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Abstract: We show that the main results in the recent paper [4] by Sadiq Basha (2012) are not real generalizations but particular cases of existing fixed point theorems in the literature. [Copyright &y& Elsevier]

A multi-modal search space can be defined as having multiple attraction basins - each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers. [ABSTRACT FROM AUTHOR]

This paper introduces a new simple moment-generating function (MGF) design modelling method to conclude an optimum filter to maximize the Q-factor and increase the link communication span. This approach mitigates the pulse temporal dispersion, particularly the underwater wireless optical communication (UWOC) systems. Hence, some form of equalizing filter design is highly desirable. The model solution environment includes a Double Gamma Function (DGF) water channel impulse response, intersymbol interference (ISI), stochastic Poisson process, and additive Gaussian thermal noise (AGTN). The optimal filters exhibit temporal profiles comparable to those derived by published works based on complex Chernoff Bound (CB) and Modified Chernoff Bound (MCB) methods. The results show the impact of the optimum filter at a signal level and optical receiver level utilizing Eye-Diagrams and BER vs. Q-Factor, respectively. The computation involves four different UWOC propagation channel models for Coastal and Harbor waters. One of the main conclusions indicates that the optimum filter manages the temporal dispersion due to the ISI impairment correctly. Also, the proposed optimum filter reduces eye-opening and the corresponding Q-Factor by less than 15% for a five-times increase in pulse width for the same transmitted optical power level. [ABSTRACT FROM AUTHOR]

The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called "structures", meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study. [ABSTRACT FROM AUTHOR]

This paper introduces a novel type of floating machine gun that can be simplified as a self-balancing two-degree-of-freedom mechanical system with distinct vibration characteristics. The model accounts for intricate motion patterns and encompasses numerous potential influencing factors. Multifactor combination optimization of the system represents a pressing engineering challenge. After establishing a simulation model for the machine gun and validating it experimentally, seven factors were chosen as optimization variables. The maximum recoil displacement of the inner receiver (MRD) and the firing rate were chosen to be indicators. Orthogonal combinations and variance analyses were used, and the effects of multiple factors were analyzed using SPSS software; these processes led to a determination of the optimal combination. The results indicated that the piston cylinder pressure, the bi-directional buffer spring energy storage, and the inner receiver mass significantly affected the MRD. Furthermore, the automaton mass and the reset spring energy storage were found to substantially affect the firing rate. Careful analysis of the variance results facilitated the determination of the optimal combination of parameter values. Remarkably, the optimal combination chosen resulted in an MRD reduction of approximately 20.2% and a firing rate increase of approximately 26.6%. [ABSTRACT FROM AUTHOR]

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid intelligent simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs) called WMN-PSOHCDGA. We implemented six mesh router replacement methods: CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM and consider Two Islands distribution of mesh clients. We carry out a comparison study of these router replacement methods for small and middle scale WMNs. We assessed the performance by computer simulations. The simulation results show that six methods have a good performance for connectivity and coverage metrics, for both small and middle scale WMNs. However, they have different behavior for load balancing. For small scale WMNs, the load balancing of LDIWM, RIWM and FC-RDVM is better than CM, LDVM and RDVM. While, comparing LDIWM, RIWM and FC-RDVM, the LDIWM has better load balancing. We found that the load balancing for small scale WMNs is not good, because there is a concentration of mesh routers in some areas. For middle scale WMNs, the CM, LDIWM, LDVM and RDVM have not a good load balancing. While, the RIWM and FC-RDVM have better performance. Comparing RIWM and FC-RDVM, we found that the load balancing of FC-RDVM is better than RIWM. [ABSTRACT FROM AUTHOR]

In this paper, we focus on using the low-rank factorization approach to solve the SDP relaxation of a graph equipartition problem, which involves an additional spectral upper bound over the traditional linear SDP. We discuss the equivalence between the decomposed problem and the original SDP problem. We also derive a sufficient condition, under which a second order stationary point of the non-convex problem is also a global minimum. Moreover, the constraints of the non-convex problem involve an algebraic variety with conducive geometric properties which we analyse. We also develop a method to escape from a non-optimal singular point on this variety. This allows us to use Riemannian optimization techniques to solve the SDP problem very efficiently with certified global optimality. [ABSTRACT FROM AUTHOR]

This article demonstrates techniques to solve the linear fractional programming (LFP) problem using an interval approach. This approach addresses uncertainties as intervals and employs interval arithmetic for robustness. In this paper, a reasonable attempt is made to construct a mathematical model of interval linear fractional programming, and various approaches were employed to solve it. The proposed process emphasizes solving the ILFP problem in different optimization techniques and uses interval arithmetic to obtain a better range of intervals. The study illustrates the practical aspects of this approach and its effectiveness in solving real-world situations when uncertainties are significant. The methods, process, solutions, and time consumption are analyzed later to show our proposed method's real-life application and efficiency. [ABSTRACT FROM AUTHOR]

Recent research focuses on optimal design of thermal energy storage (TES) systems for various plants and processes, using advanced optimization techniques. There is a wide range of TES technologies for diverse thermal applications, each with unique technical and economic characteristics. Matching an application with the most suitable TES system remains challenging. This study proposes an eight‐step design methodology guiding the process from describing the thermal process to defining the most appropriate TES based on constraints and requirements. The steps include specifying the thermal process, system design parameters, storage characteristics, integration parameters, key performance indicators, optimization method, tools, and design robustness. Seven already‐designed TES systems are evaluated to assess the methodology's effectiveness, where the design procedures have been adapted to the proposed steps. Case studies involve various applications with both sensible and latent TES systems, demonstrating the applicability of the proposed design procedure. A significant diversity exists among the design cases regarding the design objective, input, design, and output parameters. Nevertheless, the design procedure in each case can be deconstructed into the outlined design steps. The last design step has been excluded from all case studies due to insufficient information regarding the robustness of the design process. The paper demonstrates how a methodical approach can be applied to examine the TES design and the integration. The design steps proposed in this study can serve as a foundation for developing a more systematic approach for designing TES systems in future works, resulting in simplifying the design process. [ABSTRACT FROM AUTHOR]

Many different aspects of software system development and verification rely on precise function identification in binary code. Recognition of the source Assembly functions in embedded systems is one of the fundamental challenges in binary program analysis. While numerous approaches assume that the functions are given a priori, correct identification of the functions in binaries remains a great issue. This contribution addresses the problem of uncertainty in binary code in identification of functions, which were optimized during compilation. This paper investigates the difference between debug and optimized functions via modeling of these functions. To do so, we introduce an extensible model-centred hands-on approach for examining similarities between binary functions. The main idea is to model each function using a set of predetermined, experimentally discovered features, and then find a suitable weight vector that could give impact factor to each such a feature. After finding the weight vector, the introduced models of such desired functions can be identified in binary software packages. It means that we reduce the similarity identification problem of the models to a classical version of optimization problems with one optimization criterion. Using our implementation, we found that the proposed approach works smoothly for functions, which contain at least ten Assembly instructions. Our tool guarantees success at a very high level. [ABSTRACT FROM AUTHOR]

This paper focuses on exploring the application possibilities and optimization problems of Generative Adversarial Networks (GANs) in spatial computing to improve design efficiency and creativity and achieve a more intelligent design process. A method for icon generation is proposed, and a basic architecture for icon generation is constructed. A system with generation and optimization capabilities is constructed to meet various requirements in spatial design by introducing the concept of interactive design and the characteristics of requirement conditions. Next, the generated icons can effectively maintain diversity and innovation while meeting the conditional features by integrating multi-feature recognition modules into the discriminator and optimizing the structure of conditional features. The experiment uses publicly available icon datasets, including LLD-Icon and Icons-50. The icon shape generated by the model proposed here is more prominent, and the color of colored icons can be more finely controlled. The Inception Score (IS) values under different models are compared, and it is found that the IS value of the proposed model is 7.05, which is higher than that of other GAN models. The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. After introducing multi-feature recognition modules into the network model, the peak error of the recognition network is only 2.000 in the initial stage, while the initial error of the ordinary GAN without multi-feature recognition modules is as high as 5.000. It indicates that the improved model effectively helps the discriminative network recognize the core information of icon images more quickly. The research results provide a reference basis for achieving more efficient and innovative interactive space design. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model. [ABSTRACT FROM AUTHOR]

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper we report a [2 + 2] cycloaddition reaction between ketenes and benzils, characterized by an unusual double photochemical activation triggered by visible light. Employment of a flow system and optimization of reaction conditions through Design of Experiments resulted in moderate to good yields of the corresponding β-lactones. A thorough computational analysis allowed to elucidate the mechanism of the reaction and justify the observed diastereoselectivity. The reaction was also successfully tested with mixed benzils, showing complete regioselectivity. [ABSTRACT FROM AUTHOR]

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm to verify whether a finite game is a coset weighted potential game (WPG) without pre-knowledge on its coset weights. This algorithm can also provide a recursive method to calculate the unknown coset weights. Then we give the concept of near coset WPGs based on evolutionary equivalence between two finite games, and its algebraic verification is obtained. Finally, the application of near coset WPGs to game-theoretical optimisation of multi-agent systems is discussed, which can improve the applicability of potential games in multi-agent optimisation problems. [ABSTRACT FROM AUTHOR]

This paper presents the development of an optimization and modeling method for the objective functions of output power, efficiency and weight of an outer rotor permanent magnet brushless DC (BLDC) motor based on radial basis function (RBF) approximation technique. The proposed RBF‐based Pareto optimization method requires less knowledge about electric/magnetic formulas and can replace conventional optimizations based on these equations with higher accuracy. To apply the proposed optimization method, the initial design should be developed using such equations. Therefore, RBFs are used to model and predict engine behavior. To optimize the objective functions, we used a genetic algorithm optimization technique with nonlinear electric and magnetic constraints to find the Pareto front set. The design obtained by the proposed radial basis function Pareto optimization (RBFPO) method was finally verified by Ansoft Maxwell. The results of optimal design using the RBFPO method have higher output power and efficiency. Also, in addition to the advantage of a favorable accuracy, RBF‐based models are significantly faster than models available in simulation tools. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for modeling the nonlinear behavior of microwave devices, with a particular focus on addressing the challenging problem of modeling compound semiconductor transistors under large‐signal excitation. The approach utilizes a knowledge‐based neural network (KBNN) to construct a frequency‐domain behavioral model of the transistor, which maps incident and scattered waves from a coarse model to a fine model in cases where the coarse model fails to match the behavior of a new device. This neural network‐based method aims to achieve better alignment between the extracted model parameters obtained from the coarse model and the behavior of the fine model. To improve mapping efficiency and accuracy, Bayesian optimization techniques are employed to automatically adjust the hyper parameters in the KBNN within a custom‐defined hyper parameter space. The proposed method uses simulation and measurement of a 0.25 um GaN HEMT device operating at 8 GHz, with VGS at −2 V and VDS at 18 V. Furthermore, the proposed model exhibits good interpolation capability at different input power levels, indicating its broad applicability in the design of high‐speed device models. Specifically, the accuracy of the simulation data and test data reached −53.83 and −51.68 dB, respectively, with the test data used in model training being less than 7.5% of the simulation data. [ABSTRACT FROM AUTHOR]

Direct electromagnetic (EM) optimization for microwave components design is usually a time‐consuming process. To improve the optimization efficiency, this paper proposes a novel parallel EM optimization technique exploiting improved pole‐residue‐based neuro‐transfer function (neuro‐TF) surrogate and isomorphic orthogonal design of experiment (DOE) sampling strategy. We propose a new sampling method combining isomorphic orthogonal DOE and parallel EM simulations to generate training data for developing the neuro‐TF surrogate. This proposed sampling method can ensure the scattered distribution of data samples in the overall optimization process, thus effectively improving the surrogate accuracy and increasing the optimization speed. We also propose a new pole‐residue tracking technique for order‐changing to solve the discontinuity problem of pole/residues during the neuro‐TF surrogate development. Different from the fixed split ratio in existing pole‐residue tracking technique, the split ratio of poles and residues in the proposed technique is adaptive and determined according to the information of neighboring samples. Therefore, the continuity and smoothness of pole/residues after the splitting are improved, so as the neuro‐TF surrogate accuracy. In addition, the trust region algorithm is exploited during EM optimization to improve the convergence speed. In this way, the proposed EM optimization technique obtains the optimal solution in a shorter time with fewer iterations than the existing techniques. Two examples of EM optimizations of microwave components are used to illustrate the proposed technique. [ABSTRACT FROM AUTHOR]

This paper explains the importance and benefits of using AI in fluid mechanics, emphasizing its capability of finding the solution of fluid flow systems through iterative optimization techniques. It explores how AI can enhance the understanding of fluid dynamics to improve engineering processes. In the presented studies, the heat and mass transport of the Casson nanofluidic flow model on a nonlinear slanted extending sheet (HMT‐CNFM) via AI‐based Levenberg Marquard methodology with backpropagated trained neural networks (TNN‐BLMM) is studied. By applying an appropriate transformation, the governing PDEs representing HMT‐CNFM are converted into a system of nonlinear ODEs. The dataset for Levenberg Marquard methodology with backpropagated trained neural network (TNN‐BLMM) for all six scenarios of this proposed model via computational power of the Lobatto IIIA scheme using the "bvp4c" package in MATLAB and then graphically illustrate all these six scenarios through nftool to attain mean square error, regression, error histogram, performance, and fit curve. Training, testing, and validation processes of NN‐BLMM are organized for the investigation of the HMT‐CNFM model. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization (TLBO) algorithm is an excellent human-inspired optimization technique. This paper proposes an innovative improved version of TLBO—collectivism teaching–learning-based optimization (CTLBO) algorithm. This algorithm imitates group and individual behaviours in the reality of teaching and learning, applies group-individual multi-mode cooperation strategies to form new search mechanism. The CTLBO contains three phases, i.e. preparation phase, teaching and learning phases. In the preparation phase, there are two operators, i.e. teacher self-learning and teacher-learner interaction operators. In the teaching phase, class teaching and performance-based group teaching operators are implied. In the learning phase, neighbour learning, student self-learning and team-learning strategies are mixed together to form three operators. Two sets of experiments are conducted to test the performance of CTLBO. The first set of experiments validates the improvement effect of CTLBO by comparing it with the original TLBO and other authors' improved versions of TLBO. The second set of experiments illustrates the advantage of CTLBO by comparing it with other 17 meta-heuristic algorithms in solving 30 general benchmark functions and 15 CEC2015 test suit functions. The results of experiments show that CTLBO algorithm has significant improvement effect compared with TLBO. It is the most effective one amongst the improved versions of TLBO selected for comparison, and outperforms all other 17 meta-heuristic algorithms. The algorithm can significantly improve the convergence ability and the accuracy in solving different-scale complex optimization models. [ABSTRACT FROM AUTHOR]

The aim of this work is to analyze the applicability of a convex hyperbola chart's methodology to determine how many pumps should be working in a pumping station of a real case study to consume the least amount of energy. The applicability of the convex hyperbola charts is demonstrated, its effectiveness is shown, and a step-by-step exemplification is presented. Moreover, the order in which pumps should be activated is analyzed and discussed. The pumping station of the optimization is located in Tres Cantos, Madrid, Spain; it consists of a pumping station of four (+1 reserved) hydraulic pumps that take water from a reservoir and distribute it through a branched pipeline. The geometric height difference Hg of the case study is variable. This article also shows how the variability of Hg plays a major role in the optimal configuration of the pumping station. This paper also proves how the number of pumps to activate or disactivate does not necessarily need to be consecutive, meaning that activating or disactivating pumps one by one may not be the best solution. The convex hyperbola charts show how there can be circumstances in which skipping a certain number of pumps is the best solution. How the pump efficiency is distributed along the commercial pump plays a major role in determining which is the best configuration of active pumps. A straightforward and inexpensive optimization methodology for the optimization of the energy in a water supply system was proved and exemplified. This simple methodology can be applied by engineers in the operation of a water supply system when pumping is required, e.g., in agricultural systems or in underdeveloped areas where energy expenses need to be considered. [ABSTRACT FROM AUTHOR]

This review explores the pivotal role of attention in everyday life, emphasizing the significance of studying attention-related brain functions. We delve into the development of methodologies for investigating attention and highlight the crucial role of brain neuroimaging and transcranial magnetic stimulation (TMS) in advancing attention research. Attention optimization theory is introduced to elucidate the neural basis of attention, identifying key brain regions and neural circuits involved in attention processes. The theory further explores neuroplasticity, shedding light on how the brain dynamically adapts and changes to optimize attention. A comprehensive overview of TMS is provided, elucidating the principles and applications of this technique in affecting brain activity through magnetic field stimulation. The application of TMS in attention research is discussed, outlining how it can be employed to regulate attention networks. The clinical applications of TMS are explored in attention-deficit/hyperactivity disorder (ADHD) and depression. TMS emerges as an effective clinical treatment for ADHD, showcasing its potential in addressing attention-related disorders. Additionally, the paper emphasizes the efficacy of TMS technology as a method for regulating depression, further underlining the versatility and therapeutic potential of TMS in clinical settings. In conclusion, this review underscores the interdisciplinary approach to attention research, integrating neuroimaging, neuroplasticity, and TMS. The presented findings contribute to our understanding of attention mechanisms and highlight the promising clinical applications of TMS in addressing attention-related disorders. This synthesis of theoretical and practical insights aims to propel further advancements in attention research and its therapeutic applications. [ABSTRACT FROM AUTHOR]

Virtual track trains are a new type of rail transportation because the multisection formation structure leads to more degrees of freedom of the vehicle, which may cause unstable phenomena, such as tailing, cross-swing, and folding, affecting the stability and ride comfort of the vehicle driving. To explore the effect of damping coefficient of the articulated systems on vehicle dynamics performance, a vehicle system dynamics model is established based on the actual parameters of a three-module six-axle virtual track train. According to ISO14791: 2000, select typical working conditions such as straight line, lane change, and 1/4 circle curve, and optimize the damping coefficient of the articulated systems through co-simulation. The study shows that under straight-line conditions, increasing the damping coefficient can effectively suppress the yaw angular acceleration and improve the lateral ride comfort of the vehicle but has little effect on the vertical ride comfort. Under lane change conditions, too large or small damping coefficients will deteriorate the train's lateral stability, and a reasonable damping coefficient will improve the yaw damping ratio of the vehicle and reduce the lateral sway vibration between vehicles. Under the 1/4 circle curve conditions, the additional articulated system damper will reduce the vehicle's curve passing performance. In this paper, the articulation stability of multimodule fully connected vehicles is analyzed and optimized for the first time, and the damping coefficient control strategy is given based on the geometric tracking control method. The research results are of great significance for the parameter selection of virtual track trains' articulated system and the design and development of specialized articulated systems for related vehicles. [ABSTRACT FROM AUTHOR]

This paper employs low-field nuclear magnetic resonance (LF-NMR) technology to meticulously analyze and explore the intricate soybean infiltration process. The methodology involves immersing soybeans in distilled water, with periodic implementation of Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence experiments conducted at intervals of 20 to 30 minutes to determine the relaxation time T2. Currently, magnetic resonance imaging (MRI) is conducted every 30 minutes. The analysis uncovers the existence of three distinct water phases during the soybean infiltration process: bound water denoted as T21, sub-bound water represented by T22, and free water indicated as T23. The evolution of these phases unfolds as follows: bound water T21 displays a steady oscillation within the timeframe of 0 to 400 minutes; sub-bound water T22 and free water T23 exhibit a progressive pattern characterized by a rise-stable-rise trajectory. Upon scrutinizing the magnetic resonance images, it is discerned that the soybean infiltration commences at a gradual pace from the seed umbilicus. The employment of LF-NMR technology contributes significantly by affording an expeditious, non-destructive, and dynamic vantage point to observe the intricate motion of water migration during soybean infiltration. This dynamic insight into the movement of water elucidates the intricate mass transfer pathway within the soybean-water system, thus furnishing a robust scientific foundation for the optimization of processing techniques. [ABSTRACT FROM AUTHOR]

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk. [ABSTRACT FROM AUTHOR]

This paper examines the optimal design for agri‐photovoltaic (agri‐PV) systems. Agri‐PV systems should be evaluated on the total crop yield and power generation, and the balance is essential. Specifically, the amount of solar irradiance reaching the farmland and power generation by PV systems varies by panel separation and tilt angle, affecting crop yield. Quantitative analysis is essential for balancing. This study investigated the balance between the output of Agri‐PV installed on farmland and photosynthetic photon flux density (PPFD), a crucial indicator for crop growth, and discussed the optimal solutions for Agri‐PV systems installed in various regions. Simulations were conducted for Miyazaki (semi‐tropical) and Nagano (inland climate) to understand the changes in different climate regions. Miyazaki has higher PPFD and PV output than Nagano because Miyazaki has more solar irradiance and longer sunshine duration than Nagano. When the normalized panel pitch is small, PPFD changes significantly by the tilt angle. When the normalized panel pitch is large, the profit may be maximized by setting the panel tilt angle to maximize the PV output. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Using of ultra-wideband (UWB) technology in radio transceiver systems has increased in recent years due to high-speed data transmission, low power dissipation, low cost, and low complexity. In particular, distributed amplifier (DA) is a critical component of transceiver in UWB technology. However, designing an ultra-wideband DA with high performance becomes challenging. The DA design suffers from the tight trade-offs between the amplifier parameters such as gain, noise, linearity, input/output impedance matching, and power dissipation. In this paper, a new approach for multi-objective optimization of the DA is introduced. In the proposed approach, the meta-heuristic optimization techniques are applied over the entire bandwidth of the UWB, while the most recent optimization approaches for amplifiers are performed at the center frequency and they can't achieve the proper design specifications for wideband amplifiers. The simultaneous optimization of power gain (S21), noise figure (NF), input and output return loss (S11 and S22) are conducted over the wide bandwidth using three multi-objective optimization algorithms including Multi-Objective Inclined Planes System Optimization (MOIPO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). The obtained results demonstrate the tapered matrix DA optimized by MOIPO exhibits better performance than others. The circuit simulations are performed in 0.18 µm TSMC RF-CMOS technology. Simulation results show that the optimized tapered matrix DA by MOIPO, compared to NSGA-II and MOPSO, exhibits a good performance over the frequency band of 0.1–28 GHz with maximum S21 of 12.9 dB, NF less than 5.9 dB, S11 and S22 below than − 10 dB over the whole frequency band. The DC power dissipation is 25 mW from a 1.5 V supply. [ABSTRACT FROM AUTHOR]

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess "better or worse" system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption. [ABSTRACT FROM AUTHOR]

Self-energy recycling cooperative communication, in which the relay nodes simultaneously harvest energy from the source and their own transmitted signal, has been demonstrated to significantly increase the total harvested energy. In this paper, we present an optimization framework for the resource allocation and relay selection with the objective of maximizing the sum-throughput in afull-duplex multi-relay multi-user multiple-input-multiple-output network by incorporating self-energy recycling at the relays, for the first time. The formulated problem is a mixed integer non-linear programming problem, which is difficult to solve for the global optimal solution in polynomial-time. As a solution strategy, we decompose the optimization problem into two sub-problems: resource allocation problem and relay selection problem. First, we solve the resource allocation problem for a predetermined relay selection by using Taylor series approximation and convex optimization techniques. For the relay selection problem, we propose a polynomial-time sub-optimal algorithm based on the idea of iteratively selecting the relay offering the maximum throughput at each time. Through simulations, we demonstrate that the proposed dynamic time FD-MIMO system increases the throughput by 30% as compared to the conventional static time protocol system, and by a factor of 2.4 over the system without self-energy recycling. [ABSTRACT FROM AUTHOR]

The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The system mainly completes operations through elevators and multiple rail-guided vehicles (RGVs). This paper focuses on the scheduling optimization of compound operations in the AVS/RS to improve system performance. Compound operations involve the coordinated execution of both single-command and double-command operations. A mathematical model with compound operations was proposed and effectively decomposed into a horizontal component for RGVs and a vertical counterpart for the elevator, which can represent the operations of one elevator cooperating with multiple RGVs. The goal of this model was to minimize the makespan for compound operations and to determine the optimal operation sequence and path for RGVs. An improved discrete particle swarm optimization (DPSO) algorithm called AGDPSO was proposed to solve the model. The algorithm combines DPSO and a genetic algorithm in an adaptive manner to prevent the algorithm from falling into local optima and relying solely on the initial solution. Through rigorous optimization, optimal parameters for the algorithm were identified. When assessing the performance of our improved algorithm against various counterparts, considering different task durations and racking configurations, our results showed that AGDPSO outperformed the alternatives, proving its effectiveness in enhancing system efficiency for the model. The findings of this study not only contribute to the optimization of AVS/RS but also offer valuable insights for designing more efficient warehouses. By streamlining scheduling, improving operations, and leveraging advanced optimization techniques, we can create a more robust and effective storage and retrieval system. [ABSTRACT FROM AUTHOR]

Since last few decades, a lot of work has been done on the evolutionary techniques to solve the optimisation problems. With the time passes, these techniques were modified, and also new algorithms were introduced to improve the performance. These techniques had been used by many researchers in synthesis of mechanisms to get the optimum results with minimum design errors. Improved harmony search (IHS) algorithm is developed from harmony search technique by improvising the harmony in HS algorithm. This technique is inspired by searching the best state of harmony in musical process in which a jazz musician make practice after practice to find the same. In this paper, IHS algorithm is utilized to synthesize four-bar path generation mechanism. A mathematical model was derived using vector loop closure equation, wherein the error function, representing positional error between actual and desired points, was considered for forming objective function. The penalty function was used to prevent violation of structural constraints corresponding to Grashof's criteria and input crank angle sequence. Three different cases were studied with ten, fifteen and eighteen precision points with and without prescribed timings. The results obtained from the study, compared with other evolutionary techniques from literature, and found significant competition. [ABSTRACT FROM AUTHOR]

A cascaded relativistic magnetron array with symmetric feeder structure was first proposed as a multi-port phase-coherent high-power microwave source, which is intrinsically equipped with high structural symmetry. Two symmetrically positioned slow-wave structures surround the feeder structure, which reduces axial electron drifting in each resonant system and ensures the phase-locking process. In this paper, a theory of structure-provided coupling coefficient and oscillator-required coupling coefficient is proposed as the phase-locking prerequisites. The method is evaluated by adopting two A6-type resonant systems. The symmetrically driven cascaded relativistic magnetron array employs a typical π-mode with an anode voltage of 450 kV and an axial magnetic field of 0.47 T. The phase-locked state was achieved in 17 ns with a jitter less than 5 deg. The total output power exceeds 2.3 GW at a frequency of 2.15 GHz, and the power flow in each output port exceeds 350 MW. The transversely opposed driven scheme can be combined with other phase-locking patterns for additional uses, and further optimization of resonant system could be applied for enhancing device performance. The theory of coupling prerequisites is also sufficient for analyzing other cascaded relativistic magnetrons. [ABSTRACT FROM AUTHOR]

Oilfield wastewater with high moisture content can be efficiently treated by supercritical water gasification (SCWG) technology to produce hydrogen. This paper conducted a thermodynamic and environmental analysis of the oilfield wastewater SCWG system to provide optimization strategies. The waste heat utilization technology can enhance the heat integration of the system to reduce the exergy destruction and increase the H 2 yield. Sensitivity analysis showed that a ratio of preheating water to emulsion 3 can promote water-sensitive reactions to produce H 2 and save the H 2 needed to maintain the energetically self-sufficient system. Low system pressures, high feedstock preheating temperatures, and low oxidation reactor temperatures can reduce system energy destruction to obtain high H 2 yield and system efficiency. Meantime, the developed SCWG system has the potential to achieve natural enrichment of greenhouse gases after the H 2 separation has been completed. The use of waste heat utilization technology and carbon capture and storage (CCS) technology is expected to increase system efficiency to 53.43 % and reduce global warming potential (GWP) of 5.52 kg CO 2 -eq/kg H 2. This work would be of great value to the process design and optimization for the SCWG system of oilfield wastewater. [Display omitted] • The process of supercritical water gasification of oilfield wastewater was simulated. • Optimization strategies were given by thermodynamic and environmental analysis. • Operational details were revealed by comprehensive sensitivity analysis. • The exergy efficiency of 53.43 % were obtained. • Global Warming Potential decreased to 5.52 kg CO 2 -eq/kg H 2 with CCS. [ABSTRACT FROM AUTHOR]

This paper formulates multi-objective (MO) generation scheduling problem of an integrated energy system (IES). The integrated energy system comprises combined heat and power (CHP), hydroelectric, thermal and heat units. The interdependency of heat and power in CHP units, water transport delay among various multi-chain hydroelectric units and valve point loading effect of thermal units makes the generation scheduling problem a complex, constrained, discontinuous, non-differentiable and multimodal optimization problem. The main motive of this research work is to concurrently reduce the overall generation cost and pollutant emissions emitted by various generating units. The generation scheduling problem is treated as a MO problem owing to the contradictory nature of these objectives. Thus, a very proficient hybrid optimization approach, i.e., quantum-based cuckoo search algorithm (QCSA) with mutation operators, is proposed for searching for the optimal generation schedule of the MO-integrated energy system generation scheduling (IESGS) problem. The proposed technique significantly improves the solutions obtained by QCSA by utilizing three mutation operators, i.e., Cauchy, Gaussian, and opposition-based mutation. The proposed strategy has been implemented to MO-hydroelectric-thermal (HT) generation scheduling and MO-IESGS problems to demonstrate the efficacy of the proposed method. The cardinal priority method is utilized for finding the most suitable non-dominated solution for both problems. The obtained results are comparatively better than the published results. The proposed approach's robustness compared to the QCSA has been further verified using the t-test. Based on comparisons and statistical analysis, the proposed technique is a promising approach for handling complex, multi-dimensional and non-convex generation scheduling optimization problems. [ABSTRACT FROM AUTHOR]

Modern rail/road transportation systems are critical to global travel and commercial transportation. The improvement of transport systems that are needed for efficient cargo movements possesses further challenges. For instance, diesel-powered trucks and goods trains are widely used in long-haul unimodal transportation of heavy cargo in most landlocked and developing countries, a situation that leads to concerns of greenhouse gases (GHGs) such as carbon dioxide coming from diesel fuel combustion. In this context, it is critical to understand aspects such as the use of some parameters, variables and constraints in the formulation of mathematical models, optimization techniques and algorithms that directly contribute to sustainable transportation solutions. In seeking sustainable solutions to the bulk cargo long-haul transportation problems in Zambia, we conduct a systematic review of various transportation modes and related mathematical models, and optimization approaches. In this paper, we provide an updated survey of various transport models for bulk cargo and their associated optimized combinations. We identify key research challenges and notable issues to be considered for further studies in transport system optimization, especially when dealing with long-haul unimodal or single-mode heavy cargo movement in countries that are yet to implement intermodal and multimodal systems. [ABSTRACT FROM AUTHOR]

Energy costs are now one of the leading criteria when procuring new computing hardware. Until recently, developers and users focused only on pure performance in terms of time-to-solution. Recent advances in energy-aware runtime systems render the optimization of both runtime and energy-to-solution possible by including hardware tuning depending on the application's workload. This work presents the impact that energy-sensitive tuning strategies have on a state-of-the-art high-performance computing code based on the lattice Boltzmann approach called waLBerla. We evaluate both CPU-only and GPU-accelerated supercomputers. This paper demonstrates that, with little user intervention, when using the energy-efficient runtime system called MERIC, it is possible to save a significant amount of energy while maintaining performance. [ABSTRACT FROM AUTHOR]

The textile industry produces high volumes of colored effluents that require multiple treatments to remove non-adsorbed dyes, which could be recalcitrant due to their complex chemical structure. Most of the studies have dealt with the biodegradation of mono or diazo dyes but rarely with poly-azo dyes. Therefore, the aim of this paper was to study the biodegradation of a four azo-bond dye (Sirius grey) and to optimize its decolorization conditions. Laccase-containing cell-free supernatant from the culture of a newly isolated fungal strain, Coriolopsis gallica strain BS9 was used in the presence of 1-hydroxybenzotriazol (HBT) to optimize the dye decolorization conditions. A Box–Benken design with four factors, namely pH, enzyme concentration, HBT concentration, and dye concentration, was performed to determine optimal conditions for the decolorization of Sirius grey. The optimal conditions were pH 5, 1 U/mL of laccase, 1 mM of HBT, and 50 mg/L of initial dye concentration, ensuring a decolorization yield and rate of 87.56% and 2.95%/min, respectively. The decolorized dye solution showed a decrease in its phytotoxicity (Germination index GI = 80%) compared to the non-treated solution (GI = 29%). This study suggests that the laccase-mediator system could be a promising alternative for dye removal from textile wastewater. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

In order to mitigate the impact of temperature and irradiation variations on photovoltaic (PV) panels, it is crucial to employ the maximum power point tracking (MPPT) technique. This paper introduces a novel MPPT approach that provides a reference current, denoted as Iref, to extract the maximum power from the PV array. The difference between the input current Ipv, and Iref is then fed into a PID controller, which adjusts the duty cycle accordingly to control the Quadratic Boost Converter (QBC). The proposed MPPT approach is based on the analysis of measurement data obtained from PV panels. From this, a linear mathematical function between Iref and solar irradiation is achieved. To assess the performance of this approach, a comparative study is being carried out not only with conventional MPPT techniques such as P&O and INC-COND but also with modern techniques such as ANN, PSO, FLC, and improved P&O (IMP-P&O). The simulation results clearly demonstrate enhanced tracking performance under various test conditions, including the Step & Ropp test, as well as real profiles for both sunny and cloudy days. Certainly, the novel approach shows its ability to outperform both conventional and modern methods with a faster convergence speed (less than 2 milliseconds), the lowest steady-state oscillations (less than 0.25 Watts), and the highest tracking efficiency (greater than 99.79%). [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective decision model is formulated utilizing both single-layer and three-layer radon reduction layer materials by considering radon emanation rate and cost as decision objectives. Through a multi-objective non-dominated ranking method, the optimal solution is identified from a range of feasible alternatives. In the case of the example uranium tailings pond, the optimized three-layer radon reduction layer materials reduce the comprehensive cost by 47.8% compared with the single-layer radon reduction layer materials while meeting the radon emanation rate limit. Furthermore, the utilization of non-dominated multi-objective optimization techniques demonstrates higher efficiency in optimizing each radon reduction layer schemes. [ABSTRACT FROM AUTHOR]

This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Introduction/purpose: Destabilization of critical resources (CRs) or critical infrastructure (CI) important for the stability of the state can be dangerous for society, economy, and especially national security. Disruption of one CI object or one of its parts often affects and causes disruption of other dependent CI, because the modern society has become a "networked society". The paper proposes a model for quantifying and defining the interdependence between different CIs and their priorities, based on statements of experts. Methods 9 : The proposed methods that combine the Laboratory for Testing and Evaluation of Decision Making (DEMATEL) and the Analytical Network Process (ANP) have been successfully modified by fuzzy logic theory in this work. Results: Integrating multiple methods into a unique input data analysis model significantly affects the change in ranking. Conclusion: The work contributes to military science in making strategic decisions related to national security management through increasing the resilience of CRs and the societies that rely on them. [ABSTRACT FROM AUTHOR]

Systems design methods should aim for systems creating value. The decision-making processes in system engineering struggle to optimize this objective; however, even though the traditional concept of system value as a purely economic metric is recognized as deficient, a well-defined and standard conceptualization of comprehensive system value is still lacking. This study set out to facilitate different stakeholders, involved in developing systems, with a broad perspective on value. We define the system value as the system's holistic impact, encompassing the multi-domain effects on processes, environments, and stakeholders. This inclusive view, to be used by practitioners designing systems and policies, is expected to update existing practices and enhance resulting systems. This paper renders an extensive review of value references in multiple domains, both in system engineering and external, non-engineering, disciplines, and sets the foundation for a revised framing of value in systems engineering. To enable future applications for systems optimization, system value is thoroughly characterized, including its dependency on internal and external factors. This research lays the groundwork for problem formulation of a system value measure, its application in system engineering methods, and further analysis of the subject, both for engineered and non-technical systems. [ABSTRACT FROM AUTHOR]

Large amounts of geographical and property rights information are contained in 3D cadastral property units, although the amount of cadastral information expressed from various perspectives varies. Therefore, it is crucial to choose a perspective that is compatible with human visual perception and conveys the most data as the perspective changes. This paper proposes an optimization method for generating the optimal view of 3D cadastral property units to address the above issues. The construction of candidate perspectives is the first step, followed by the calculation of feature values, including visibility of 3D boundary points under each candidate perspective, visible area ratio, and visual comfort. Then, an improved particle swarm optimization technique is used to determine the optimal view of 3D cadastral property units and the ideal viewpoint for the expected input model. The research results show that the selected optimal viewpoint fully considers human visual comfort, can accommodate a large amount of cadastral information, and can be used as a mapping drawing in social applications of 3D cadastral systems. This viewpoint can effectively supplement front views, side views, and axis side views, enabling more visual information to be transmitted in 3D religion maps and 3D property right maps and improving readability. [ABSTRACT FROM AUTHOR]

This paper deals with the proposition of a kinetic model for the direct synthesis of DME via CO2 hydrogenation in view of the necessary optimization of the catalytic system, reactor design, and process strategy. Despite the fact that DME synthesis is typically treated as a mere combination of two separated catalytic steps (i.e., methanol synthesis and methanol dehydration), the model analysis is now proposed by taking into account the improvements related to the process running over a hybrid catalyst in a rational integration of the two catalytic steps, with boundary conditions properly assumed from the thermodynamics of direct DME synthesis. Specifically, the CO2 activation step at the metal–oxide interface in the presence of ZrO2 has been described for the first time through the introduction of an ad hoc mechanism based on solid assumptions from inherent studies in the literature. The kinetic modeling was investigated in a tubular fixed-bed reactor operating from 200 to 260 °C between 1 and 50 bar as a function of a gas hourly space velocity ranging from 2500 to 60,000 NL/kgcat/h, in a stoichiometric CO2/H2 feed mixture of 1:3 v/v. A well-detailed elementary mechanism was used to predict the CO2 conversion rate and identify the key reaction pathways, starting with the analysis of the implicated reactions and corresponding kinetic mechanisms and expressions, and finally estimating the main parameters based on an appropriate modeling of test conditions. [ABSTRACT FROM AUTHOR]

Mathematical optimization is a widespread method in order to improve, for instance, the efficiency of energy systems. A simulation approach based on partial differential equations can typically not be formulated as an optimization problem, thus requiring interfacing to an external optimization environment. This is, among others, also true for the programming language Modelica. Because of high computation time, such coupled approaches are often limited to small-scale optimization problems. Since simulation models tend to get more complex, simulation time and, in turn, associated optimization time rise significantly. To enable proper sampling of the search space, individual optimization runs need to be solved in acceptable times. This paper addresses the search for a proper optimization approach and tool to couple with Modelica/Dymola. The optimization is carried out on an exemplary power plant model from the ClaRa-Library using an evolutionary algorithm (SPEA2-based) with Ansys optiSlang. To verify and evaluate the results, a comparison with the standard Dymola optimization library is performed. Both parallelization and indirect optimization with surrogate models achieved a significant runtime reduction by a factor of up to 5.4. The use of meta models is particularly advantageous for repetitive optimization runs of the same optimization problem but may lead to deviations due to the calculated approximations. [ABSTRACT FROM AUTHOR]

There has been a notable increase in the frequency and severity of extreme events, such as hurricanes, floods, wildfires, and storms. These events, although infrequent, have a significant disruptive effect, causing prolonged outages and compromising essential services, thereby severely affecting customers' safety. As a result, there is an urgent requirement to enhance the resilience of distribution networks by quickly restoring the loads during and after a disaster. In this regard, this paper reviews the existing studies on black-start service restoration in active distribution systems and microgrids. A comprehensive review is conducted for each aspect of the restoration problem, encompassing various proposed methods and modeling techniques found in the existing literature. The aim of this review is to consolidate the knowledge and findings from previous studies, providing a valuable resource for researchers and practitioners in the field. Also, some key research directions for the future work in this field are recommended for developing more practical and reliable methods. [ABSTRACT FROM AUTHOR]