convergence toward the global optimum. Our results not only demonstrated the algorithm's robustness across different network complexities but also underscored the significance of local search in achieving consistent and reliable modularity scores in community detection.</p> </abstract>

A Fast Evolutionary Knowledge Transfer Search for Multiscale Deep Neural Architecture.

The emergence of neural architecture search (NAS) algorithms has removed the constraints on manually designed neural network architectures, so that neural network development no longer requires extensive professional knowledge, trial and error. However, the extremely high computational cost limits the development of NAS algorithms. In this article, in order to reduce computational costs and to improve the efficiency and effectiveness of evolutionary NAS (ENAS) is investigated. In this article, we present a fast ENAS framework for multiscale convolutional networks based on evolutionary knowledge transfer search (EKTS). This framework is novel, in that it combines global optimization methods with local optimization methods for search, and searches a multiscale network architecture. In this article, evolutionary computation is used as a global optimization algorithm with high robustness and wide applicability for searching neural architectures. At the same time, for fast search, we combine knowledge transfer and local fast learning to improve the search speed. In addition, we explore a multiscale gray-box structure. This gray box structure combines the Bandelet transform with convolution to improve network approximation, learning, and generalization. Finally, we compare the architectures with more than 40 different neural architectures, and the results confirmed its effectiveness.

Development and validation of a local thermal non-equilibrium model for high-temperature thermal energy storage in packed beds

High-temperature thermal energy storage (TES) in packed beds is gaining interest for industrial energy recovery. The wide range of temperature distributions causes significant variations in thermophysical properties of the fluid and solid phases, leading to inaccuracies of classical TES models and heat transfer correlations. The objective of this work is to develop and validate a detailed but pragmatic model accounting for high-temperature effects. Based on a literature survey spanning over several communities interested in high-temperature porous media, we propose a generic local thermal non-equilibrium model for granulate porous media accounting for conservation of mass, momentum and energy (two-equation temperature model). The effective parameters needed to inform the model are the effective thermal conductivities of the different phases and the heat transfer coefficient. An experimental-numerical inverse analysis method is employed to determine these parameters. A dedicated experimental facility has been designed and built to study a model granulate made of glass bead of 16 mm diameter. Experiments are conducted using the Transient Single-Blow Technique (TSBT) by passing hot air (ranging from 293 K to 630 K) through cold particles at various mass flow rates, covering a Reynolds number range of 58 to 252. The new model was implemented in the Porous material Analysis Toolbox based on OpenFoam (PATO) used to compute the transient temperature fields. Two optimization algorithms were employed to determine the parameters by minimizing the error between experimental and simulated temperatures: a Latin Hypercube Sampling (LHS) method, and a local optimization method Adaptive nonlinear least-squares algorithm (NL2SOL). The results indicate that the value of heat transfer coefficient hv in the two-equation model falls in the range of 1.0 × 104∼1.93× 104 W/(m3 K) under the given conditions. The axial dispersion gas thermal conductivity was found to be around 5.9 and 67.1 times higher than the gas thermal conductivity at Peclet numbers of around 55 and 165, respectively. Furthermore, two improved correlations of Nusselt number (Nu=2+1.54Re(T)0.6Pr(T)1/3) and of axial dispersion gas thermal conductivity (kdis,∥=0.00053Re(T)2.21Pr(T)⋅kg) are proposed and validated for a range of Reynolds number from 58 to 252. The overall approach is therefore validated for the model granulate of the study, opening new perspectives towards more precise design and monitoring of high-temperature TES systems.

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

AbstractMost studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

Optical Optimization of Tandem Solar Cells: A Systematic Review for Enhanced Power Conversion

Tandem solar cells (TSCs) perform a better adaptation of the incident photons in different-energy-level bandgap materials, and overcome the Shockley–Queisser limit, but they require advanced control over the management of light for optimum performance. Nanomaterials and nanostructures offer a vastly improved control over the management of light. Through different optimization techniques, researchers can gain valuable insights regarding the optimization of various parameters of nano-optical designs. Over the past years, the number of studies on this topic has been continuously increasing. The present study reviews various current state-of-the-art optical designs, and provides an overview of the optimization techniques and numerical modeling of TSCs. This paper collected and analyzed different studies published within the years 2015–2022, using systematic literature review techniques, such as specific protocol screening and a search strategy. Seven different optical designs were extracted, along with their advanced local and global optimization methods, which offer a solution to the optical limitations of TSCs.

A Multi-Objective Local Optimization Method for Imaging Optical Systems

Addressing the inherent limitations of conventional local optimization methodologies such as damping least squares (DLS) and adaptation algorithms, this study proposes a novel approach to multi-objective optimization for imaging optical systems. The proposed method entails the formulation of a multi-objective optimization mathematical framework, where the objectives are established upon lateral aberration and wave aberration criteria. Subsequently, enhancements are made to the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) by implementing a directional initial population strategy and parallel optimization with multiple-trajectory planning. The genesis of the initial population is rooted in the gradient direction information extracted from the starting positions. This strategic foundation precludes potential efficiency limitations in subsequent optimization stages arising from undesirable initial population quality. By employing mechanisms such as differentiation and mutation to sustain population diversity, the trajectory of evolution is guided by the first- and second-order derivatives of the optimal individual, thereby elevating the quality of the evolutionary offspring population. The fusion of the parent and offspring populations yields a composite population, which undergoes rapid and non-dominated sorting, crowding calculation, and elite strategies. Empirical results illustrate the validation of the proposed methodology. The proof-of-concept paradigm demonstrates high efficiency in multi-objective local optimization for imaging optical systems.

Design Optimization and Analysis of Radial Flux Permanent Magnet Generator Using Local Optimization Method

Recently, Permanent Magnet Generator has been widely used due to its ability of having simple structure yet high efficiency. To maximise the output performance of the generator, advances and trends in mathematical modelling and computer simulation with using optimization techniques have opened the way to a new approach for electrical machine design. In this paper, a Radial Flux Permanent Magnet Generator configuration was examined and optimised throughout sizes and weight using Local Optimization Method as an iterative process in order to achieves optimal output. By utilizing LOM technique as optimization process, the output voltage of RFPMG increase by 13.64% from the initial results. With a corresponding increase in output voltage performances, the configurations geometry adjustment additionally helps in expanding the generator operational area.

Global optimization and structural analysis of Coulomb and logarithmic potentials on the unit sphere using a population-based heuristic approach

Global optimization of high-dimensional non-convex functions arises in many important applications and researches. In this paper, we focus on the minimum energy configuration problem on the unit sphere, whose goal is to determine the minimum-energy configuration of N particles interacting via the standard Coulomb potential or the discrete logarithmic potential on the unit sphere. This is a classic global optimization problem with many important applications in mathematics, physics, biology and chemistry, and is one of the 18 great mathematical problems for the twenty-first century listed by the Fields Medal winner (Smale, 1998). To determine the minimum-energy configuration for these two potential functions, we propose a population-based global optimization algorithm that combines a two-phase local optimization method with early-stopping strategy, a random perturbation operator, a distance-based population update strategy using a novel distance function to measure the difference between solutions, and a population rebuilding method. Computational results show that the proposed algorithm is very competitive compared to the best known results in the literature. In particular, it improves the best-known result for one instance of the classic Thomson problem (i.e., N=360) widely studied in the literature. The configurations of instances with up to N=500 particles are systematically optimized for the first time by the proposed algorithm for the discrete logarithmic potential. The comparative study shows that the putative optimal configurations have a high similarity for the Coulomb and logarithmic potentials and that the defects play an important role in reducing the energy of configuration. The energies of particles on the putative optimal configurations are analyzed for the studied logarithmic potential.

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar, which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

Inner product preconditioned trust-region methods for frequency-domain full waveform inversion

Full waveform inversion is a seismic imaging method which requires solving a large-scale minimization problem, typically through local optimization techniques. Most local optimization methods can basically be built up from two choices: the update direction and the strategy to control its length. In the context of full waveform inversion, this strategy is very often a line search. We here propose to use instead a trust-region method, in combination with non-standard inner products which act as preconditioners. More specifically, a line search and several trust-region variants of the steepest descent, the limited memory BFGS algorithm and the inexact Newton method are presented and compared. A strong emphasis is given to the inner product choice. For example, its link with preconditioning the update direction and its implication in the trust-region constraint are highlighted. A first numerical test is performed on a 2D synthetic model then a second configuration, containing two close reflectors, is studied. The latter configuration is known to be challenging because of multiple reflections. Based on these two case studies, the importance of an appropriate inner product choice is highlighted and the best trust-region method is selected and compared to the line search method. In particular we were able to demonstrate that using an appropriate inner product greatly improves the convergence of all the presented methods and that inexact Newton methods should be combined with trust-region methods to increase their convergence speed.

Research on microseismic localization algorithm with global search and local optimization

A microseismic localization algorithm that combines global search and local optimization is proposed. The Fewer Conditions Trigger Difference (FCTD) objective function of global search and local optimization is constructed, the execution process of the algorithm is described by numerical simulation, and the global search and local optimization microseismic localization algorithm is verified and applied by field data analysis. The results show that: (1) the global search and local optimization methods have fast search speed in the global range, high convergence accuracy and stable localization results in the local range, and high localization accuracy and stability without relying on the velocity model and initial values in the process of search. (2) By comparing the localization results of different localization methods, the global search and local optimization algorithms have better localization results.

A variable gradient descent shape optimization method for transition tee resistance reduction

In this paper, a local component optimization method is proposed for reducing resistance and consumption in building transmission and distribution systems. The gradient descent method is used to investigate energy dissipation minimization constrained by the Navier‒Stokes equation with nonlinear boundary conditions, and the optimality conditions of the shape are derived from the shape optimization problem using the function space parameterization method. The shape of the upper wall of the transition tee is iteratively optimized until the optimal shape with the minimum energy dissipation is obtained. The derivative of the free boundary shape of the transition tee and the objective function are proposed, and the normalized wall height of the transition tee with different area ratios are provided; these results can enable the adaptation of various sizes of transition tees in engineering to achieve air supply conditions that meet the engineering requirements. In this paper, the flow field and pressure loss of the optimized and conventional tees are compared, and the optimized tee performance improvement and energy saving potential are evaluated. The optimized transition tee has a resistance reduction rate of 6∼99% for the straight duct and 2–98% for the branch duct under different area ratios and flow ratios. Our method has the advantages of being more accurate, flexible and general and provides a novel method for the resistance optimization design of building transmission and distribution systems.

Anisotropic and Coherent Control of Radical Pairs by Optimized RF Fields.

Radical pair kinetics is determined by the coherent and incoherent spin dynamics of spin pair and spin-selective chemical reactions. In a previous paper, reaction control and nuclear spin state selection by designed radiofrequency (RF) magnetic resonance was proposed. Here, we present two novel types of reaction control calculated by the local optimization method. One is anisotropic reaction control and the other is coherent path control. In both cases, the weighting parameters for the target states play an important role in the optimizing of the RF field. In the anisotropic control of radical pairs, the weighting parameters play an important role in the selection of the sub-ensemble. In coherent control, one can set the parameters for the intermediate states, and it is possible to specify the path to reach a final state by adjusting the weighting parameters. The global optimization of the weighting parameters for coherent control has been studied. These manifest calculations show the possibility of controlling the chemical reactions of radical pair intermediates in different ways.

Optimization of the Hardware Layer for IoT Systems Using a Trust-Region Method With Adaptive Forward Finite Differences

Trust-region (TR) algorithms represent a popular class of local optimization methods. Owing to straightforward setup and low computational cost, TR routines based on linear models determined using forward finite differences (FD) are often utilized for performance tuning of microwave and antenna components incorporated within the Internet of Things systems. Despite usefulness for design of complex structures, performance of TR methods vastly depends on the quality of FD-based local models. The latter are normally identified from perturbations determined a priori using either rules-of-thumb, or as a result of manual tuning. In this work, a framework for automatic determination of FD steps and their adjustment between the TR algorithm iterations is proposed. The method involves numerical optimization of perturbations so as to equalize the objective function changes w.r.t. the center design to the desirable precision. To maintain acceptable cost, the FD-tuning procedure is executed using the same approximation model as the one exploited in the course of the structure optimization. The proposed framework has been tested on a total of twelve design problems. Furthermore, the presented method has been thoroughly validated against TR-algorithms with static, a priori selected perturbations. Numerical results indicate that the proposed framework provides up to 50% performance improvement (in terms of the optimized designs quality) compared to the state-of-the-art TR-based approaches. Usefulness of the proposed method for the real-world Internet of Things systems has been implicitly demonstrated through utilization of one of the optimized structures in a hardware layer of a real-time localization system.

Identifying and updating local optimization methods in extended Kalman filter SLAM

he essence of simultaneous location and mapping (SLAM) is an estimation problem. It requires mobile robots to use the sensor information to estimate the structure of the external environment in real-time and their position in the background. Smith and Cheeseman first applied the EKF estimation method to slam in the last century. The SLAM algorithm based on an extended Kalman filter (EKF-SLAM for short) is one of the research hotspots of slam technology. The EKF is improved based on the Kalman filter to make the Kalman filter suitable for nonlinear dynamic systems to solve the estimation problem of the nonlinear system model. Therefore, the Kalman filter can be applied to slam technology. The slam model based on filtering mainly includes three equations: Firstly, is to use the equation of motion, which will increase the uncertainty of robot positioning. Then the equation for initializing the guidepost according to the observation initializes the new state quantity according to the observation information. Lastly is the projection equation of the landmark state to comment, update and correct the condition according to the observation information to reduce the uncertainty. In the implementation, there exists a problem regarding storing the obstacle location. The current standard practice is to abandon all the existing data when the location number reaches the threshold. This will cause calculation time waste and instant high error [1]. In this paper, several improvement methods try to fully utilize the received message by different algorithms built on the ROS system. These include abandoning local data logic, updating data instead of deleting data, and retaining user data when updating new data. These advanced methods designed for EKF-SLAM will provide a more precise outcome with a low error range.

Local optimized Stokes polarimetry for specific polarization states.

In this Letter, we propose a locally optimized Stokes polarimetry. Focusing on the effect on polarization measurements by Poisson noise, the studies establish a new, to the best of our knowledge, optimization function combining the equally weighted variance with the condition number. This method considers both the stability and the precision of polarization measurements; by trading an increase in the condition number by 2.48%, we realize a decrease in equal-weighted variance by 19.1% near the north pole. The advantages of this local optimization method are demonstrated based on Monte Carlo (MC) simulations and experiments of continuous polarization state modulation. Finally, an imaging demonstration using a 4 µm pathological section implies the potential of this new local optimization method in improving polarization measurements and applying it to more biomedical research.