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There are several methods available for modeling sustainable supply chain and logistics (SSCL) issues. Multi-objective optimization (MOO) has been a widely used method in SSCL modeling (SSCLM), nonetheless selecting a suitable optimization technique and solution method is still of interest as model performance is highly dependent on decision-making variables of the model development process. This study provides insights from the analysis of 95 scholarly articles to identify research gaps in the MOO for SSCLM and to assist decision-makers in selecting suitable MOO techniques and solution methods. The results of the analysis indicate that economic and environmental aspects of sustainability are the main context of SSCLM, where the social aspect is still limited. More SSCLMs for sourcing, distribution, and transportation phases of the supply chain are required. Additionally, more sophisticated techniques and solution methods, including hybrid metaheuristics approaches, are needed in SSCLM.

Simulation‐optimization methods are commonly used in groundwater pollution source identification. Traditional simulation‐optimization methods require multiple calls to the numerical model, which leads to a considerable computational burden. Surrogate models based on machine learning can replace numerical models while maintaining accuracy. Previous studies have focused on the fitting accuracy of surrogate models, this study emphasizes the importance of the precision of surrogate models for the inversion process. We use the analytic hierarchy process to integrate ConvLSTM, convolutional neural network, and BiLSTM to improve the precision of the surrogate model. GMS is used to construct numerical models of two hypothetical cases and a practical case. Compared with the best results of the single deep learning methods, the integrated surrogate model improves the precision of the solution of the two hypothetical cases by 90% and 26%, respectively. In addition, the accuracy of the pollution source information obtained by incorporating the integrated surrogate model into the optimization model is higher than that obtained by ConvLSTM as the surrogate model. The inversion results of 7 metaheuristic optimization algorithms are compared through two hypothetical cases, and then the optimization algorithm with higher accuracy is applied to the solution of the practical case. To obtain more accurate results, we reobtain a batch of training data by resampling and training the integrated surrogate model. The results show that constructing an integrated surrogate model and selecting an optimization algorithm can improve the solution accuracy of the simulation‐optimization method. This research provides a new perspective for the construction of simulation‐optimization methods.

The development of the promising Artificial Intelligence of The things (AIoT) technology increases the demand for implementing Convolutional Neural Networks (CNN) algorithms on the edge devices. However, implementing huge CNN-based applications on the resource-constrained edge devices is considered challenging. Therefore, several CNN optimization methods are integrated into the deployment tools of the edge devices. Since this field evolves rapidly, relevant tools adopt non-uniform deployment optimization flows, and the optimization details are poorly explained. This fact hinders developers from further analyzing the bottlenecks of the CNN-based applications on the edge devices. Hence, the paper comprehensively analyzes the deployment optimization methods for the CNN-based applications on the edge devices. Optimization methods are classified into the Hardware-Agnostic and Hardware-Specific methods. Their ideas and processing details are analyzed, and some suggestions are proposed according to the deployment experiments with different architecture models.

This paper presents an adaptive multi-tracker optimization algorithm (AMTOA) for global optimization problems with an emphasis on applications in chemical engineering. To obtain the AMTOA, first, several modifications are performed on the conventional multi-tracker optimization algorithm (MTOA). Then a number of its parameters are considered to be adaptive. The modifications include a novel way of determining the search radius of each global tracker (GT), and introducing a more efficacious technique of searching for a new solution by GTs. GTs are the main components of the MTOA which look for the global optimal point (GOP). Additionally, the adaptation rules are employed for GTs search radii and their searching parameters. These modifications lead to increasing the precision of the solution and reliability of the algorithm, both of which are the most important properties of an optimizer. Reducing the number of parameters of MTOA is another advantage of AMTOA. The results of applying this algorithm to several unconstrained and constrained general benchmarks along with several chemical engineering optimization problems reveal that AMTOA outperforms other well-known methods such as genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and conventional MTOA. Additionally, comparing the results of AMTOA to other advanced optimization algorithms such as LSHADE44, MA-ES, and IUDE show its superiority for chemical engineering optimization problems. Thus, the development of AMTOA could be advantageous to the area of chemical engineering.

The accuracy for target tracking using a conventional interacting multiple-model algorithm (IMM) is limited. In this paper, a new variable structure of interacting multiple-model (VSIMM) algorithm aided by center scaling (VSIMM-CS) is proposed to solve this problem. The novel VSIMM-CS has two main steps. Firstly, we estimate the approximate location of the true model. This is aided by the expected-mode augmentation algorithm (EMA), and a new method—namely, the expected model optimization method—is proposed to further enhance the accuracy of EMA. Secondly, we change the original model set to ensure the current true model as the symmetry center of the current model set, and the model set is scaled down by a certain percentage. Considering the symmetry and linearity of the system, the errors produced by symmetrical models can be well offset. Furthermore, narrowing the distance between the true model and the default model is another effective method to reduce the error. The second step is based on two theories: symmetric model set optimization method and proportional reduction optimization method. All proposed theories aim to minimize errors as much as possible, and simulation results highlight the correctness and effectiveness of the proposed methods.

Abstract A critical challenge lies in developing an energy-efficient and eco-friendly power supply system. Despite the enhanced energy efficiency offered by combined cooling, heating, and power (CCHP) systems, optimizing them poses challenges due to conflicting goals like reducing fuel consumption and carbon dioxide emissions while maximizing cost savings. To address these issues, this research suggests a solution that merges a modified subtraction-average-based optimizer with a multiobjective optimization strategy. This proposed framework attains a superior equilibrium among competing objectives compared to three existing optimization algorithms. It leads to a 12% decrease in fuel consumption, a 15% drop in carbon dioxide emissions, and a 10% cost reduction for shopping center proprietors. Moreover, the optimized CCHP system outperforms a stand-alone production system and a nonoptimized CCHP system, yielding 20% and 15% fuel savings annually, respectively. By offering a more comprehensive and balanced approach to CCHP system optimization, the proposed framework contributes to the progression of energy system optimizer, fostering the creation of more sustainable and environmentally friendly energy systems of shopping centers.

Inverse problems for a 3D model of electrostatics, which arise when developing technologies for designing electric cloaking and shielding devices, are studied. It is assumed that the devices being designed to consist of a finite number of concentric spherical layers filled with homogeneous anisotropic or isotropic media. A mathematical technique for solving these problems has been developed. It is based on the formulation of cloaking or shielding problems in the form of inverse problems for the electrostatic model under consideration, reducing the latter problems to finite-dimensional extremum problems, and finding their solutions using one of the global minimization methods. Using the developed technology, the inverse problems are replaced by control problems, in which the role of controls is played by the permittivities of separate layers composing the device being designed. To solve them, a numerical algorithm based on the particle swarm optimization method is proposed. Important properties of optimal solutions are established, one of which is the bang-bang property. It is shown on the base of the computational experiments that cloaking and shielding devices designed using the developed algorithm have the simplicity of technical implementation and the highest performance in the class of devices under consideration.

This article offers a new physical-based meta-heuristic optimization algorithm, which is named Transient Search Optimization (TSO) algorithm. This algorithm is inspired by the transient behavior of switched electrical circuits that include storage elements such as inductance and capacitance. The exploration and exploitation of the TSO algorithm are verified by using twenty-three benchmark, where its statistical (average and standard deviation) results are compared with the most recent 15 optimization algorithms. Furthermore, the non-parametric sign test, p value test, execution time, and convergence curves proved the superiority of the TSO against other algorithms. Also, the TSO algorithm is applied for the optimal design of three well-known constrained engineering problems (coil spring, welded beam, and pressure vessel). In conclusion, the comparison revealed that the TSO is promising and very competitive algorithm for solving different engineering problems.

In this paper we survey the primary research, both theoretical and applied, in the area of robust optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multistage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.

The main goal of machine learning is the creation of self-learning algorithms in many areas of human activity. It allows a replacement of a person with artificial intelligence in seeking to expand production. The theory of artificial neural networks, which have already replaced humans in many problems, remains the most well-utilized branch of machine learning. Thus, one must select appropriate neural network architectures, data processing, and advanced applied mathematics tools. A common challenge for these networks is achieving the highest accuracy in a short time. This problem is solved by modifying networks and improving data pre-processing, where accuracy increases along with training time. Bt using optimization methods, one can improve the accuracy without increasing the time. In this review, we consider all existing optimization algorithms that meet in neural networks. We present modifications of optimization algorithms of the first, second, and information-geometric order, which are related to information geometry for Fisher–Rao and Bregman metrics. These optimizers have significantly influenced the development of neural networks through geometric and probabilistic tools. We present applications of all the given optimization algorithms, considering the types of neural networks. After that, we show ways to develop optimization algorithms in further research using modern neural networks. Fractional order, bilevel, and gradient-free optimizers can replace classical gradient-based optimizers. Such approaches are induced in graph, spiking, complex-valued, quantum, and wavelet neural networks. Besides pattern recognition, time series prediction, and object detection, there are many other applications in machine learning: quantum computations, partial differential, and integrodifferential equations, and stochastic processes.