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The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a dynamic representation of problems, a real-time sensing system, and adaptive techniques to navigate continuously changing optimization environments. Through a simulated dynamic optimization problem, the AMF’s capability is demonstrated to detect environmental changes and proactively adjust its search strategy. This framework utilizes a differential evolution algorithm that is improved with an adaptation module that adjusts solutions in response to detected changes. The capability of the AMF to adjust is tested through a series of iterations, demonstrating its resilience and robustness in sustaining solution quality despite the problem’s development. The effectiveness of AMF is demonstrated through a series of simulations on a dynamic optimization problem. Robustness and agility characterize the algorithm’s performance, as evidenced by the presented fitness evolution and solution path visualizations. The findings show that AMF is a practical solution to dynamic optimization and a major step forward in the creation of algorithms that can handle the unpredictability of real-world problems.

Quarry sites are complex systems that involve several heavy machines, equipment, people, and management systems working together in an unstructured off-road environment. Gaining accurate insights about these sites requires integrating models at various levels to enable a holistic view systems and processes involved and facilitate effective planning, coordination, and decision-making. In this paper, a multi-level modelling framework is proposed to provide an overall structure for the modelling of quarry sites. The motivation for this framework is drawn from insights gained through a large manufacturing company in the heavy-duty vehicle industry, providing a practical perspective on the modeling approach. The framework integrates models of different operations on site enabling effective simulation and optimization and leading to better understanding of the workflow on site and pointing out any possible bottlenecks. The feasibility of the proposed framework was validated through workshops that included a panel of experts in different areas of the field of off-road machinery production company.

25th IEEE Wireless Communications and Networking Conference, WCNC 2024 -- 21 April 2024 through 24 April 2024 -- Dubai -- 200793, Extremely large aperture arrays can enable unprecedented spatial multiplexing in beyond 5G systems due to their extremely narrow beamfocusing capabilities. However, acquiring the spatial correlation matrix to enable efficient channel estimation is a complex task due to the vast number of antenna dimensions. Recently, a new estimation method called the 'reduced-subspace least squares (RS-LS) estimator' has been proposed for densely packed arrays. This method relies solely on the geometry of the array to limit the estimation resources. In this paper, we address a gap in the existing literature by deriving the average spectral efficiency for a certain distribution of user equipments (UEs) and a lower bound on it when using the RS-LS estimator. This bound is determined by the channel gain and the statistics of the normalized spatial correlation matrices of potential UEs but, importantly, does not require knowledge of a specific UE's spatial correlation matrix. We establish that there exists a pilot length that maximizes this expression. Additionally, we derive an approximate expression for the optimal pilot length under low signal-to-noise ratio (SNR) conditions. Simulation results validate the tightness of the derived lower bound and the effectiveness of using the optimized pilot length. © 2024 IEEE., ASELSAN Inc.; Stiftelsen för Strategisk Forskning, SSF; Scientific and Technological Research Council of Türkiye, (FFL18-0277)

Lithium-ion battery storage has emerged as a promising solution for various energy systems. However, complex degradation behavior, relatively short lifetime, high capital, and operational costs, and electricity market volatility are critical factors that challenge its practical viability. Thus, to ensure sustained profitability of Lithium-ion batteries in real-life applications, a smart and optimal management strategy considering key influencing factors is imperative for achieving efficient battery utilization. This study proposes two day-ahead battery-behavior-aware operation scheduling strategies to maximize profitability and longevity in residential grid-connected applications with dynamic electricity pricing. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario optimizes short-term profitability by prioritizing revenue gains under three charge/discharge rates (high, moderate, low), considering daily charge and discharge timings as decision variables. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of variables to simultaneously maximize revenue and minimize degradation costs, ensuring short-term and long-term profitability. Decision variables include the cycle frequency for each specific day, timings as well as durations for charging and discharging per cycle. To ensure effective long-term assessment, both scenarios accurately estimate battery performance, calendric and cyclic capacity degradations, remaining-useful-lifetime, and internal states under real operational conditions until battery reaches its end-of-life criteria. The scenarios are assessed economically using various indicators. Furthermore, the impact of battery price and size on optimization outcomes are examined. The key findings indicate that, among the first set of scenarios, the strategy with low charge/discharge rate extends the battery lifetime most efficiently, e

Throughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.

Search missions for objects are relevant in both industrial and civilian context, such as searching for a missing child in a forest or to locating equipment in a building or large factory. To send out a drone swarm to quickly locate a misplaced item in a factory, a missing machine on a building site or a missing child in a forest is very similar. Image-based Machine Learning algorithms are now so powerful that they can be trained to identify objects with high accuracy in real time. The next challenge is to perform the search as efficiently as possible, using as little time and energy as possible. If we have information about the area to search, we can use heuristic and probabilistic methods to perform an efficient search. In this paper, we present a case study where we developed a method and approach to evaluate different search algorithms enabling the selection of the most suitable, i.e., most efficient search algorithm for the task at hand. A couple of probabilistic and heuristic search methods were implemented for testing purposes, and they are the following: Bayesian Search together with a Hill Climbing search algorithm and Bayesian Search together with an A-star search algorithm. A swarm adapted lawn mower search strategy is also implemented. In our case study, we see that the performance of the search heavily depends on the area to search in and domain knowledge, e.g., knowledge about how a child is expected to move through a forest area when lost. In our tests, we see that there are significant gains to be made by selecting a search algorithm suitable for the search context at hand.

This paper presents a comprehensive survey of optimization developments in various aspects of electric vehicles (EVs). The survey covers optimization of the battery, including thermal, electrical, and mechanical aspects. The use of advanced techniques such as generative design or origami-inspired topological design enables by additive manufacturing is discussed, along with sensitivity studies of battery performance with alternate materials and incorporating sustainability considerations. Strategies for battery charging/discharging and battery swapping are reviewed, taking into consideration factors such as operation, cost, battery performance, and range anxiety. Future research is suggested to address uncertainties in charging ecosystem design and incorporate both forward and inverse prediction capabilities, leveraging benefits for both the grid and individual vehicles. The optimization techniques for other EV components, such as motors, powertrains, tires, and chassis, are also discussed. Finally, this paper presents a review of the EV management, specifically the optimization of charging station, grid, and fleet management, including research on charging station construction, charging station operation strategies, and power system operation strategies. The need for further research on robustness, reliability, and sustainability is emphasized to justify the use of EVs in the future. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 22AG001, 22AG024

Mixed-integer linear programming (MILP) techniques are widely used in cryptanalysis, aiding in the discovery of optimal linear and differential characteristics. This paper delves into the analysis of block ciphers KLEIN and PRINCE using MILP, specifically calculating the best linear and differential characteristics for reduced-round versions. Both ciphers employ matrix multiplication in their diffusion layers, which we model using multiple XOR operations. To this end, we propose two novel MILP models for multiple XOR operations, which use fewer variables and constraints, proving to be more efficient than standard methods for XOR modeling. For differential cryptanalysis, we identify characteristics with a probability of 2−59 for 7 rounds of KLEIN and a probability of 2−56 for 7 rounds of PRINCE. In linear cryptanalysis, we identify characteristics with a bias of 2−27 for 6 rounds of KLEIN and a bias of 2−29 for 7 rounds of PRINCE. These results establish the best single-key differential and linear distinguishers for these ciphers in the literature. © TÜBİTAK.

Fluctuations in electricity price create arbitrage opportunities for compressed CO2 energy storage (CCES) systems. However, previous studies often neglected the dynamic characteristics of CCES systems, leading to inaccurate assessments. This paper addresses this gap by evaluating the CCES system arbitrage considering its dynamic characteristics. We introduce a novel indicator, state of charge (SOC), into a mixed-integer linear programming (MILP) optimization model to capture the dynamics. Utilizing real electricity prices, the model optimizes the CCES operation strategy for a maximum profit. The results demonstrate that a CCES system with a 267 MWh capacity could achieve a total income of 22.5 MEUR in 2022, with a net present value (NPV) of 258.1 MEUR over 35 years, a payback time of 2 years, and an average round-trip efficiency (ARTE) of 77.0 %. Sensitivity analysis reveals that the sizes of the compressor, the expander, and the high-pressure gas tank significantly impact the arbitrage potential. In contrast, the steady-state model-based results demonstrate that the CCES system could yield a higher NPV of 573.7 MEUR, a shorter payback time of 1 year, and a higher ARTE of 87.0 %. This emphasizes the pivotal importance of integrating dynamic characteristics into the design and assessment of CCES systems for arbitrage assessment.

This paper introduces a novel computational fluid dynamics-artificial neural network (CFD-ANN) approach that has been devised to enhance the efficiency of plunger valves. The primary emphasis of this research is to achieve an optimal equilibrium between hydraulic flow and geometric configuration. This study is a novel contribution to the field as it explores the flow dynamics of plunger valves using Computational Fluid Dynamics (CFD) and proposes a unique methodology by incorporating Machine Learning (ML) for performance forecasting. An artificial neural network (ANN) architecture was developed using a thorough comprehension of flow physics and the impact of geometric parameters acquired through computational fluid dynamics (CFD). Using optimization, the primary aspects of the Artificial Neural Network (ANN), including the learning algorithm and the number of hidden layers, have been modified. This refinement has resulted in the development of an architecture exhibiting a remarkably high R2 value of 0.987. This architectural design was employed to optimize the plunger valve. By utilizing Artificial Neural Networks (ANN), a comprehensive analysis comprising 1000 distinct configurations was effectively performed, resulting in a significant reduction in time expenditure compared to relying on Computational Fluid Dynamics (CFD). The result was a refined arrangement that achieved maximum head loss, subsequently verified using computational fluid dynamics (CFD) simulations, resulting in a minimal discrepancy of 2.66%. The efficacy of artificial neural networks (ANN) becomes apparent due to their notable cost-efficiency, along with their capacity to produce outcomes that are arduous and expensive to get through conventional optimization research utilizing computational fluid dynamics (CFD). © 2024 Elsevier Ltd

This paper describes a novel method developed for the optimization of composite components against distortion caused by cure-induced residual stresses. A novel ply stack alteration algorithm is described, which is coupled to a parametrized CAD/FE model used for optimization. Elastic strain energy in 1D spring elements, used to constrain the structure during analysis, serves as an objective function incorporating aspects of global/local part stiffness in predicted distortion. Design variables such as the number and stacking sequence of plies, and geometric parameters of the part are used. The optimization problem is solved using commercial software combined with Python scripts. The method is exemplified with a case study of a stiffened panel subjected to buckling loads. Results are presented, and the effectiveness of the method to reduce the effects of cure-induced distortion is discussed. © 2023, The Author(s)., Funding: This project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 716864.

The PETase enzyme from the bacterium Ideonella sakaiensis can degrade polyethylene terephthalate (PET) back into its polymeric constituents at room temperature, making it an ecologically friendly tool for reducing PET pollution. Computational enzyme optimization is a fundamental tool to accelerate enzyme engineering towards the “green revolution” promised by the introduction of enzymatic catalysis in industry. The Asp83Asn mutant generates a sub-optimal reactive conformation that the mutation-induced transition state stabilization does not compensate for, and the barrier is raised by 1.9 kcal mol−1. In contrast, the Asp89Asn mutant keeps a perfect reactive conformation, and the mutation stabilizes the transition state more than the reactants, lowering the barrier by 4.7 kcal mol−1. We show that computer-based well-chosen single-residue substitutions in PETase can decrease the activation barrier significantly, facilitating the development of highly-efficient PETase mutants. The results of this work encourage future studies that aim for rational enzyme engineering on PETase and other enzymes.

The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud​ matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implem

The signal processing community is currently witnessing the emergence of sensor array processing and direction-of-arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, and drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless systems. This trend is further enhanced by technology leaps and the availability of powerful and affordable multiantenna hardware platforms. © 1991-2012 IEEE.

Sidescan sonar intensity encodes information about changes in the surface normal of the seabed. However, other factors such as seabed geometry as well as its material composition also affect the return intensity. One can model these intensity changes in a forward direction from the surface normals from a bathymetric map and physical properties to the measured intensity, or alternatively one can use an inverse model which starts from the intensities and models the surface normals. Here, we use an inverse model which leverages deep learning's ability to learn from data; a convolutional neural network is used to estimate the surface normal from the sidescan. Once this information is estimated, a bathymetric map can be reconstructed through an optimization framework that also includes altimeter readings to provide a sparse depth profile as a constraint. Implicit neural representation learning was recently proposed to represent the bathymetric map in such an optimization framework. In this article, we use a neural network to represent the map and optimize it under constraints of altimeter points and estimated surface normal from sidescan. By fusing multiple observations from different angles from several sidescan lines, the estimated results are improved through optimization. We demonstrate the efficiency and scalability of the approach by reconstructing a high-quality bathymetry using sidescan data from a large sidescan survey. We compare the proposed data-driven inverse model approach of modeling a sidescan with a forward Lambertian model. We assess the quality of each reconstruction by comparing it with data constructed from a multibeam sensor., QC 20230523

Ultrasonic welding has become a key joining approach in batteries and energy vehicles. This work reports the optimization in static property and stability of self-piercing riveting joints which is becoming the most versatile way to join microstructures. Tool heads of three different knurling profiles (e.g., A, B, and C) are used to perform ultrasonic composite with riveting. Based on tensile-shear tests, SEM, EDS, XRD, and Vickers microhardness analysis were conducted to investigate the comparative study of the fracture morphology, element distribution, phase structure, and hardness. The results indicated that all three types of knurling generate solid phase welding at the sheet joints. Thus, it improved the forming quality and mechanical properties of self-piercing riveting joints. The C-shaped welding tool head has advantageous effect on optimizing the mechanical properties of joints. Further, it enhanced the average peak load by 25.6%, the average failure displacement by 31.1%, and the average energy absorption by 88.8%. The microscopic results showed that a large amount of oxides are precipitated at the edge of welding joints when the B-shape knurling tool is used. The distribution of the hardness value of joints horizontal line is “M” shape. Besides, the coarse grains in the joint area and the interplanar space increase significantly after ultrasonic welding which softens the sheet. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

This paper is concerned with a new variant of bin packing called priority-based bin packing with subset constraints (PBBP-SC). PBBP-SC is a variant of traditional bin packing (TBP). In a TBP instance, we are given a collection of items {t1,t2,…tn}, where each item ti has size si∈(0,1). The goal of TBP is to pack the items in as few unit-sized bins as possible. In a PBBP-SC instance, we are given a collection of n unit-size items and a collection of m bins of varying capacities. Associated with each item is a positive integer which is called its priority. The priority of an item indicates its importance in being included in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. The first problem we study is the feasibility problem. This is the problem of checking if there is a feasible assignment to a given PBBP-SC instance. The second problem we study is the priority maximization problem. This is the problem of finding a maximum priority assignment of an infeasible PBBP-SC instance. The third problem we study is the bin minimization problem. This is the problem of finding an assignment with the fewest number of bins to pack all of the items in a feasible PBBP-SC instance. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. In particular, we show that both the feasibility and priority maximization problems are solvable in polynomial time. We also show that the bin minimization problem is log-APX-complete and cannot be solved in time o(1.41m) unless the Strong Exponential Time Hypothesis fails. © 2023 Elsevier B.V., Defense Advanced Research Projects Agency, DARPA: HR001123S0001-FP-004, This research was supported in part by the Defense Advanced Research Projects Agency through grant HR001123S0001-FP-004 .

2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 -- 7 September 2022 through 9 September 2022 -- 183936, With the spread of online platforms, problems such as manned/unmanned food delivery, cargo delivery, raw material delivery, are increasing the importance of logistics day by day. Vehicle routing problem, which is one of the most important problems in the field of logistics, is a combinatorial problem and as the problem space grows, it takes a long time to find a solution with human effort and in most cases it is not even possible. Thus, it becomes essential for the solution of this problem to be autonomous. Although it is possible to solve the problem with classical heuristic optimization methods, it takes a long time and sometimes does not give a good enough solution. Deep reinforcement learning models with attention mechanisms have great potential in this regard. However,in case of insufficient training in large problem space, it is possible to get away from the optimal solution. In this study, better results are taken in an acceptable time by using the deep reinforcement learning models with attention-model and heuristic methods in a hybrid way. © 2022 IEEE.

Aselsan;CIS ARGE;Yeditepe University, 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 -- 25 July 2023 through 27 July 2023 -- 191902, We study the problem of multi-user scheduling, beamforming and phase shift optimization in Reconfigurable Intelligent Surface (RIS)-assisted mmWave downlink transmission. The proposed proportional fair scheduling algorithm takes into account the direct and cascade achievable rates and inter-user interference. In order for a RIS to be useful in mmWave band, extremely high number of RIS elements are required. This results in prohibitive complexity in channel estimation and beam-forming/phase shift optimization. In this work we also discussed and proposed solutions for low complexity beamforming and phase shift optimization. Performance of the proposed scheduling, beamforming and phase shift optimization solution is evaluated by simulations. Comparison with some benchmarks reveal that interference-aware proportional fair user scheduling results in significant performance gains. © 2023 IEEE., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 122E401, This research is supported by the Turkish Scientific and Technological Research Council (TUBITAK) Research Project No. EEEAG 122E401.

This paper examines how to minimize the energy consumption of a user equipment (UE) when transmitting short data payloads. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS), which requires additional pilot signals to be configured, to improve the channel conditions. The challenge is that the pilot signals increase the energy consumption and must be balanced against energy savings during data transmission. We derive a formula for the energy consumption, including both pilot and data transmission powers and the effects of imperfect channel state information and discrete phase-shifts. To shorten the pilot length, we propose dividing the RIS into subarrays of multiple elements using the same reflection coefficient. The pilot power and subarray size are tuned to the payload length to minimize the energy consumption. Analytical results show that there exists a unique energy-minimizing solution. For small payloads and when the direct path loss between the BS and UE is weak compared to the path loss via the RIS, the solution is using subarrays with many elements and low pilot power and vice versa. The optimal percentage of energy spent on pilot signaling is in the order of 10-40%. IEEE

This paper is concerned with a new variant of bin packing called priority-based bin packing with subset constraints (PBBP-SC). PBBP-SC is a variant of traditional bin packing (TBP). In a TBP instance, we are given a collection of items {t1,t2,…tn}, where each item ti has size si∈(0,1). The goal of TBP is to pack the items in as few unit-sized bins as possible. In a PBBP-SC instance, we are given a collection of n unit-size items and a collection of m bins of varying capacities. Associated with each item is a positive integer which is called its priority. The priority of an item indicates its importance in being included in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. The first problem we study is the feasibility problem. This is the problem of checking if there is a feasible assignment to a given PBBP-SC instance. The second problem we study is the priority maximization problem. This is the problem of finding a maximum priority assignment of an infeasible PBBP-SC instance. The third problem we study is the bin minimization problem. This is the problem of finding an assignment with the fewest number of bins to pack all of the items in a feasible PBBP-SC instance. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. In particular, we show that both the feasibility and priority maximization problems are solvable in polynomial time. We also show that the bin minimization problem is log-APX-complete and cannot be solved in time o(1.41m) unless the Strong Exponential Time Hypothesis fails. © 2023 Elsevier B.V., Defense Advanced Research Projects Agency, DARPA: HR001123S0001-FP-004, This research was supported in part by the Defense Advanced Research Projects Agency through grant HR001123S0001-FP-004 .

This paper examines how to minimize the energy consumption of a user equipment (UE) when transmitting short data payloads. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS), which requires additional pilot signals to be configured, to improve the channel conditions. The challenge is that the pilot signals increase the energy consumption and must be balanced against energy savings during data transmission. We derive a formula for the energy consumption, including both pilot and data transmission powers and the effects of imperfect channel state information and discrete phase-shifts. To shorten the pilot length, we propose dividing the RIS into subarrays of multiple elements using the same reflection coefficient. The pilot power and subarray size are tuned to the payload length to minimize the energy consumption. Analytical results show that there exists a unique energy-minimizing solution. For small payloads and when the direct path loss between the BS and UE is weak compared to the path loss via the RIS, the solution is using subarrays with many elements and low pilot power and vice versa. The optimal percentage of energy spent on pilot signaling is in the order of 10-40%. IEEE

The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available computational power. While recent research has made significant strides in developing pruning methods to build a sparse network for reducing the computing overhead of DNNs, there remains considerable accuracy loss, especially at high pruning ratios. We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. The main reason is that the current methods do not support sparse architectures in their search space and use a search objective that is made for dense networks and does not focus on sparsity.This paper proposes a new method to search for sparsity-friendly neural architectures. It is done by adding two new sparse operations to the search space and modifying the search objective. We propose two novel parametric SparseConv and SparseLinear operations in order to expand the search space to include sparse operations. In particular, these operations make a flexible search space due to using sparse parametric versions of linear and convolution operations. The proposed search objective lets us train the architecture based on the sparsity of the search space operations. Quantitative analyses demonstrate that architectures found through DASS outperform those used in the state-of-the-art sparse networks on the CIFAR-10 and ImageNet datasets. In terms of performance and hardware effectiveness, DASS increases the accuracy of the sparse version of MobileNet-v2 from 73.44% to 81.35% (+7.91% improvement) with a 3.87× faster inference time.

The impact of the enhanced heat transfer performance of longitudinal fins on the entire process of melting and solidification in a phase change heat storage unit is investigated through experiments and numerical simulations in this paper. The position and structure of the longitudinal fins are optimized by using the response surface method while ensuring that the total volume of the heat storage medium remains unchanged. Studies have identified that some challenging zones in melting and solidification significantly impact the heat transfer of the entire heat storage cycle. Specifically, the challenging zone in melting primarily exists in the lower part of the unit during the charing process, while the challenging zone in solidification is mainly distributed around the unit during the discharging process. Through optimization, the optimized structure (fin spacing is 22.5 mm, fin width is 6.05 mm) can reduce the charging and discharging time by 16.94 % and 45.90 %, respectively. Additionally, the round trip time is significantly reduced by 39.19 %, and the mean heat absorption rate during the melting process is enhanced by 20.28 %. Moreover, the mean heat release rate during solidification is enhanced by 80.23 %.

Due to photovoltaic (PV) technology advantages as a clean, secure, and pollution-free energy source, PV power plants installation have shown an essential role in the energy sector. Nevertheless, the PV power plant cost of energy must be competitive when compared to traditional energy sources. Therefore, numerous studies are continuously being conducted aiming to optimize PV power plants, including components arrangements within the installation site, the inverter topology, cables, PV modules and inverters numbers, PV module tilt angle and shading effect. For selecting the most suitable combinations for system parameters, this study seeks to systematically analyze and synthesize the design of the PV power plant optimization from the current literature. The study also examines component sizing for PV power plants, involving PV modules tilt angle, inverter, transformer, and cables. Moreover, it provides an overview of the main components employed to install the PV power plant, which includes PV modules, inverter, transformer and wiring. It examines the different inverter topologies used in PV power plants along with a comparison between these topologies.

Convolutional Neural Networks (CNNs) have received great attention in the computer vision domain. However, CNNs are vulnerable to adversarial attacks, which are manipulations of input data that are imperceptible to humans but can fool the network. Several studies tried to address this issue, which can be divided into two categories: (i) training the network with adversarial examples, and (ii) optimizing the network architecture and/or hyperparameters. Although adversarial training is a sufficient defense mechanism, they suffer from requiring a large volume of training samples to cover a wide perturbation bound. Tweaking network activation functions (AFs) has been shown to provide promising results where CNNs suffer from performance loss. However, optimizing network AFs for compensating the negative impacts of adversarial attacks has not been addressed in the literature. This paper proposes the idea of searching for AFs that are robust against adversarial attacks. To this aim, we leverage the Simulated Annealing (SA) algorithm with a fast convergence time. This proposed method is called SARAF. We demonstrate the consistent effectiveness of SARAF by achieving up to 16.92%, 18.3%, and 15.57% accuracy improvement against BIM, FGSM, and PGD adversarial attacks, respectively, over ResNet-18 with ReLU AFs (baseline) trained on CIFAR-10. Meanwhile, SARAF provides a significant search efficiency compared to random search as the optimization baseline.

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.

Over the years, a range of Internet of Vehicles services has emerged, along with improved quality parameters. However, the field still faces several limitations, including resource constraints and the time response requirement. This paper extracts cost, energy, processing power, service management, and resource allocation parameters. Mathematical equations are then defined based on these parameters. To simplify the process complexity and ensure scalability, we propose an algorithm that uses the genetic algorithm for fault and cost management during resource allocation to services. The main concept is to pick resources for services using a genetic algorithm. We discuss the processing and energy costs associated with this function, which is the algorithm's objective function and is created to optimize cost. Our approach goes beyond the conventional genetic algorithm in two stages. In the first step, services are prioritized, and resources are allocated in accordance with those priorities; in the second step, load balancing in message transmission paths is ensured, and message failures are avoided. The algorithm's performance is evaluated using various parameters, and it was shown to outperform other metaheuristic algorithms like the classic genetic algorithm, particle swarm, and mathematical models. Different scenarios with various nodes and service variables are defined in various system states, including fault occurrences to various percentages of 10, 20, and 30. To compare methods, we consider different parameters, the most significant being performance success rate. Moreover, the cost optimization has a good convergence after iterations, and the rate of improvement in the big scenario has slowed down after 150 iterations. Besides, it provides acceptable performance in response time for services.

The ongoing transformation of manufacturing industry into digitalized production, Industry 4.0, has put new perspectives on the maintenance of production systems. The technologies offer an array of new possibilities in optimization of maintenance and data driven decision making. On the other hand, these new technologies offer a lot of challenges in form of investment costs, need for new competences, and how to handle the equipment legacy, i.e. upgrading old equipment. Many researchers associate data driven decision making with intelligent sensors, cloud computing and cyber physical systems, but are these technologies the most cost-effective way of achieving data driven maintenance? The aim of this paper is to discuss how manufacturing industry should approach smart maintenance in order to improve the industry’s competitiveness, rather than spending money on technology that doesn’t contribute. The basis for the discussion will mainly be a literature study but additional empirical data may be included.

This thesis is composed of five papers, which all attempt to optimise calculations of scattering amplitudes in high-energy-physics collisions. These scattering amplitudes are a key part of theoretical predictions for particle-physics experiments like the Large Hadron Collider at CERN. The first four papers are the main topic of the thesis, and describe a novel method called chirality flow. Chirality flow simplifies Feynman-diagram calculations and makes them more intuitive. Papers I, II, and IV describe chirality flow in detail at both tree-level and one-loop level, while paper III shows a first implementation of it in the event generator MadGraph5_aMC@NLO. The final paper instead explores the speed, accuracy, and precision of an approximation of the colour part of a scattering amplitude.Paper I introduces the chirality-flow formalism, a new pictorial method used to calculate tree-level helicity amplitudes by drawing lines and connecting them to find spinor inner products, instead of doing algebraic manipulations. This method makes calculations more transparent, and often allows one to go from Feynman diagram to spinor inner products in a single line. Massless QED and QCD are treated in full. Paper II extends the chirality-flow formalism of paper I to deal with massive particles, and therefore allows chirality flow to be used for any tree-level Standard Model calculation. Paper III describes our implementation of chirality flow in massless QED in MadGraph5_aMC@NLO. A speed comparison is made showing up to a factor of 10 increase in evaluation speed. Paper IV extends the chirality-flow formalism to the one-loop level for any Standard Model calculation, showing the simplifications in the numerator algebra and the tensor reduction. Paper V describes an extension to the MadGraph5_aMC@NLO event generator in which the kinematics are calculated using Berends-Giele recursions instead of Feynman diagrams, and the colour matrix can be expanded in the number of colours Nc. The

The optimal placement of virtual network functions (VNFs) improves the overall performance of servicefunction chains (SFCs) and decreases the operational costs formobile network operators. To cope with changes in demands,VNF instances may be added or removed dynamically, resourceallocations may be adjusted, and servers may be consolidated.To maintain an optimal placement of SFCs when conditionschange, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, suchreconfigurations may lead to stress on the VNF infrastructure,which may cause service degradation. On the other hand, notchanging the placement may lead to suboptimal operation,and servers and links may become congested or underutilized,leading to high operational costs. In this paper, we investigatethe trade-off between the reconfiguration of SFCs and theoptimality of their new placement and service-flow routing. Wedevelop a multi-objective genetic algorithm that explores thePareto front by balancing the optimality of the new placementand the cost to achieve it. Our numerical evaluations show thata small number of reconfigurations can significantly reduce theoperational cost of the VNF infrastructure. In contrast, toomuch reconfiguration may not pay off due to high costs. Webelieve that our work provides an important tool that helpsnetwork providers to plan a good reconfiguration strategy fortheir service chains.