Global Optimization Research Articles

In Situ Surfaced Mn-Mn Dimeric Sites Dictate CO Hydrogenation Activity and C2 Selectivity over MnRh Binary Catalysts.

Massive ethanol production has long been a dream of human society. Despite extensive research in past decades, only a few systems have the potential of industrialization: specifically, Mn-promoted Rh (MnRh) binary heterogeneous catalysts were shown to achieve up to 60% C2 oxygenates selectivity in converting syngas (CO/H2) to ethanol. However, the active site of the binary system has remained poorly characterized. Here, large-scale machine-learning global optimization is utilized to identify the most stable Mn phases on Rh metal surfaces under reaction conditions by exploring millions of likely structures. We demonstrate that Mn prefers the subsurface sites of Rh metal surfaces and is able to emerge onto the surface forming MnRh surface alloy once the oxidative O/OH adsorbates are present. Our machine-learning-based transition state exploration further helps to resolve automatedly the whole reaction network, including 74 elementary reactions on various MnRh surface sites, and reveals that the Mn-Mn dimeric site at the monatomic step edge is the true active site for C2 oxygenate formation. The turnover frequency of the C2 product on the Mn-Mn dimeric site at MnRh steps is at least 107 higher than that on pure Rh steps from our microkinetic simulations, with the selectivity to the C2 product being 52% at 523 K. Our results demonstrate the key catalytic role of Mn-Mn dimeric sites in allowing C-O bond cleavage and facilitating the hydrogenation of O-terminating C2 intermediates, and rule out Rh metal by itself as the active site for CO hydrogenation to C2 oxygenates.

Improving QoS Management Using Associative Memory and Event-Driven Transaction History

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm.

Multiple elite strategy enhanced RIME algorithm for 3D UAV path planning

With the wave of artificial intelligence sweeping the world in recent years, UAVs is widely used in various fields. UAV path planning has attracted much attention from scientists as an essential part of UAV work. In order to design an efficient and reasonable 3D UAV path planning program, recent researchers have invented and improved many algorithms. This paper proposes an elite RIME algorithm for 3D UAV path planning. First, we propose an elite reverse learning population selection strategy based on piecewise mapping to enhance the population diversity of the algorithm for better exploration. Second, this paper proposes a stochastic factor-controlled elite pool exploration strategy so that the algorithm is difficult to enter the local optimum and can better explore the global optimum. Then, this paper proposes a hard frost puncture exploitation strategy based on the sine–cosine function so that the algorithm can find the global optimum faster during the exploitation process. Meanwhile, in order to test the performance of the algorithm proposed in this paper, we compare it with 13 other intelligent optimization algorithms that are classical and popular nowadays on 52 test functions in three test sets, CEC2017, CEC2020, and CEC2022, and obtain competitive results. Finally, we applied it to the 3D UAV path planning problem in three different terrain scenarios, and the ELRIME algorithm achieved good results in all of them. Especially in the 7-peak model, the ELRIME algorithm improves the performance of the RIME algorithm by a factor of two. In the 9-peak model, the average value aspect also reduce the cost by 91 compared to the RIME algorithm, and more importantly, it has the smallest fluctuation in 30 runs, which is among the most stable of all the compared algorithms. In the 12-peak model, its stability is also significantly enhanced, and in terms of worst-case cost, it improves the cost by 340 compared to RIME.

Global optimization for large‐scale water network synthesis based on dynamic partition and adaptive bound tightening

AbstractThe synthesis of large‐scale integrated water networks is typically formulated as nonconvex mixed‐integer quadratic constrained programming (MIQCP) or QCP problems. With the complexity arising from bilinear terms in modeling mass flows of contaminants and binary variables representing the presence of units or streams, numerous local optima exist, thus presenting a significant optimization challenge. This study introduces a deterministic global optimization algorithm based on mixed‐integer programming (MIP) to tackle such problems. The approach involves dynamically strengthening the relaxed problems to converge towards the original problems. A simultaneous partition strategy is proposed combining locally uniform division with dynamic partitioned variables choosing. Furthermore, several adaptive bound contraction schemes are introduced to efficiently manage the size of the relaxed problems, assisting in accelerating the solution process. The algorithm's effectiveness and robustness are demonstrated with a large test set, showing superior performance compared to commercial solvers specifically on MIQCP problems.

Antenna optimization using machine learning with reduced-dimensionality surrogates

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM) simulation models using conventional nature-inspired methods entails significant computational costs. Alternatively, surrogate-based techniques show promise but are impeded by dimensionality-related challenges and nonlinearity of antenna outputs. This study introduces an innovative technique for swiftly optimizing antennas. It leverages a machine learning framework with an infill criterion employing predicted enhancement of the merit function, utilizing a particle swarm optimizer as the primary search engine, and employs kriging for constructing the underlying surrogate model. The surrogate model operates within a reduced-dimensionality domain, guided by directions corresponding to maximum antenna response variability identified through fast global sensitivity analysis, tailored explicitly for domain determination. Operating within this reduced domain enables building dependable metamodels at a significantly lower computational cost. To address accuracy loss resulting from dimensionality reduction, the global optimization phase is supplemented by local sensitivity-based parameter adjustment. Extensive comparative experiments involving various planar antennas demonstrate the competitive operation of the presented technique over machine learning algorithms operating in full-dimensionality space and direct EM-driven bio-inspired optimization techniques.

A surrogate-assisted expensive constrained multi-objective global optimization algorithm and application

Expensive multi-objective optimization problems (MOPs) have seen the successful applications of surrogate-assisted evolutionary algorithms (SAEAs). Nevertheless, the majority of SAEAs are developed for costly unconstrained optimization, and costly constrained MOPs (CMOPs) have received less attention. Therefore, this article proposes a surrogate-assisted global optimization algorithm (named CTEA) for solving CMOPs within a very limited number of fitness evaluations. The proposed algorithm combines two selection frameworks, a bi-level selection framework, and an adaptive sampling framework, to enhance optimization performance. Leveraging on a constraint-improving strategy and a Pareto-based three-indicator criterion (convergence, constraint, and diversity indicators) at the different levels, the proposed bi-level selection framework can select more promising solutions. Moreover, an adaptive sampling framework is developed to prioritize objective and constraint functions and select the candidate solutions for real function evaluations according to the priority. Experimental results demonstrate that the proposed CTEA exhibits superior performance when compared with five state-of-the-art algorithms, achieving the best results in 61.9 % out of the 64 test instances. Finally, CTEA is applied to the multidisciplinary design optimization of blended-wing-body underwater gliders, and an impressive solution set is obtained.

Cooperative energy optimal control involving optimization of longitudinal motion, powertrain, and air conditioning systems

This paper is concerned with the optimal control strategies for the longitudinal control, powertrain, and air conditioning (A/C) system of connected four-wheel hub-drive electric vehicles (EVs). A hierarchical control framework is developed to enhance the energy economy of the vehicle. Real-time connected information is utilized in the upper layer to determine the travel mode. Then, a multi-objective motion planning system (MOMPS) is proposed to plan the optimal acceleration trajectory. In the lower layer, an offline global optimization approach is employed to find the torque combinations that minimize the total power loss. The proposed A/C controller operates based on the bi-level model predictive control (Bi-level MPC) algorithm. A novel prediction model is developed to enable the A/C system to decrease energy consumption by leveraging the speed of the vehicle. The performance of the MOMPS is evaluated using urban test road data, demonstrating that the MOMPS can balance multiple objectives compared to global dynamic programing (Global DP) and the intelligent driver model (IDM). In addition, the proposed torque distribution strategy results in a 4.98% energy-savings rate through comparison with the even torque distribution strategy. Moreover, the A/C controller proposed in this paper can optimize energy consumption by 13.57% compared to a baseline strategy that maintains a constant setting.

A new improved convex underestimator for univariate functions in global optimization

ABSTRACT In this paper, we consider convex underestimators for univariate nonconvex functions over an interval in R . We propose a new convex underestimator that can be used for computing the lower bound of the range of nonconvex functions, or for solving global optimization problems. We show that the new underestimator is tighter than other underestimators which are developed in the literature.

Tighter convex underestimator for general twice differentiable function for global optimization

In this paper, we propose a new convex underestimator for a general C 2 nonconvex function. The new underestimator can be used in branch and bound algorithm for solving global optimization problems. We show that the new underestimator is tighter than the classical underestimator in the α BB method.

Hybrid Artificial Protozoa-Based JADE for Attack Detection

This paper presents a novel hybrid optimization algorithm that combines JADE Adaptive Differential Evolution with Artificial Protozoa Optimizer (APO) to solve complex optimization problems and detect attacks. The proposed Hybrid APO-JADE Algorithm leverages JADE’s adaptive exploration capabilities and APO’s intensive exploitation strategies, ensuring a robust search process that balances global and local optimization. Initially, the algorithm employs JADE’s mutation and crossover operations, guided by adaptive control parameters, to explore the search space and prevent premature convergence. As the optimization progresses, a dynamic transition to the APO mechanism is implemented, where Levy flights and adaptive change factors are utilized to refine the best solutions identified during the exploration phase. This integration of exploration and exploitation phases enhances the algorithm’s ability to converge to high-quality solutions efficiently. The performance of the APO-JADE was verified via experimental simulations and compared with state-of-the-art algorithms using the 2022 IEEE Congress on Evolutionary Computation benchmark (CEC) 2022 and 2021. Results indicate that APO-JADE achieved outperforming results compared with the other algorithms. Considering practicality, the proposed APO-JADE was used to solve a real-world application in attack detection and tested on DS2OS, UNSW-NB15, and ToNIoT datasets, demonstrating its robust performance.

An improve nonlinear robust control approach for robotic manipulators with PSO-based global optimization strategy

During the trajectory tracking of robotic manipulators, many factors including dead zones, saturation, and uncertain dynamics, greatly increase the modeling and control difficulty. Aiming for this issue, a nonlinear active disturbance rejection control (NADRC)-based control strategy is proposed for robotic manipulators. In this controller, an extended state observer is introduced on basis of the dynamic model, to observe the extend state of model uncertainties and external disturbances. Then, in combination with the nonlinear feedback control structure, the robust trajectory tracking of robotic manipulators is achieved. Furthermore, to optimize the key parameters of the controller, an improved particle swarm optimization algorithm (IPSO) is designed using chaos theory, which improves the tracking accuracy of the proposed NDRC strategy effectively. Finally, using comparative studies, the effectiveness of the proposed control strategy is demonstrated by comparing with several commonly used controllers.

Shrinkage cracking law and anti-crack inverse design in early-age concrete: A novel perspective on the development of crack resistance properties

Early-age cracking in massive concrete has long attracted research focus, essentially governed by the game between crack driving and resistance. However, cracking resistance aspect received inadequate attention. This study examines the impact of crack resistance properties development processes on shrinkage cracking law in early-age concrete, employing chemo-thermo-mechanical coupling phase-field model. Two quantitative evaluation indicators, namely the index of damage development and average growth rate of maximum damage, are introduced to characterize the process of damage and cracking. The findings demonstrate that the development processes of elastic modulus and tensile strength exert a considerable influence on shrinkage cracking process in early-age concrete, whereas the impact of fracture energy development process is insignificant. Multifactor analysis revealed substantial coupling effects among parameters. Additionally, an anti-crack reverse design method is proposed based on the global optimization analysis, which can guide the optimization design of shrinkage crack prevention and control in early-age concrete from both perspectives of crack driving and crack resistance.

Clustering methods: To optimize or to not optimize?

Many clustering problems are associated with a particular objective criterion that is sought to be optimized. There are often several methods that can be used to tackle the optimization problem, and one or more of them might guarantee a globally optimal solution. However, it is quite possible that, relative to one or more suboptimal solutions, a globally optimal solution might be less interpretable from the standpoint of psychological theory or be less in accordance with some known (i.e., true) cluster structure. For example, in simulation experiments, it has sometimes been observed that there is not a perfect correspondence between the optimized clustering criterion and recovery of the underlying known cluster structure. This can lead to the misconception that clustering methods with a tendency to produce suboptimal solutions might, in some instances, be preferable to superior methods that provide globally optimal (or at least better locally optimal) solutions. In this article, we present results from simulation studies in the context of K-median clustering where departure from global optimality was carefully controlled. Although the results showed that suboptimal solutions sometimes produced marginally better recovery for experimental cells where the known cluster structure was less well-defined, capriciously accepting inferior solutions is an unwise practice. However, there are instances in which some sacrifice in the optimization criterion value to meet certain desirable constraints or to improve the value of one or more other relevant criteria is principled. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field.

Machine-learning force fields have achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, this approach still faces several challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically informed neural network (PINN). This approach involves incorporating physical knowledge into the parameters of the neural network, coupled with an efficient global optimizer, the Tabu-Adam algorithm, proposed in this work to augment optimization under strict physical constraint. We choose the AMOEBA+ force field as the physics-based model for embedding and then train and test it using the diethylene glycol dimethyl ether (DEGDME) data set as a case study. The results reveal a breakthrough in constructing a precise and noise-robust machine learning force field. Utilizing two training sets with hundreds of samples, our model exhibits remarkable generalization and density functional theory (DFT) accuracy in describing molecular interactions and enables a precise prediction of the macroscopic properties such as the diffusion coefficient with minimal cost. This work provides valuable insight into establishing a fundamental framework of the PINN force field.

Resonant-mode metasurface thermal super mirror by deep learning-assisted optimization algorithms

A “super-mirror” having ultrahigh infrared reflectance is achieved by an optimized photonic contrast grating metasurface. Finding ways to achieve this exceptional performance can be enabled by implementing global optimization and machine learning elements, such as Bayesian optimization and genetic algorithm. Here, we acquired an optimized grating design made of high-index germanium, which excites resonances that result in ultralow emittance at certain wavelengths. Our optimizations assisted in the discovery of hybridized coupling of Fabry-Pérot modes and guided modes in a monolithic microscale multilayered coating. We demonstrate constraints in the given geometric variable ranges improves the overall performance of algorithms. We also show the enhanced performance of a deep learning Feedforward Neural Network, which is implemented as the inverse design using the network trained with dataset obtained from Bayesian optimization and Genetic Algorithm approaches. The performance of the Feedforward Neural Network-assisted design produced normal emissivity difference by only +3.5 %, with lower sensitivity to grating dimensional parameter variations. The improvement is achieved by predicting and better understanding of the optical physics of resonant gratings. The proposed few-layer grating coating can be applied to space components, enclosures, and vessels to suppress thermal radiative heat loss.

Automatic source code generation for deterministic global optimization with parallel architectures

Trends over the past two decades indicate that much of the performance gains of commercial optimization solvers is due to improvements in x86 hardware. To continue making progress, it is critical to consider alternative/specialized massively parallel computing architectures. In this work, we detail the development of an open-source source code transformation approach built using Symbolics.jl to construct McCormick-based relaxations of functions that enables their effective parallelized evaluation. We then apply this approach in a novel parallelized branch-and-bound routine that offloads lower- and upper-bounding problems to a GPU. The effectiveness of this new approach is demonstrated on three nonconvex problems of interest, where it yields convergence time improvements of 22–118x compared to an equivalent serial CPU implementation and in two cases outperforms vanilla branch-and-bound versions of existing state-of-the-art solvers that use tighter bounding techniques. This work exemplifies how deterministic global optimizers using alternative hardware architectures can compete with—or eventually outclass—even the most powerful serial CPU implementations, and to the best of the authors' knowledge, represents the first successful demonstration of deterministic global optimization using a GPU.

Automatic deep sparse clustering with a dynamic population-based evolutionary algorithm using reinforcement learning and transfer learning

Clustering data effectively remains a significant challenge in machine learning, particularly when the optimal number of clusters is unknown. Traditional deep clustering methods often struggle with balancing local and global search, leading to premature convergence and inefficiency. To address these issues, we introduce ADSC-DPE-RT (Automatic Deep Sparse Clustering with a Dynamic Population-based Evolutionary Algorithm using Reinforcement Learning and Transfer Learning), a novel deep clustering approach. ADSC-DPE-RT builds on Multi-Trial Vector-based Differential Evolution (MTDE), an algorithm that integrates sparse auto-encoding and manifold learning to enable automatic clustering without prior knowledge of cluster count. However, MTDE's fixed population size can lead to either prolonged computation or premature convergence. Our approach introduces a dynamic population generation technique guided by Reinforcement Learning (RL) and Markov Decision Process (MDP) principles. This allows for flexible adjustment of population size, preventing premature convergence and reducing computation time. Additionally, we incorporate Generative Adversarial Networks (GANs) to facilitate dynamic knowledge transfer between MTDE strategies, enhancing diversity and accelerating convergence towards the global optimum. This is the first work to address the dynamic population issue in deep clustering through RL, combined with Transfer Learning to optimize evolutionary algorithms. Our results demonstrate significant improvements in clustering performance, positioning ADSC-DPE-RT as a competitive alternative to state-of-the-art deep clustering methods.

Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

Risk identification and prediction model for continuous‐lane‐change vehicles considering driving style

Lane change risk identification and prediction research is important for traffic safety, development of advanced driver assistance systems, traffic flow optimization. Among them, vehicle continuous‐lane‐change (CLC) risk identification and prediction is weak, but this element is important for vehicle decision-making and path planning in multi-lane, non-adjacent lane situations. To compensate for the shortcomings of risk identification based on both deterministic and probabilistic methods and address the issues of poor measurement accuracy and efficiency caused by manual tuning, this study established a risk identification and prediction model based on the Bayesian global optimization (BO) gated recurrent unit (GRU) neural network (BO-GRU) and instantiated the model using the trajectory data of 129 vehicles. Overall, the results showed that the accuracy of identifying CLC risks and driving-style was 99.33% and 97.44%, respectively, while the overall average accuracy of predicting potential CLC risks based on the identification of risks and predicted trajectories was 97.13%. The root-mean-square errors of CLC predicted trajectory for different driving-styles were 0.2503, 0.2331, 0.2093, and 0.2706, respectively, which were 84%−88% lower compared to 1.7361 when driving-styles were not considered. The performance of the BO-GRU model is comparable to that of parameter-optimized LSTM and its variant models, which highlights the significance of parameter optimization for neural network models. Overall, the use of a BO-GRU-based model for identifying and predicting continuous lane change risks in vehicles can aid in making driving decisions and planning routes, decrease the risk of collisions, enhance traffic flow efficiency, and encourage safe driving behavior.

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.