Global Optimization Algorithm Research Articles

Fault detection of RV reducer cycloidal gear under robot working conditions based on optimization improved envelope spectrum sideband energy ratio

Abstract Under industrial robot operating conditions, the cycloidal gear in the rotary vector(RV) reducer in the robot joint runs in a compound rotation motion with a low-speed and incomplete cycle, which complicates the fault detection for the cycloidal gear. Until now, no effective detection method for the cycloidal gear tooth fault under industrial robot operating conditions has been reported. Consequently, a method based on optimization improved envelope spectrum sideband energy ratio is proposed in this paper. Firstly, encoder signals are acquired and used to estimate the instantaneous angular speed (IAS) signals, which are then separated into two sets of data based on rotational directions. Then, the data corresponding to the approximately stable speed is truncated and connected to form a constructed signal. Next, the coherence of the constructed signal is calculated using the fast spectral coherence algorithm. Subsequently, the improved envelope spectrum sideband energy ratio is used as the objective value, and a global optimization algorithm is employed to determine the integration frequency band of the improved envelope spectrum (IES). Finally, the IES of this frequency band is calculated to obtain the fault-relatedorder characteristics. An RV reducer test rig was used for the experiment, and the analysis results from the proposed approach, cyclic spectral coherence (CSCoh), and improved envelope spectrum via feature optimization-gram (IESFOgram) were contrasted. Experiment results show that the proposed method is valid for the detection of tooth faults in the cycloidal gear of the RV reducer in robot operating conditions.

Self-learning salp swarm algorithm for global optimization and its application in multi-layer perceptron model training

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents’ abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA.

Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications

Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications

Rapid Assessment Method of Buckling Stability of Bridge Pier–Cap–Pile System Under Scour Effect

A novel analytical method for evaluating the buckling stability of the pier–cap–pile system under scour effect is proposed, and furthermore, the concept of rapid assessment of bridge buckling stability based on natural frequency is innovatively introduced in this paper. First, the total potential energy function of the bridge pier–cap–pile system is established, and the closed-form solution of structural system critical buckling load under scouring is solved according to the principle of stationary potential energy. Second, the sensitivity analyses of pier height, pier width, bending stiffness reduction in pier, pile slenderness ratio, pile bending stiffness reduction, m-value of soil and structural gravity to the buckling load are carried out. On this basis, 60 sets of sensitive parameter sample combinations are extracted by the Latin Hypercube Sampling algorithm, and a proxy model relating the critical buckling load to scour depth and the sensitive parameters are then developed using the McQuarter global optimization algorithm. Finally, the regression relationship model between the critical buckling load and the natural frequency is formed by combining the proxy model and the existing relationship between the scour depth and the first-order natural frequency. The feasibility of the proposed assessment method is demonstrated by the finite element method using a bridge case study, which provides an insight and a new means of quantitatively evaluating the safety of bridges under scour effect.

Optimal performance design of bat algorithm: An adaptive multi‐stage structure

AbstractThe bat algorithm (BA) is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness, which can be used to find the globally optimal solutions for various optimisation problems. Knowing the recent criticises of the originality of equations, the principle of BA is concise and easy to implement, and its mathematical structure can be seen as a hybrid particle swarm with simulated annealing. In this research, the authors focus on the performance optimisation of BA as a solver rather than discussing its originality issues. In terms of operation effect, BA has an acceptable convergence speed. However, due to the low proportion of time used to explore the search space, it is easy to converge prematurely and fall into the local optima. The authors propose an adaptive multi‐stage bat algorithm (AMSBA). By tuning the algorithm's focus at three different stages of the search process, AMSBA can achieve a better balance between exploration and exploitation and improve its exploration ability by enhancing its performance in escaping local optima as well as maintaining a certain convergence speed. Therefore, AMSBA can achieve solutions with better quality. A convergence analysis was conducted to demonstrate the global convergence of AMSBA. The authors also perform simulation experiments on 30 benchmark functions from IEEE CEC 2017 as the objective functions and compare AMSBA with some original and improved swarm‐based algorithms. The results verify the effectiveness and superiority of AMSBA. AMSBA is also compared with eight representative optimisation algorithms on 10 benchmark functions derived from IEEE CEC 2020, while this experiment is carried out on five different dimensions of the objective functions respectively. A balance and diversity analysis was performed on AMSBA to demonstrate its improvement over the original BA in terms of balance. AMSBA was also applied to the multi‐threshold image segmentation of Citrus Macular disease, which is a bacterial infection that causes lesions on citrus trees. The segmentation results were analysed by comparing each comparative algorithm's peak signal‐to‐noise ratio, structural similarity index and feature similarity index. The results show that the proposed BA‐based algorithm has apparent advantages, and it can effectively segment the disease spots from citrus leaves when the segmentation threshold is at a low level. Based on a comprehensive study, the authors think the proposed optimiser has mitigated the main drawbacks of the BA, and it can be utilised as an effective optimisation tool.

ROBUST DESIGN OPTIMIZATION OF A COMPRESSOR ROTOR USING RECURSIVE COKRIGING BASED MULTI-FIDELITY UNCERTAINTY QUANTIFICATION AND MULTI-FIDELITY OPTIMIZATION

Abstract This work focuses on the application of multi-fidelity methods for the robust design optimization of engine components. The robust design optimization approach yields geometric designs with high efficiency and low sensitivity to uncertainties from manufacturing and wear. The uncertainty quantification techniques required to evaluate the robustness are computationally expensive, which limits their use in robust optimization. Multi-fidelity methods offer a promising solution to reduce the computational cost while maintaining the accuracy in both uncertainty quantification and optimization. A Kriging and a multi-fidelity recursive Cokriging framework are developed and applied to a test function. Furthermore, a multi-fidelity super efficient global optimization algorithm is developed. The optimizer is surrogate based and handles constraints. The developed methods are then applied to a compressor test case of a high pressure compressor blade row with 9 uncertainty and 24 design parameters of the geometry. The 2.5 % quantile of the stage efficiency is used as a robustness measure and is optimized. Design bounds and performance constraints are applied. Various uncertainty quantification techniques are analyzed. A multi-fidelity uncertainty quantification approach is developed that combines simplified coarse-grid low-fidelity with high-fidelity results to reduce the computational cost while maintaining a high accuracy. Uncertainty quantification techniques of three fidelity levels are developed and used for the multi-fidelity approach in the design space. The robust design optimization is performed and the optimal designs obtained from the multi-fidelity approach show superior performance compared to existing robust design optima in the literature.

Phase synthesis method in multi-aperture optical systems based on iterative image processing algorithms

Background. Modern technologies in the field of optics and photonics place high demands on the quality and output power of the radiation source. The fulfillment of these requirements can currently be achieved by creating multi-aperture laser systems with coherent beam addition. The main problem of creating such systems is the development of phase synthesis methods in a multichannel optical system. Aim. In this paper, we consider a phase synchronization method without a reference beam for a coherent addition system with active feedback, which is based on the Gerchberg–Saxton algorithm. This algorithm makes it possible to reconstruct complex field amplitudes in the aperture and focal planes from intensity distributions in these fields. The application of this algorithm for multichannel systems is analyzed and its features, such as the occurrence of stagnation and ambiguity of the solution, are revealed. Methods. Solutions are proposed to eliminate the problem of convergence of the algorithm to side solutions and getting the iterative procedure into the local extremum using global optimization algorithms, methods of reducing the dimension of the problem and the introduction of antisymmetric amplitude modulation. Results. The paper demonstrates the results of phase field reconstruction for a large number of optical sources. For a seven-aperture system, a physical simulation was performed to restore phase information, confirming the results of numerical modeling. Conclusion. The proposed approach is a reasonable alternative to currently existing methods and can be used in the problem of coherent addition in multichannel optical systems.

Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models

Crystal plasticity models are powerful tools for predicting the deformation behaviour of polycrystalline materials accounting for underlying grain morphology and texture. These models typically have a large number of parameters, an understanding of which is required to effectively calibrate and apply the model. This study presents a structured framework for the global sensitivity analysis of the effect of crystal plasticity parameters on model outputs. Due to the computational cost of evaluating crystal plasticity models multiple times within a finite element framework, a Gaussian process regression surrogate was constructed and used to conduct the sensitivity analysis. Influential parameters from the sensitivity analysis were carried forward for calibration using both a local Nelder-Mead and global differential evolution optimisation algorithm. The results show that the surrogate based global sensitivity analysis is able to efficiently identify influential crystal plasticity parameters and parameter combinations. Comparison of the Nelder-Mead and differential evolution algorithms demonstrated that only the differential evolution algorithm was able to reliably find the global optimum due to the presence of local minima in the calibration objective function. However, the performance of the differential evolution algorithm was dependent on the optimisation hyperparameters selected.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

Eurasian lynx optimizer: a novel metaheuristic optimization algorithm for global optimization and engineering applications

Eurasian lynx optimizer: a novel metaheuristic optimization algorithm for global optimization and engineering applications

Near-Infrared Dual-Range Methane Sensor Using the OAIC-HC Mode Based on VMD-SG-Assisted Optical Noise Suppression.

Based on tunable diode laser absorption spectroscopy and off-axis integrated cavity output spectroscopy, a dual-range methane hybrid sensor was constructed utilizing the overtone absorption band of CH4 gas molecules at 1653.7 nm. By simultaneously utilizing an off-axis integrated cavity and Herriott cell with an effective absorption path of 11 and 405 m, respectively, the two received photoelectric signals are decomposed into different frequency components by VMD and then reconstructed after SG filtering. Applying the global optimization algorithm DA-ELM to CH4 concentration inversion, the correlation coefficient R2 is as high as 0.9995. Through long-term stability verification, the instrument's standard deviation at 1 ppm is 27 ppb. After Allan-Werle deviation analysis, the sensor's limit of detection is 2.298 ppb at an integration time of 138 s. Using the self-developed sensor, the detection of dual-range trace CH4 gas is achieved, enabling a dynamic detection range of trace CH4 gas ranging from 400 ppb to 1000 ppm. The sensor realizes dual-range methane trace detection and actively controls methane emissions to improve environmental quality while taking into account the safety benefits of reducing production accidents.

Structure and Bonding of Titanium Alanate

AbstractTitanium alanate Ti(AlH4)4 is a candidate material for high‐density hydrogen storage that thus far has been scarcely investigated. This study determines the low energy structures of Ti(AlH4)4 by adopting a global optimization algorithm combined with density functional theories, and systematically compares their stabilities, electronic structures, and bonding properties with Mg(AlH4)2 and Ca(AlH4)2. Compared to other compounds, Ti(AlH4)4 is less stable and the Ti−H bond is of covalent nature with strong polarities. The covalent Ti−H interaction can weaken the Al−H bonds. Because both the ionic and covalent interactions of the Al−H bonds are the weakest in Ti(AlH4)4, it is favorable for the desorption of hydrogen.

Multi-strategy arithmetic optimization algorithm for global optimization and uncertain motion tracking

Multi-strategy arithmetic optimization algorithm for global optimization and uncertain motion tracking

Centroid opposition-based backtracking search algorithm for global optimization and engineering problems

Evolutionary algorithms (EAs) have a lot of potential to handle nonlinear and non-convex objective functions. Particularly, the backtracking search algorithm (BSA) is a popular nature-based evolutionary optimization method that has attracted many researchers due to its simple structure and efficiency in problem-solving across diverse fields. However, like other optimization algorithms, BSA is also prone to reduced diversity, local optima, and inadequate intensification capabilities. To overcome the flaws and increase the performance of BSA, this research proposes a centroid opposition-based backtracking search algorithm (CoBSA) for global optimization and engineering design problems. In CoBSA, specific individuals simultaneously acquire current and historical population knowledge to preserve population variety and improve exploration capability. On the other hand, other individuals execute the position from the current population's centroid opposition to progress convergence speed and exploitation potential. In addition, an elite process based on logistic chaotic local search was developed to improve the superiority of the current individuals. The suggested CoBSA was validated on a set of benchmark functions and then employed in a set of application examples. According to extensive numerical results and assessments, CoBSA outperformed the other state-of-the-art methods in terms of accurateness, reliability, and execution capability.

Neuro-Musculoskeletal Modeling for Online Estimation of Continuous Wrist Movements from Motor Unit Activities.

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of 8 × 8 electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error (0.10 ± 0.02) and the highest determination coefficient (0.87 ± 0.06). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.

Optimization of the full-load combustion schemes of n-butanol/biodiesel reactivity-controlled compression ignition (RCCI) toward high-efficiency and low-emission engines

Biodiesel and n-butanol, as popular alternative fuels, can be used in advanced reactivity-controlled compression ignition (RCCI) engines for efficient and clean combustion. However, the complex interactions between n-butanol and biodiesel, as well as the best combustion schemes at different loads, remain poorly understood. Given this, the objective of this paper is to identify optimal fuel organizations for n-butanol/biodiesel RCCI engines across different load scenarios by combining engine simulation with a global optimization algorithm, explore the potential synergies of control parameters, and analyze microscopic dual-fuel interactions based on the integrated average reaction path analysis. Results show that the optimal scheme at low load employs almost homogeneous fuel-air mixtures at elevated temperature atmosphere above 390 K, with n-butanol energy share exceeding 96% and optimal biodiesel injection timing between −70 and −50 °CA. Higher n-butanol ratio coupled with increased intake temperature improves engine performance. For mid load, biodiesel injection timing is retarded to −55 to −30 °CA, and biodiesel proportion is increased to introduce reactivity stratification, while maintaining its energy share below 20% to mitigate NOx emissions. Notably, biodiesel density is identified as the primary physical property influencing NOx formation. At high load, a split combustion scheme involving premixing and subsequent diffusion combustion is optimal. The physical effects of n-butanol addition minimally impact the low-temperature ignition of biodiesel, while its chemical effects significantly defer the low-temperature reactivity. The reaction path analysis reveals that n-butanol competes with biodiesel for OH radical consumption, with a large proportion of OH and HO2 consumed in the cyclic reactions of nC4H9OH and nC4H8OH. Adjusting the reactivity stratification affects the reaction state of n-butanol entrained by the biodiesel jets. Higher reactivity gradient intensifies biodiesel reactions to induce the pyrolysis of the involved n-butanol through nC4H8OH =&gt; 2C2H4 + OH, therefore resulting in more staged heat release.

Determining the chemical ordering in nanoalloys by considering atomic coordination types.

The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys.

Adsorption mechanism of CO, CO2, NO, NO2, and SO2 gases onto AlNNT(m,n)_k, (m = 5, 7; n = 0, 5, 7; k = 3–9)

This study provides valuable insights into two key areas. First, we conduct a detailed examination of aluminum nitride nanotubes (AlNNT (m,n)_k), where m = 5,7; n = 0,5,7; and k = 3−9, focusing on their electronic properties across different lengths and diameters. We then investigate how these nanotubes interact with various gases, including CO, CO2, NO, NO2, and SO2. To determine the most energetically favorable configurations, we use the bee colony algorithm for global optimization. Our computational approach employs several Density Functional Theory (DFT) methods, such as PBE0, B3LYP(GD3BJ), CAM-B3LYP, HSE06, M06–2X, TPSSh, and ωB97XD functionals, in combination with the Def2svpp basis set. We prioritize the optimization of structural configurations and analyze the changes in energy band gaps within the nanotubes using conceptual DFT analysis. Additionally, we examine charge transfer mechanisms during gas/nanotube interactions through Natural Bond Orbital (NBO) analysis and assess the nature of these interactions with the Quantum Theory of Atoms in Molecules (QTAIM) framework. Our findings reveal that the interaction with SO2 tends to be slightly stronger compared to the other gases, as evidenced by various analytical techniques. This comprehensive analysis enhances our understanding of gas interactions with aluminum nitride nanotubes and provides insights into their practical applications.

An Efficient Optimization Model and Tabu Search–Based Global Optimization Approach for the Continuous p-Dispersion Problem

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a nonconvex multiply connected region that are rarely studied in the literature due to their intractability and the absence of an efficient optimization model. Using the penalty function approach, we design a unified and almost everywhere differentiable optimization model for these complex problems and propose a tabu search–based global optimization (TSGO) algorithm for solving them. Computational results over a variety of benchmark instances show that the proposed model works very well, allowing popular local optimization methods (e.g., the quasi-Newton methods and the conjugate gradient methods) to reach high-precision solutions due to the differentiability of the model. These results further demonstrate that the proposed TSGO algorithm is very efficient and significantly outperforms several popular global optimization algorithms in the literature, improving the best-known solutions for several existing instances in a short computational time. Experimental analyses are conducted to show the influence of several key ingredients of the algorithm on computational performance. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search &amp; Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72122006, 71821001, and 72471100] Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0089 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0089 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .