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Proposing new metaheuristic methods is crucial for continuous algorithmic improvement and the ability to effectively address increasingly complex real-world optimization problems. On the other hand, Evolutionary Game Theory analyzes how trough competition is possible to modify the strategies of individuals within a population in order to spread successful mechanisms and reduce or remove less successful strategies. This paper introduces a novel optimization approach based on the principles of evolutionary game theory. In the proposed method, all individuals are initialized using the Metropolis–Hasting technique, which sets the solutions at a starting point closer to the optimal or near-optimal regions of the problem. An original strategy is then assigned to each individual in the population. By considering the interactions and competition among different agents in the optimization problem, the approach modifies the strategies to improve search efficiency and find better solutions. To evaluate the performance of the proposed technique, it is compared with eight well-known metaheuristic algorithms using 30 benchmark functions. The proposed methodology demonstrated superiority in terms of solution quality, dimensionality, and convergence when compared to other approaches. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, an improved multiobjective particle swarm optimization (PSO) algorithm is proposed to optimize a three-phase, 12-slot, 19-pole, yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. Design/methodology/approach: Based on the structural characteristics of the YASA-AFFSPM, a mathematical model is established to calculate the main size of the YASA-AFFSPM motor. The split ratio, stator axial length, sandwiching pole angle, rotor pole angle, PM arc and number of conductors per slot are selected as optimization variables. Also, the efficiency, power factor, cogging torque and average torque are considered as the optimization objectives. The objectives are optimized by combining the improved multiobjective PSO algorithm with electromagnetic calculation. Findings: Based on the proposed algorithm, the investigated motor is optimized. The on-load efficiency, power factor and average torque of the motor performance have increased by 0.87%, 3.13% and 10.39%, respectively. Moreover, the cogging torque and slot fill factor have undergone decreases of 8.57% and 3.34%, respectively. Finally, the effectiveness of the algorithm is verified using experiment results. Originality/value: So far, no comprehensive report has been observed on the optimization of the YASA-AFFSPM motor using evolutionary algorithms and the study of the effect of the motor parameters. Therefore, in this paper, the authors decided to investigate the effect of YASA-AFFSPM motor parameters and improve motor performance with the improved PSO method. [ABSTRACT FROM AUTHOR]

This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimization stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimization outcomes. • Proposed Gooseneck Barnacle Optimizer mimics gooseneck barnacles' mating behaviour. • Inspiration: sperm casting, wind direction, intertidal wave action during mating. • Address the issues with original Barnacle Mating Optimizer. • Standard benchmark functions evaluate GBO's performance. • Hybridize with Least Square Support Vector Machine for real time-series forecasting. [ABSTRACT FROM AUTHOR]

In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush–Kuhn–Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems. [ABSTRACT FROM AUTHOR]

Many real-life problems can be formulated as numerical optimization problems. Such problems pose a challenge for researchers when designing efficient techniques that are capable of finding the desired solution without suffering from premature convergence. This paper proposes a novel evolutionary algorithm that blends the exploitative and explorative merits of two main evolutionary algorithms, namely the Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. This amalgam has an effective interaction and cooperation of an ensemble of diverse strategies to derive a single framework called Iterative Cyclic Tri-strategy with adaptive Differential Stochastic Fractal Evolutionary Algorithm (Ic3-aDSF-EA). The component algorithms cooperate and compete to enhance the quality of the generated solutions and complement each other. The iterative cycles in the proposed algorithm consist of three consecutive phases. The main idea behind the cyclic nature of Ic3-aDSF-EA is to gradually emphasize the work of the best-performing algorithm without ignoring the effects of the other inferior algorithm during the search process. The cooperation of component algorithms takes place at the end of each cycle for information sharing and the quality of solutions for the next cycle. The algorithm's performance is evaluated on 43 problems from three different benchmark suites. The paper also investigates the application to a set of real-life problems. The overall results show that the proposed Ic3-aDSF-EA has a propitious performance and a reliable scalability behavior compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network. Design/methodology/approach: The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model. Findings: The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation. Originality/value: There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered. Highlights: Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm). [ABSTRACT FROM AUTHOR]

This paper presents a numerical investigation of the spectral conjugate directions formulation for optimizing unconstrained problems. A novel modified algorithm is proposed based on the conjugate gradient coefficient method. The algorithm employs the Wolfe inexact line search conditions to determine the optimum step length at each iteration and selects the appropriate conjugate gradient coefficient accordingly. The algorithm is evaluated through several numerical experiments using various unconstrained functions. The results indicate that the algorithm is highly stable, regardless of the starting point, and has better convergence rates and efficiency compared to classical methods in certain cases. Overall, this research provides a promising approach to solving unconstrained optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel swarm intelligence optimizer inspired by human behavior called the Special Forces Algorithm (SFA). SFA is inspired by the missions of modern Special Forces in real life. First of all, in the initial stage of algorithm iteration, the speed of light mechanism is proposed for simulating the UAV-assisted search of special forces, and the loss probability mechanism is proposed for simulating the loss of contact due to force majeure during the search of special forces. The algorithm can more fully explore the solution space and enhance the ability of an algorithm to jump out of a local optimum. Secondly, by simulating the combat tactics of special forces, the algorithm uses command parameters to control the three stages of large-scale search, transitional stage search and small-scale precise search, making the balance between the exploration stage and the exploitation stage more reasonable. Finally, SFA inspired by special forces operations are presented. To verify the effectiveness of the proposed algorithm, SFA is compared with 6 classical algorithms in 23 benchmark functions. In addition, the algorithm is applied to practical engineering problems. Experimental results show that SFA has shown great potential and competitive results. SFA can achieve good search performance and optimization accuracy based on a better balance of exploration and exploitation capabilities. • A new swarm intelligence optimizer called SFA is proposed. • An index Instruction which is used to change exploration and exploitation phases is created. • Three novel search mechanisms are designed. • Thresholds have been designed during the optimization operation. [ABSTRACT FROM AUTHOR]

Purpose: Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information. Design/methodology/approach: This paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA. Findings: The performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively. Originality/value: This paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction. [ABSTRACT FROM AUTHOR]

Solving the convergence problem in the world of computing is needed to find an optimization solution. Many researchers propose a meta-heuristic approach in solving problems quickly using the results of the approach. This method is used because of its flexibility and fast search rate. Each meta-heuristic method has its own characteristics and is not necessarily able to solve the problem for all cases. This paper proposes a meta-heuristic method that is inspired by the way in which leaders are chosen in the chimpanzee community. The leader in the chimpanzee community is always the alpha male chimpanzee and has relationships with all chimps, both male and female. The principle of Chimpanzee Leader Election Optimization (CLEO) is the phase of improving performance and social relations with community members. The chimp performance improvement phase is based on the age aspect, which affects the memorize's ability of the chimps, which are inverted U-shaped. Meanwhile, the social relations/mating phase has been analogized in this CLEO algorithm. This paper has also evaluated the performance of the proposed algorithm, both to find the optimal value in the minimum and maximum search. This algorithm has also been compared with various other meta-heuristic algorithms. The results of the CLEO algorithm are quite competitive with other meta-heuristic algorithms in multi-high dimensional. [Display omitted] • Meta-heuristic. • Optimization. • Computational Modeling. [ABSTRACT FROM AUTHOR]

This paper considers the distributed prescribed-time optimization problem of multi-agent systems (MASs). Considering the strongly convex function of time-invariant for each agent, the two-stage distributed prescribed-time optimization algorithm is designed based on the idea of zero-gradient-sum. Meanwhile, in order to save system resources, the event-triggered control mechanism is introduced into the algorithm in this paper. In the first stage, the distributed prescribed-time event-triggered algorithm is proposed to minimize the local objective functions of each agent at the prescribed-time interval. In the second stage, the algorithm is driven to optimize the global cost function while maintaining the gradient sum of all local cost functions to zero. The criteria for achieving the consensus and optimization of MASs are obtained by using Lyapunov stability theory and optimization theory. Moreover, it is proved in detail that using the two triggering functions will not result in Zeno behavior. The numerical example is given to demonstrate the correctness of the theoretical analysis and the effectiveness of the control algorithms. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been widely used to solve different optimization problems. There have always been ongoing efforts to develop new and efficient algorithms. In this paper, the Orchard Algorithm (OA) is designed and introduced, inspired by fruit gardening. In this process, various actions such as irrigation, fertilization, trimming, and grafting lead to a fruit orchard where most trees grow and produce fruit adequately. In OA, both explorations of the search space and exploitation of the best solutions are achieved using personal and social behavior. By introducing various operators such as annual growth, screening, and grafting, the algorithm can efficiently search and explore the search space. The performance of the proposed OA algorithm was evaluated on CEC2005, IEEE CEC06 2019,test functions, and five real-world engineering problems compared with 13 widely used and competitive algorithms. Thirty benchmark functions were used to compare the capabilities of the OA algorithm with other research. The OA yields far better results in many aspects than the other algorithms. The results show the OA's superiority and this algorithm's capability in solving optimization problems. • We proposed new meta-heuristic algorithm for solving optimization problems. • The orchard algorithm is introduced, which is inspired by fruit gardening process. • In OA, using personal and social behavior, exploration and exploitation are achieved. • At 60 test functions, OA performed better than other algorithms. • The orchard algorithm performed very well in five engineering benchmark problems. [ABSTRACT FROM AUTHOR]

Using reinforcement learning (RL) algorithm to optimize guidance law can address non-idealities in complex environment. However, the optimization is difficult due to huge state-action space, unstable training, and high requirements on expertise. In this paper, the constrained guidance policy of a neural guidance system is optimized using improved RL algorithm, which is motivated by the idea of traditional model-based guidance method. A novel optimization objective with minimum overload regularization is developed to restrain the guidance policy directly from generating redundant missile maneuver. Moreover, a bi-level curriculum learning is designed to facilitate the policy optimization. Experiment results show that the proposed minimum overload regularization can reduce the vertical overloads of missile significantly, and the bi-level curriculum learning can further accelerate the optimization of guidance policy. [ABSTRACT FROM AUTHOR]

Residual block is a very common component in recent state-of-the art CNNs such as EfficientNet/EfficientDet. Shortcut data accounts for nearly 40% of feature-maps access in ResNet152. Most of the previous DNN compilers/accelerators ignore the shortcut data optimization. This paper presents ShortcutFusion, an optimization tool for FPGA-based accelerator with a reuse-aware static memory allocation for shortcut data, to maximize on-chip data reuse given resource constraints. From TensorFlow DNN models, the proposed design generates instruction sets for a group of nodes which uses an optimized data reuse for each residual block. The accelerator design implemented on the Xilinx KCU1500 FPGA card $2.8\times $ faster and $9.9\times $ more power efficient than NVIDIA RTX 2080 Ti for $256\times 256$ input size. Compared to the result from baseline, in which the weights/inputs/outputs are accessed from the off-chip memory exactly once per each layer, ShortcutFusion reduces the DRAM access by 47.8-84.8% for RetinaNet, Yolov3, ResNet152, and EfficientNet. Given a similar buffer size to ShortcutMining, which also “mine” the shortcut data in hardware, the proposed work reduces off-chip access for feature-maps $5.27\times $ while accessing weight from off-chip memory exactly once. [ABSTRACT FROM AUTHOR]

The main focus of this paper is the presentation of reliable methods for the determination of the optimum coloring of a graph, commonly known in the literature as vertex coloring problem. It has been shown that networks of capacitively coupled oscillators can be used to solve vertex coloring problems. In this paper we address the negative impact of an unbalanced number of couplings for the oscillators on the performance of the network and compensate for this non-uniform coupling structure by an adjustment in the network itself. The negative effect of the memristor device-to-device variability of the NbOx memristor on the array functionality will be investigated and reduced via an adaptation of the memristor operating point. The main improvement in network performance is achieved by setting up a control procedure allowing the network to bypass the local solutions and converge to the global one. Two strategies inspired by global optimization algorithms will be proposed to allow the network to overcome sub-optimal solutions, and find the solution corresponding to the absolute minimum of a performance measure function of the vertex coloring problem. [ABSTRACT FROM AUTHOR]

The paper presents a new way of classifying numerical sets (division into abstraction classes, called cuts hereafter in this paper) after imposing a constraint on the sum of elements of their subsets. A simple and not too expensive operation of sorting a set (with maximum n log (n) , sorting by merge sort) allowed to determine pairs of subsets (which shall be described in the paper as pairs in a solid compound) representing a given set with the accepted constraint, which in turn allowed to prove various properties and limit the number of necessary comparisons. Then, the properties of the ratio of the sum of elements to the constraint within its cut were demonstrated, and to this cut (the abstraction class) an interval on the real number axis was assigned. To illustrate the obtained results, a mutation tree is used as an image of a unit hypercube. For introduced concepts, for example, their application for the classical number partitioning problem is shown, which is NP-complete according to the classification (which was proved by Richard Karp in 1972 in his work [1] and classified it as a subproblem of the knapsack problem). [ABSTRACT FROM AUTHOR]

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems. [ABSTRACT FROM AUTHOR]

For the design of ring oscillator-based ADCs, little has been reported on how to optimally co-design the readout scheme and the ring oscillator core towards optimal energy efficiency. This paper describes a methodology to find this optimum for a target SQNR and signal bandwidth, for the case of 1st-order quantization noise shaping. In short, starting from initial assumptions on the VCO, the readout optimization boils down to finding the best VCO frequency. From this, the number of readout phases, the sampling frequency of the readout and the number of bits in the quantizers are derived. Then, it is explained how to adjust the VCO core toward the corresponding optimal VCO configuration. This is discussed for the case of a current-controlled ring oscillator, indicating the main constraints that define the design space for such a VCO optimization. If the actual VCO configuration is bounded by practical constraints, this approach can be re-iterated where a new readout optimization is performed taking these constraints into account. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to focus on determining the optimal sales price for non-instantaneous deterioration items according to consideration of freshness and demand. Design/methodology/approach: In this model, the authors have described the demand function which is dependent on price as well time. The products that the deterioration is considered as non-instantaneous have a determinate shelf life, and their demand rate will decrease over time after the beginning of the selling period. This paper depicts that the total profit of non-instantaneous deterioration items using the dynamic pricing strategy is higher than that using fixed pricing strategy. Findings: Finally, to illustrate and validate the model, the authors have used some numerical examples. A new freshness function and the model to study pricing policy are developed as well applied to solve managerial decision problems. Originality/value: This paper complements the lack of the existing theoretical research of pricing for non-instantaneous deterioration items under an e-commerce environment. A new freshness function and the model to study pricing policy are developed as well applied to solve managerial decision problems. [ABSTRACT FROM AUTHOR]

Template matching (TM) is a technique used in computer vision that helps us to identify small pieces of a predefined sub image known as template and a corresponding area inside a large image. There are several approaches in the literature that solve this problem. In spite of their acceptable results, the existing methods fail to detect templates when they are rotated in a large image. On the other hand, the structural similarity (SSIM) index is a resemblance model that allows us to evaluate the affinity between two images. Different from other similarity indexes, SSIM considers important structural information while also incorporating essential elements such as luminance and contrast terms. This paper presents a new approach for template matching when an image is rotated. Our method considers the complete process as an optimization problem where a metaheuristic algorithm is used to find the best resemblance between the template and its rotated or unrotated image. In order to obtain a robust detection, our approach considers the use of the SSIM index as an objective function. Although metaheuristic approaches produce interesting results, there is not an ideal algorithm that can competitively solve all problems. Under such conditions, our study analyzes the performance of five different metaheuristic techniques for solving the problem of TM. In the comparison, several complicated computer experiments have been conducted considering different performance indexes. The results show that the proposed technique solves the problem of TM competitively for rotated instances in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Purpose: Aerodynamic shape optimisation is complex because of the high dimensionality of the problem, the associated non-linearity and its large computational cost. These three aspects have an impact on the overall time of the design process. To overcome these challenges, this paper aims to develop a method for transonic aerodynamic design with dimensionality reduction and multifidelity techniques. Design/methodology/approach: The developed methodology is used for the optimisation of an installed civil ultra-high bypass ratio aero-engine nacelle. As such, the effects of airframe-engine integration are considered during the optimisation routine. The active subspace method is applied to reduce the dimensionality of the problem from 32 to 2 design variables with a database compiled with Euler computational fluid dynamics (CFD) calculations. In the reduced dimensional space, a co-Kriging model is built to combine Euler lower-fidelity and Reynolds-averaged Navier stokes higher-fidelity CFD evaluations. Findings: Relative to a baseline aero-engine nacelle derived from an isolated optimisation process, the proposed method yielded a non-axisymmetric nacelle configuration with an increment in net vehicle force of 0.65% of the nominal standard net thrust. Originality/value: This work investigates the viability of CFD optimisation through a combination of dimensionality reduction and multifidelity method and demonstrates that the developed methodology enables the optimisation of complex aerodynamic problems. [ABSTRACT FROM AUTHOR]

This paper designs and simulates a circular heatsink (HSK) with a novel geometry. A number of cylindrical pin-fins (PIFs) are placed on the HSK. The flow of alumina/water nanofluid (NF) enters from the middle of the HSK, passes through the PIFs, goes toward the outer curvature, and exits from the HSK. Constant thermal flux is applied at the bottom of the HSK. The variables include the length of PIFs changing from 5 to 20 mm, the distance between PIFs varying from 10 to 15 mm, and the diameter of PIFs changing from 1 to 4 mm. The effect of these variables on the maximum of the HSK, the average temperature (T-AV) of the HSK is examined. Finally, numerical optimization is done on the results using machine learning and artificial intelligence in terms of the minimum HSK temperature. The flow of NF is simulated using a two-phase model and the finite element method (FEM). An increment in the length of the PIFs from 5 to 20 reduces the T-AV by 11.42 K. An increment in the diameter of the PIFs from 1 to 4 reduces the T-AV of the HSK by 5.98 K. [ABSTRACT FROM AUTHOR]

This paper deals with the frequency design of lossless passive electronic filters under magnitude constraints. With the huge increase in design complexity for mobile applications, new systematic and efficient methods are required. This paper focuses on the direct synthesis approach, an historical design approach that has not been recently updated. It consists in directly synthesizing the $LC$ values of a pre-specified circuit until the spectral mask is satisfied. While beneficial in practice, this approach typically leads to an important computational time and requires an initial guess to reduce it. Based on recent developments of the System and Control community, that led to efficient methods for system design, the direct synthesis approach is revisited. To achieve this, the port-Hamiltonian Differential Algebraic Equation (pHDAE) representation, that particularly fits the design problem, is introduced. A synthesis method is then developed, leading to solve an optimization problem of moderate complexity. For particular cases, this complexity happens to be remarkably low. Based on this observation, a second method reveals how to obtain such complexity for the more general case, using an original combination between the pHDAE and the LFT representations. Finally, a numerical example shows the validity and illustrates the benefits of this work. [ABSTRACT FROM AUTHOR]

The memristor-based neuromorphic computing system (NCS) with emerging storage and computing integration architecture has drawn extensive attention. Because of the unique nonvolatility and programmability, the memristor is an ideal nano-device to realize neural synapses in VLSI circuit implementation of neural networks. However, in the hardware implementation, the performance of the memristive neural network is always affected by quantization error, writing error, and conductance drift, which seriously hinders its applications in practice. In this paper, a novel weight optimization scheme combining quantization and Bayesian inference is proposed to alleviate this problem. Specifically, the weight deviation in the memristive neural network is transformed into the weight uncertainty in the Bayesian neural network, which can make the network insensitive to unexpected weight changes. A quantization regularization term is designed and utilized during the training process of the Bayesian neural network, reducing the quantization error and improving the robustness of the network. Furthermore, a partial training method is raised to extend the applicability of the proposed scheme in large-scale neural networks. Finally, the experiments on a Multilayer Perceptron and LeNet demonstrate that the proposed weight optimization scheme can significantly enhance the robustness of memristive neural networks. [ABSTRACT FROM AUTHOR]

In this paper, we revisit Boole’s expansion theorem to propose a new synthesis method for implication logic circuits based on memristors. By rewriting the sum-of-products form of Boole’s expansion theorem in terms that are best suited for the implication logic, we develop a generalized rule to derive the sequence of operations needed to realize any logic function written in the classical AND-OR form. The proposed method leverages on multi-input operation, minimizing both the number of steps required to compute a given Boolean function and the number of memristors involved. Moreover, it allows using well-established methods of logic circuit optimization like binary decision diagrams, Karnaugh maps, and heuristic algorithms, that are already implemented in commercial CAD software. The proposed method allows a fair comparison between the performance of CMOS and implication logic implementations of the same logic function under the same degree of optimization, and is shown to outperform existing approaches. Possible device-circuit co-design strategies to optimize circuit performance are finally discussed. [ABSTRACT FROM AUTHOR]

To better understand the correlation between network topological features and the robustness of network controllability in a general setting, this paper suggests a practical approach to searching for optimal network topologies with given numbers of nodes and edges. Since theoretical analysis seems impossible at least in the present time, exhaustive search based on optimization techniques is employed, firstly for a group of small-sized networks that are realistically workable, where exhaustive means 1) all possible network structures with the given numbers of nodes and edges are computed and compared, and 2) all possible node-removal sequences are considered. A main contribution of this paper is the observation of an empirical necessary condition (ENC) from the results of exhaustive search, which shrinks the search space to quickly find an optimal solution. ENC shows that the maximum and minimum in- and out-degrees of an optimal network structure should be almost identical, or within a very narrow range, i.e., the network should be extremely homogeneous. Edge rectification towards the satisfaction of the ENC is then designed and evaluated. Simulation results on large-sized synthetic and real-world networks verify the effectiveness of both the observed ENC and the edge rectification scheme. As more operations of edge rectification are performed, the network is getting closer to exactly satisfying the ENC, and consequently the robustness of the network controllability is enhanced towards optimum. [ABSTRACT FROM AUTHOR]

• Propose an interval biform game model to handle both strategy choice and coalition formation. • Develop an interval weighted equal contribution division method to solve cooperative games. • Identify existence and necessary conditions of pure strategy Nash equilibriums. • Numerical examples are presented to illustrate the proposed method. This paper develops an interval noncooperative-cooperative biform game (referred to as the interval biform game in short hereafter) model that accounts for both strategy choice and coalition formation while also addressing profit allocation with interval coalition characteristic values. This interval biform game consists of four elements: a finite set of players, their strategy choices, coalitions, and interval coalition characteristic functions, and contains both noncooperative and cooperative parts. The payoffs of different strategies are not directly given a priori in the noncooperative part but obtained by solving interval cooperative games in the cooperative part. To solve the interval cooperative game, we extend a real-valued weighted equal contribution division (WECD) approach to an interval-valued version. Subsequently, an interval acceptability degree comparison method is introduced to compare the interval payoff values. We then establish existence and necessary conditions of pure strategy Nash equilibriums and derive a condition for a Nash equilibrium to be efficient in an interval biform game. Finally, numerical examples are furnished to illustrate the validity and applicability of the proposed framework. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to analyze the impacts of combined utilization of multi-jet impinging cooling of nanofluids with magnetic field and porous layer on the cooling performance, as effective cooling with impinging jets are obtained for various energy systems, including photovoltaic panels, electronic cooling and many other convective heat transfer applications. Design/methodology/approach: Finite element method is used to explore the magnetic field effects with the inclusion of porous layer on the cooling performance efficiency of slot nanojet impingement system. Impacts of pertinent parameters such as Reynolds number (Re between 250 and 1,000), strength of magnetic field (Ha between 0 and 30), permeability of the porous layer (Da between 0.001 and 0.1) on the cooling performance for flat and wavy surface configurations are explored. Findings: It is observed that the average Nusselt number (Nu) rises by about 17% and 20.4% for flat and wavy configuration while temperature drop of 4 K is obtained when Re is increased to 1,000 from 250. By using magnetic field at the highest strength, the average Nu rises by about 29% and 7% for flat and wavy cases. Porous layer permeability is an effective way of controlling the cooling performance while up to 44.5% variations in the average Nu is obtained by varying its value. An optimization routine is used to achieve the highest cooling rate while the optimum parameter set is obtained as (Re, Ha, Da, γ, sx) = (1,000, 30, 0.07558, 86.28, 2.585) for flat surface and (Re, Ha, Da, γ, sx) = (1,000, 30, 0.07558, 71.85, 2.329) for wavy surface configurations. Originality/value: In thermal systems, cooling system design is important for thermal management of various energy systems, including fuel cells, photovoltaic panels, electronic cooling and many others. Impinging jets are considered as effective way of cooling because of its ability to give higher local heat transfer coefficients. This paper offers novel control tools, such as magnetic field, installation of porous layer and hybrid nano-liquid utilization for control of cooling performance with multiple impinging jets. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to experimentally and numerically investigate the cooling performance of the air-to-water thermoelectric cooling system under different working conditions. Design/methodology/approach: An air-to-water thermoelectric cooling system was designed and manufactured according to the principle of discrete binary thermoelectric Peltier modules, and the thermal performance, heat transfer rate and average COP values were examined at different cooling water temperatures and voltages applied. Additionally, numerical simulations were performed by computational fluid dynamics approach to investigate the temperature distribution and airflow structure inside the cooling chamber. Findings: Analyses were performed using experimental tests and numerical methods. It was concluded that, by decreasing the cooling water temperature from 20 to 5 °C, the average COP increases about 36%. The voltage analysis showed that the efficiency of the system does not always increase as the voltage rises; more importantly, the optimum voltage is different and depends on whether it is desired to increase COP or increase the cooling rate. Originality/value: In the studies published in the field of thermoelectric cooling systems, little attention has been paid to the voltage applied and its relationship to other operating conditions. In most cases, the tests are performed at a constant voltage. In this study, several options, including applied voltage and cooling water temperature, were considered simultaneously and their effects on performance have been tested. It was found that under such studies, optimization work should be done to evaluate maximum performance in different working conditions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to compare stochastic gradient method used for neural network training with global optimizer without use of gradient information, in particular differential evolution. Design/methodology/approach: This contribute shows the application of heuristic optimization algorithms to the training phase of artificial neural network whose aim is to predict renewable power production as function of environmental variables such as solar irradiance and temperature. The training problem is cast as the minimization of a cost function whose degrees of freedom are the parameters of the neural network. A differential evolution algorithm is substituted to the more usual gradient-based minimization procedure, and the comparison of their performances is presented. Findings: The two procedures based on stochastic gradient and differential evolution reach the same results being the gradient based moderately quicker in convergence but with a lower value of reliability, as a significant number of runs do not reach convergence. Research limitations/implications: The approach has been applied to two forecasting problems and, even if results are encouraging, the need for extend the approach to other problems is needed. Practical implications: The new approach could open the training of neural network to more stable and general methods, exploiting the potentialities of parallel computing. Originality/value: To the best of the authors' knowledge, the research presented is fully original for the part regarding the neural network training with differential evolution. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithms, which are developed for solving complex optimization problems designed by focusing on simulating the social behavior of one species of simple animals. However, simple animals utilize cooperation to work together that result in more complex and smarter behaviors. This paper proposes a novel population-based optimization paradigm for solving NP-hard problems called "Aphid–Ant Mutualism (AAM)" which is inspired by a unique relationship between aphids and ants' species. This relationship is called 'mutualism'. Despite the previous studies that the social behaviors of aphids and ants were simulated, AAM models mutual interaction among aphids and ants in nature. Thus, AAM has new features by incorporating heterogeneous individuals consisting of aphids and ants that live in various colonies together and have different decentralized learning behaviors and objectives. Inspired by nature, colony-based information exchange and using different search strategies including focusing on the individual's personal knowledge, learning from other colony's members and information sharing with adjacent colonies are used. This mutualism leads to converging to the global optimum and avoids premature convergence. Performance of AAM is assessed using statistical evaluation, convergence analysis, and a non-parametric Wilcoxon rank-sum test with a 5% significance degree on forty-one benchmarks selected from well-known functions of recent studies and more challenging benchmark functions called CEC 2014, CEC 2017 and also CEC-C06 2019 test suite. Statistical results and comparisons with other meta-heuristic algorithms demonstrate that the AAM algorithm provides promising and competitive outcomes. Furthermore, it can produce more accurate solutions with a faster convergence rate to the global optima. • A novel population-based metaheuristic algorithm is developed which is called AAM. • AAM is inspired by interaction between aphids' and ants' species called "Mutualism". • AAM has new features by including heterogeneous individuals that live in the colonies. • AAM has few free parameters that make it versatile for diverse optimization problems. • Numerical simulations show that AAM outperforms six other comparative algorithms. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to discuss inverse problems that arise in a variety of practical thermal processes and systems. It presents some of the approaches that may be used to obtain results that lie within a small region of uncertainty. Therefore, the non-uniqueness of the solution is reduced so that the final design and boundary conditions may be determined. Optimization methods that may be used to reduce the uncertainty and to select locations for experimental data and for minimizing the error are presented. A few examples of thermal systems are given to illustrate the applicability of these methods and the challenges that must be addressed in solving inverse problems. Design/methodology/approach: In most analytical and numerical solutions, the basic equations that describe the process, as well as the relevant and appropriate boundary conditions, are known. The interest lies in obtaining a unique solution that satisfies the equations and boundary conditions. This may be termed as a direct or forward solution. However, there are many problems, particularly in practical systems, where the desired result is known but the conditions needed for achieving it are not known. These are generally known as inverse problems. In manufacturing, for instance, the temperature variation to which a component must be subjected to obtain desired characteristics is prescribed, but the means to achieve this variation are not known. An example of this circumstance is the annealing, tempering or hardening of steel. In such cases, the boundary and initial conditions are not known and must be determined by solving the inverse problem to obtain the desired temperature variation in the component. The solutions, thus, obtained are generally not unique. This is a review paper, which discusses inverse problems that arise in a variety of practical thermal processes and systems. It presents some of the approaches or strategies that may be used to obtain results that lie within a small region of uncertainty. It is important to realize that the solution is not unique, and this non-uniqueness must be reduced so that the final design and boundary conditions may be determined with acceptable accuracy and repeatability. Optimization techniques are often used for minimizing the error. This review presents several methods that may be applied to reduce the uncertainty and to select locations for experimental data for the best results. A few examples of thermal systems are given to illustrate the applicability of these methods and the challenges that must be addressed in solving inverse problems. By considering a variety of systems, the paper also shows the importance of solving inverse problems to obtain results that may be used to model and design thermal processes and systems. Findings: The solution of inverse problems, which frequently arise in thermal processes, is discussed. Different strategies to obtain the conditions that lead to the desired result are given. The goal of these approaches is to reduce uncertainty and obtain essentially unique solutions for different circumstances. The error of the method can be checked against known conditions to see if it is acceptable for the given problem. Several examples are given to illustrate the use of these methods. Originality/value: The basic strategies presented here for solving inverse problems that arise in thermal processes and systems, as well as the optimization techniques used to reduce the domain of uncertainty, are fairly original. They are used for certain challenging problems that have not been considered in detail earlier. Several methods are outlined for considering different types of problems. [ABSTRACT FROM AUTHOR]

This paper presents a transimpedance-to-noise optimization approach for design of a resistive shunt-feedback TIA. This optimization offers an enhancement in the transimpedance and a noise performance very close to the theoretical minimum noise of the TIA. In addition, the transimpedance-to-noise optimization approach results in a small front-end FET size which enables a further reduction in power and area. Moreover, this approach enables using a fewer number of stages in the receiver chain which makes a high PSRR feasible and obviates the necessity for using an offset cancellation circuitry. Building on this approach, a fully differential analog front-end including a resistive shunt-feedback TIA and a post amplifier (PA) for time-of-flight (ToF) Lidar receivers is designed and implemented, achieving 94dB Ω transimpedance gain, 71nA input-referred rms noise current, −3dB bandwidth of 340MHz, and power supply rejection ratio (PSRR) of more than 87dB in a 0.11 μm CMOS process. The associated DC power consumption is 19.4mW with V DD of 1.8V. Moreover, a push-pull buffer with 1V output swing is integrated for driving 50 Ω loads, such as off-chip time discriminators, which also additionally amplifies the signal with a gain of 5dB while consuming an extra 20.9mW of DC power. The whole chip (excluding pads) occupies 210 μm × 110 μm in area. [ABSTRACT FROM AUTHOR]

Recently, there have been many studies attempting to take advantage of advancements in Artificial Intelligence (AI) in Analog and Mixed-Signal (AMS) circuit design. Automated circuit sizing optimization and improving the accuracy of performance models are the two predominant uses of AI in AMS circuit design. This paper first introduces and explains the basic concepts in AI especially the ones that are more suitable to this application. Next, it surveys some recent studies of various AI techniques for AMS circuit design. Then, it discusses the main approaches as well as the pros and cons of each method. Finally, it gives meaningful insights about the current challenges and open issues, as well as recommends approaches for specific applications. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to improve transient response and dynamic performance of automatic voltage regulator (AVR). Design/methodology/approach: This paper proposes a novel fractional order proportional–integral–derivative plus derivative (PIλDµDµ2) controller called FOPIDD for AVR system. The FOPIDD controller has seven optimization parameters and the equilibrium optimizer algorithm is used for tuning of controller parameters. The utilized objective function is widely preferred in AVR systems and consists of transient response characteristics. Findings: In this study, results of AVR system controlled by FOPIDD is compared with results of proportional–integral–derivative (PID), proportional–integral–derivative acceleration, PID plus second order derivative and fractional order PID controllers. FOPIDD outperforms compared controllers in terms of transient response criteria such as settling time, rise time and overshoot. Then, the frequency domain analysis is performed for the AVR system with FOPIDD controller, and the results are found satisfactory. In addition, robustness test is realized for evaluating performance of FOPIDD controller in perturbed system parameters. In robustness test, FOPIDD controller shows superior control performance. Originality/value: The FOPIDD controller is introduced for the first time to improve the control performance of the AVR system. The proposed FOPIDD controller has shown superior performance on AVR systems because of having seven optimization parameters and being fractional order based. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to execute the efficiency analysis of the selected metaheuristic algorithms (MAs) based on the investigation of analytical functions and investigation optimization processes for permanent magnet motor. Design/methodology/approach: A comparative performance analysis was conducted for selected MAs. Optimization calculations were performed for as follows: genetic algorithm (GA), particle swarm optimization algorithm (PSO), bat algorithm, cuckoo search algorithm (CS) and only best individual algorithm (OBI). All of the optimization algorithms were developed as computer scripts. Next, all optimization procedures were applied to search the optimal of the line-start permanent magnet synchronous by the use of the multi-objective objective function. Findings: The research results show, that the best statistical efficiency (mean objective function and standard deviation [SD]) is obtained for PSO and CS algorithms. While the best results for several runs are obtained for PSO and GA. The type of the optimization algorithm should be selected taking into account the duration of the single optimization process. In the case of time-consuming processes, algorithms with low SD should be used. Originality/value: The new proposed simple nondeterministic algorithm can be also applied for simple optimization calculations. On the basis of the presented simulation results, it is possible to determine the quality of the compared MAs. [ABSTRACT FROM AUTHOR]

• Two closely related nature inspired algorithms are evaluated extensively on a function for which nothing can be proved. • Results show that both of them are similar. • The case of randomized hypercomputation is debunked. • Three theses related to nature inspired algorithms and hypercomputation are proposed. • Antithesis to belief, these theses are meant to be proved (or disproved) and not only meant as theses. The intention of this paper is to empirically compare two closely related (and considered novel metaphor based) nature inspired algorithms and then to use these comparisons and other ideas to shed some light on randomized hypercomputation. The Bat and the Novel Bat algorithms are two of the recently created nature inspired algorithms with the claim of novelty in both of them as both of them uses different randomization, but how novel or similar are they in reality remains hazy especially from the practical perspective. Therefore, here I compare both of them on a data dependent, unexplainable real world classification function. Particularly, I create two classification machines, one derived by BA and another derived by the NBA, using weighted liner loss twin support vector machine and compare it with other trailblazing classifiers on the real world UCI machine learning data sets. These machines are, especially compared with each other also. The results show that the formulated machines perform better with respect to other classifiers on more than 80% of comparisons but are extraordinarily similar between themselves, as they agree with each other up to even four decimal places in almost 100% of cases. Moreover, these optimization algorithms form a perfect example of partially random machines which are claimed to be hypercomputational. But, here it can be shown that unless one has an access to the uncomputable input and uses it intelligently one cannot hypercompute. [ABSTRACT FROM AUTHOR]

Dropout and DropConnect are two techniques to facilitate the regularization of neural network models, having achieved the state-of-the-art results in several benchmarks. In this paper, to improve the generalization capability of spiking neural networks (SNNs), the two drop techniques are first applied to the state-of-the-art SpikeProp learning algorithm resulting in two improved learning algorithms called SPDO (SpikeProp with Dropout) and SPDC (SpikeProp with DropConnect). In view that a higher membrane potential of a biological neuron implies a higher probability of neural activation, three adaptive drop algorithms, SpikeProp with Adaptive Dropout (SPADO), SpikeProp with Adaptive DropConnect (SPADC), and SpikeProp with Group Adaptive Drop (SPGAD), are proposed by adaptively adjusting the keep probability for training SNNs. A convergence theorem for SPDC is proven under the assumptions of the bounded norm of connection weights and a finite number of equilibria. In addition, the five proposed algorithms are carried out in a collaborative neurodynamic optimization framework to improve the learning performance of SNNs. The experimental results on the four benchmark data sets demonstrate that the three adaptive algorithms converge faster than SpikeProp, SPDO, and SPDC, and the generalization errors of the five proposed algorithms are significantly smaller than that of SpikeProp. Furthermore, the experimental results also show that the five algorithms based on collaborative neurodynamic optimization can be improved in terms of several measures. [ABSTRACT FROM AUTHOR]

Purpose: Responsible for lift generation, the helicopter rotor is an essential component to protect against ice accretion. As rotorcraft present a smaller wing cross-section and a lower available onboard power compared to aircraft, electro-thermal heating pads are favored as they conform to the blades' slender profile and limited volume. Their optimization is carried out here taking into account, for the first time, the highly three-dimensional (3D) nature of the flow and ice accretion, in contrast to the current state-of-the-art that is limited to two-dimensional (2D) airfoils. Design/methodology/approach: Conjugate heat transfer simulation results are provided by the truly 3D finite element Navier–Stokes analysis package-ICE code, embedded in a proprietary rotorcraft simulation toolkit, with reduced-order modeling providing a time-efficient evaluation of the objective and constraint functions at every iteration. The proposed methodology optimizes heating pads extent and power usage and is versatile enough to address in a computationally efficient manner a wide variety of optimization formulations. Findings: Low-error reduced-order modeling strategies are introduced to make the tackling of complex 3D geometries feasible in todays' computers, with the developed framework applied to four problem formulations, demonstrating marked reductions to power consumption along with improved aerodynamics. Originality/value: The present paper proposes a 3D framework for the optimization of electro-thermal rotorcraft ice protection systems, in hover and forward flight. The current state-of-the-art is limited to 2D airfoils. [ABSTRACT FROM AUTHOR]

• First introduced PD control theory for optimizing learning rate in CNN models. • Designed of innovative center selection and elite guidance strategies. • Developed an efficient enhanced battle royale algorithm(EBRO). • Proposed a new EBRO-ICNN model for air quality prediction. PM 2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM 2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability. [ABSTRACT FROM AUTHOR]

Crypto-currency markets are known to exhibit inefficiencies, which presents opportunities for profitable cyclic transactions or arbitrage, where one currency is traded for another in a way that results in a net gain without incurring any risk. Quantum computing has shown promise in financial applications, particularly in resolving optimization problems like arbitrage. In this paper, we introduce a differential evolution (DE) optimization algorithm for Variational Quantum Eigensolver (VQE) using Qiskit framework. We elucidate the application of crypto-currency arbitrage using different VQE optimizers. Our findings indicate that the proposed DE-based method effectively converges to the optimal solution in scenarios where other commonly used optimizers, such as COBYLA, struggle to find the global minimum. We further test this procedure's feasibility on IBM's real quantum machines up to 127 qubits. With a scenario of three currencies, the algorithm converged in 417 steps over a 12 hour period on the "ibm_geneva" machine. These results suggest the potential to achieve a quantum advantage in solving increasingly complex problems. • We introduce a differential evolution (DE) optimization algorithm for Variational Quantum Eigensolver (VQE) using Qiskit. • We validate this method using crypto-currency arbitrage, as an example of problem with many local minima. • We test this procedure's feasibility on IBM's real quantum machines up to 127 qubits. [ABSTRACT FROM AUTHOR]

Training of Deep Neural Network (DNN) at the edge faces the challenge of high energy consumption due to the requirements of a large number of memory accesses for gradient calculations. Therefore, it is necessary to minimize data fetches to perform training of a DNN model on the edge. In this paper, a novel technique has been proposed to reduce the memory access for the training of fully connected layers in transfer learning. By analyzing the memory access patterns in the backpropagation phase in fully connected layers, the memory access can be optimized. We introduce a new method to update the weights by introducing the delta term for every node of output and fully connected layer. Delta term aims to reduce memory access for the parameters which are required to access repeatedly during the training process of fully connected layers. The proposed technique shows 0.13x-13.93x energy savings for the training of fully connected layers for famous DNN architectures on multiple processor architectures. The proposed technique can be used to perform transfer learning on-chip to reduce energy consumption as well as memory access. [ABSTRACT FROM AUTHOR]

Once there are constant or infrequently changed bits (COIBs) in two adjacent input messages of SHA-2, the switching power of input messages data registers (IMD-REGs) used for COIBs will disappear. Meanwhile, when full pipeline SHA-2 is applied in a certain application scenario where the IMD-REGs used for COIBs can be removed, more area of full pipeline SHA-2 can be saved as the proportion of IMD-REGs in message word registers increases. This paper proposes a new message expansion structure for full pipeline SHA-2 to increase the proportion of IMD-REGs. By inserting two expanders in last part of expansion structure pipeline stages and rescheduling the expander, the consumption rate of input messages will be decreased and the proportion of IMD-REGs will be increased. Compared with normal message expansion structure, the ratio of IMD-REGs to total message word registers in the proposed structure is increased from 15.1% to 41.6% for full pipeline SHA256, and 11.2% to 32.4% for full pipeline SHA512. When COIBs exists in adjacent input messages, the power and area advantages of proposed new message expansion structure have been demonstrated by FPGA and ASIC implementations. [ABSTRACT FROM AUTHOR]

Under process, voltage, and temperature variations, SRAM cell stability largely fluctuates from the nominal value. In the design step, SRAM cell optimization while ignoring the fluctuation induces the yield loss for the stability. Variation-aware optimization of an SRAM cell can prevent the yield loss problem by considering the mean and variance of SRAM cell stability when finding optimal design parameters. This paper proposes a novel SRAM optimization method that uses a deep neural network (DNN). Multiple DNNs from ensemble techniques represent the mean and variance of SRAM cell stability for the nominal design parameters. Subsequent sensitivity analysis of DNN extracts the ${K}$ design parameters that have the most dominant effects on the mean and variance of SRAM cell stability. Then multidimensional optimization is used to find the optimal values of these ${K}$ parameters to maximize the mean stability while minimizing its variance. The proposed method achieved an average of 2% error compared to MC simulation. The proposed optimization method takes only 561 s to provide the most optimal design parameter values of an SRAM cell. [ABSTRACT FROM AUTHOR]

This article discusses methods of parametric and structural-parametric synthesis of resonators, which contain transmission line segments of the same length. These methods allow optimizing resonators of various configurations with different segments number $N$ and characteristic impedances $Z_{0i}$. We will call optimal resonators if they have a minimum ratio $m = Z_{\mathrm {0max}}/Z_{\mathrm {0min}}$ at given ratio between the critical frequencies (poles and zeros of the input impedance). The input impedances of the resonators under consideration at the Richards’ frequency variable $S$ are described by the reactance functions of $N$ degree: $Z^{N}(S)$. The parametric synthesis refers to stepped impedance resonators (SIRs). The structural-parametric synthesis refers to resonators of a more general form, which contain open- and short-circuited stubs. The methods are based on two states established in this paper: the order of the reactance function $Z^{N}(S)$ should be the minimum possible ($N = N_{\mathrm {min}}$) to implement the required ratio between certain critical frequencies; the characteristic impedances of the composite sections $Z_{0i}$ take only two values $Z_{\mathrm {0min}}$ or $Z_{\mathrm {0max}}$. These methods were used to synthesize different resonators useful for practical application. With the help of the structural-parametric synthesis, the resonators of a more complex configuration are synthesized, which significantly exceed SIRs. The results of the measurements and simulation are presented. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present a novel approach to the determination of the maximum power point (MPP) in the photovoltaic system using genetic algorithm (GA). The optimization is realised on two types of photovoltaic (PV) modules: monocrystalline and polycrystalline solar modules, with the same rated peak power (400 Wp) but different electrical output data. Design/methodology/approach: The proposed algorithm is a nature-based algorithm that uses genetic operators such as reproduction, crossover and mutation to realise the search through the investigated area of solutions. To determine the MPP of the PV modules, a two-diode model of a PV cell is used. Based on the input electrical data for the analysed PV module, as well as the mathematical model of the PV, the algorithm can estimate the current and voltage at the MPP for given solar irradiation and cell temperature. The analysis is made for several different irradiations, but in work, the results are presented for irradiations of: 100, 500 and 1,000 W/m2 and cell temperatures of 0, 25 and 40 °C. Findings: From the presented results and performed analysis, it can be concluded that GA gives adequate results for both modules and for all working conditions. From the obtained results, it can be concluded that the optimization algorithm performs better when applied to the monocrystalline module works better especially in conditions with larger cell temperature, in comparison with the performance of the optimization algorithm applied to the polycrystalline module. On the other hand, the optimization algorithm applied to the polycrystalline module works better for the other working scenarios with smaller cell temperatures. Practical implications: From the performed analysis, it can be concluded that the use GA as an optimization tool for the determination of the MPP can be successfully implemented. In addition, to improve the overall performance of the PV system, it is also necessary to forecast the weather conditions of the location where the PV system would be installed to forecast the cell temperature and the solar irradiation. This is necessary to choose the right PV module and inverter for the given location. Originality/value: An optimization technique using GA as an optimization tool has been developed and successfully applied in the determination of the MPP for a PV system. The results are compared with the analytically determined values as well as with the values given by the producer, and they show good agreement. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is the design study and realisation of portable low-field open MRI system. Design/methodology/approach: The design of the magnetic resonance imaging (MRI) system is based on an optimization study using a genetic algorithm. Non-linear two-dimensional and three-dimensional numerical electromagnetic models are developed and inserted in the optimization environment. Findings: The results are found to be consistent with those issued from fully experimental tests. The static field produced by the device is 0.295 T with a homogeneity of 2.8% (28,000 ppm) over 100 mm diameter sphere volume. The z-axis gradient coils are capable of generating switching gradients with an amplitude of 8 mT/m and a frequency of 1.2 kHz. Originality/value: Our system is an open portable MRI which can be used in an ambulance. The open topology permits an easy access into the lateral sides when a surgery using surgical instrument with video feedback is needed. [ABSTRACT FROM AUTHOR]

This paper utilizes a combination of response surface method and genetic algorithm to provide an optimized model for microchannel heatsink with wavy microtube. The effect of flow rate in microchannel and microtube, location and diameter of microtube on the desired response surfaces, including hydrodynamic, thermal, and entropy parameters is examined. Then, the correlation between the variables is determined using a non-parametric regression model. The optimal design points are identified by multi-objective optimization and the Pareto diagram. The greatest effect is related to the nominal Reynolds number, with is directly related to the pumping power and entropy generation. The next effective parameter is the diameter of the microtube, which is inversely related to the pumping power, average surface temperature, and entropy generation. It is found that the maximum temperature difference on the hot surface is 3.1 K for optimal design HS3. For the geometry HS1, this value is less than 0.9 K by examining the optimal geometries of the micro heatsink based on the simultaneous study of the uniformity of CPU surface temperature and average CPU temperature. In addition, the flow velocity in the microtube of the design HS1 is much higher than that of the design HS3. [ABSTRACT FROM AUTHOR]

In this paper, the first dual-band sub-sampling receiver front end with sampling frequency optimization to meet the ultimate receiver error vector magnitude (EVM) of −40 dB over wide input power range of 19 dB is proposed. A systematic sub-sampling receiver chain EVM optimization with respect to major system-level impairments, such as noise folding, sampling frequency, IQ mismatches, phase noise of the sub-sampling clock, and unit capacitor value realizable at the decimation filter, is presented. The proposed dual-band sub-sampling receiver has a 26–41 dB continuously tunable gain for 2.4 GHz and 26–38.5 dB for the 5 GHz WLAN band. Continuously tunable gain ensures the ultimate receiver EVM performance over wider input power levels. In addition, the 5 GHz band is continuously tunable from 4.5 to 5.7 GHz. An active balun feedback low-noise amplifier followed by a sub-sampling down-conversion mixer is implemented to down-convert both WLAN bands to an intermediate frequency in the range from 445 to 538 MHz. Sub-sampling frequency optimization proposed in this paper down-converts both WLAN bands with the sampling frequency from 1.78 to 2.15 GHz to reach the target EVM. Additionally, a switched capacitor decimation filter running at 90 MHz is implemented to provide dual functionalities of down-conversion to baseband and band selection. A test-chip is implemented in a 1.2 V 65-nm CMOS technology. The proposed dual-band sub-sampling receiver occupies a total active area of 0.72 mm2 and has a total power dissipation of 55.6 mW. The overall receiver chain shows a noise figure of 11.5 dB at the highest gain and an IIP3 of −8 dBm at the lowest gain. [ABSTRACT FROM AUTHOR]

Advanced power amplifier (PA) architectures are critical for 5G communication which requires PAs to have sufficient output power, high linearity, and high energy efficiency. The time-variant operational environment further demands the self-reconfigurability in the PA design. Recent developments in bandit-problems and reinforcement learning (RL) have led to data-driven control algorithms. This paper presents various RL-based algorithms for Doherty PA control, which achieve robust adaptive operation over environmental changes. Multiple RL frameworks are incorporated in the control algorithms including multi-armed bandit (MAB), continuum-armed bandit (CAB), contextual-bandit (CB), and actor-critic with experience replay (AC). The control algorithms based on the latter three frameworks leverage on the prior information about the Doherty PA’s characteristics to improve learning efficiency. In our simulation test where the optimal policy needs to be adjusted due to transmitter output load impedance mismatch, the MAB-based control learns the optimal policy within 25,000 samples. The control algorithms based on CAB and CB learn the optimal policy within 5,000 samples. The fastest learning rate is achieved by the control algorithm based on AC, which learns the optimal policy within 1,500 samples. [ABSTRACT FROM AUTHOR]