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Purpose: This paper aims to describe the use of optimization approaches to increase the range of near-future howitzer ammunition. Design/methodology/approach: The performance of a gliding projectile concept is assessed using an aeroballistic workflow, comprising aerodynamic characterization and flight trajectory computation. First, a single-objective optimization is run with genetic algorithms to find the maximal attainable range for this type of projectile. Then, a multi-objective formulation of the problem is proposed to consider the compromise between range and time of flight. Finally, the aerodynamic model used for the gliding ammunition is evaluated, in comparison with direct computational fluid dynamics (CFD) computations. Findings: Applying single-objective range maximization results in a great improvement of the reachable distance of the projectile, at the expense of the flight duration. Therefore, a multi-objective optimization is implemented in a second time, to search sets of parameters resulting in an optimal compromise between fire range and flight time. The resulting Pareto front can be directly interpreted and has the advantage of being useful for tactical decisions. Research limitations/implications: The main limitation of the work concerns the aerodynamic model of the gliding ammunition, which was initially proposed as an alternative to reduce significantly the computational cost of aerodynamic characterization and enable optimizations. When compared with direct CFD computations, this method appears to induce an overestimation of the range. This suggests future evolution to improve the accuracy of this approach. Originality/value: To the best of the authors' knowledge, this paper presents an original ammunition concept for howitzers, aiming at extending the range of fire by using lifting surfaces and guidance. In addition, optimization techniques are used to improve the range of such projectile configuration. [ABSTRACT FROM AUTHOR]

This paper discusses the process of optimizing the shape of systems that are controlled by the Stokes flow with threshold leak boundary conditions. In the theoretical part it focuses on studying the stability of solutions to the state problem in relation to a specific set of domains. In order to facilitate computation, the slip term and impermeability condition are regulated. In the computational part, the optimized portion of the boundary is defined using Bézier polynomials, in order to create a finite dimensional optimization problem. The paper also includes numerical examples to demonstrate the computational efficiency of this approach. [ABSTRACT FROM AUTHOR]

Explicit model predictive control (EMPC) maps offline the control laws as a set of regions as a function of bounded uncertain parameters using multi-parametric programming. Then, in online mode, it seeks the best solution within these areas. Unfortunately, the offline solution can be computationally demanding because the number of regions can grow exponentially. Thus, this paper presents the application of a deep neural network (DNN) to learn the EMPC's regions for a photovoltaic-based charging station. The main uncertain parameters in this study are the forecast error of photovoltaic power production and the battery's state of charge. Additionally, the connection or disconnection of an electric vehicle is considered a disruption. The final controller creates the regions at the start of each prediction time or when a disruption occurs, only using the previously created DNN. The obtained solution is validated using data from an e-vehicle charging station installed at the University of Trieste, Italy. • Uncertainties like EV consumption affect the performance of optimization techniques. • EMPC creates offline critical regions that are a function of uncertain parameters. • The dimensionality of the problem can be untractable and time-consuming with EMPC. • DNN can be trained to create critical regions in a reduced computation time. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to construct a model that considers video purchase and then identifies the logical relationships implied by the parameters to explore video platform operation mechanisms. Design/methodology/approach: The authors analyzed the video platform system using a mathematical modeling approach and numerical optimization techniques. Through pricing decisions, the authors obtained equilibrium results for the profitability of the video platforms and analyzed the favorable market factors. The authors then extended the model by analyzing the competitive strategies of the two video platforms in the market. Findings: The authors find that advertiser profitability, ad nuisance, video sensitivity and video creator network effects are important factors influencing the pricing strategy of video platforms. During positive market conditions, video platforms tend to lower their prices until they absorb enough users. As market conditions change, the price adjustment strategies of video platforms are affected by parameter changes and inter-parameter relationships. Originality/value: The study considers the network effects of video creators, which provides a realistic reference for scholars and managers. In addition, the authors consider the bargaining power of platforms when purchasing content. The authors provide a fresh perspective for scholars while filling a gap in the field as video platforms can acquire a portion of the content on the market by setting a purchase price. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to optimize passive damper system (PDS) design by configuring its parameters to improve its performance and behavior in permanent magnet synchronous machines (PMSM). Design/methodology/approach: First, the principle and effectiveness of the PDS are recalled. Second, the impact of different PDS parameters on its operation is analyzed. Third, a multi-objective optimization is proposed to explore a compromise design of PDS. Finally, the transient finite element method simulation is performed to validate the optimized design, which can ensure an excellent noise reduction effect and weaker negative impacts. Findings: A suitable capacitance value in PDS is a key to realizing the damping effect. A larger copper wire can improve the noise reduction performance of PDS and reduce its Joule losses. A compromise solution obtained from a multi-objective optimization remains the excellent noise reduction and reduces Joule losses. Originality/value: This paper explores the impact of PDS parameters on its operation and provides an orientation of PDS optimization, which is favorable to extend its application in different electrical machines. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function. Design/methodology/approach: In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation. Findings: From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job. Practical implications: This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location. Originality/value: An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to gain a better understanding on how metasurfaces behave, in terms of currents in each unit cell. A better knowledge of their behavior could lead to an ad-hoc design for specific applications. Design/methodology/approach: The methodology used is both theoretical and numerical; it is based on circuit theory and on an optimization procedure. Findings: The results show that when the knowledge of the current in each unit cell of a metasurface is needed, the most common approximations currently used are often not accurate. Furthermore, a procedure for the termination of a metasurface, with application-driven goals, is given. Originality/value: This paper investigates the distribution of the currents in a 2D metamaterial realized with magnetically coupled resonant coils. Different models for the analysis of these structures are illustrated, and the effects of the approximations they introduce on the current values are shown and discussed. Furthermore, proper terminations of the resonators on the boundaries have been investigated by implementing a numerical optimization procedure with the purpose of achieving a uniform distribution of the resonator currents. The results show that the behavior of a metasurface (in terms of currents in each single resonator) depends on different properties; as a consequence, their design is not a trivial task and is dependent on the specific applications they are designed for. A design strategy, with lumped impedance termination, is here proposed. [ABSTRACT FROM AUTHOR]

This paper introduces a nearly linear-time algorithm for solving the negative weighted shortest-path problem in directed graphs without converting them to undirected counterparts, a departure from traditional approaches. By leveraging advanced graph decomposition techniques, it demonstrates that efficient algorithms can be developed while working exclusively within the framework of directed graphs. This breakthrough not only addresses a longstanding challenge in graph theory but also provides a foundation for designing efficient algorithms for other complex directed graph problems.

This paper presents a localization task for a robot swarm guided by the RAOI behavioral rules (repulsion, attraction, orientation, and influence). Slight changes in these parameters can significantly affect the task's performance. To address this challenge, we have developed a swarm simulator incorporating the robots' dynamic model. The localization task is evaluated using objective functions, represented as metrics, which are minimized using multi-objective optimization techniques. Our results showcase the Pareto fronts, illustrating how the objective functions react to variations in the RAOI parameters, aiming to expedite target localization while maintaining the swarm's integrity. • Repulsion, Attraction, Orientation, and Influence parameters (RAOI) are proposed. • We steer a swarm of robots through RAOI parameters to solve collective tasks. • Swarm efficiency depends on simulations and satisfies several objective functions. • Pareto fronts visualize the interaction of objective functions with RAOI parameters. • Results highlight the potential applicability of these methods in swarm tasks. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the problem of estimating the angle of attack (AoA) and relative velocity for vertical axis wind turbine (VAWT) blades from computational fluid dynamics data. Design/methodology/approach: Two methods are implemented as function objects within the OpenFOAM framework for estimating the blade's AoA and relative velocity. For the numerical analysis of the flow around and through the VAWT, 2 D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are carried out and validated against experimental data. Findings: To gain a better understanding of the complex flow features encountered by VAWT blades, the determination of the AoA is crucial. Relying on the geometrically-derived AoA may lead to wrong conclusions about blade aerodynamics. Practical implications: This study can lead to the development of more robust optimization techniques for enhancing the variable-pitch control mechanism of VAWT blades and improving low-order models based on the blade element momentum theory. Originality/value: Assessment of the reliability of AoA and relative velocity estimation methods for VAWT' blades at low-Reynolds numbers using URANS turbulence models in the context of dynamic stall and blade–vortex interactions. [ABSTRACT FROM AUTHOR]

The article explores how deep learning models have evolved from relying on hand-crafted operator libraries to utilizing compiler-based approaches for optimization, especially with the growing diversity of hardware platforms. It highlights the Apache TVM project, which allows machine learning engineers to compile models for specific hardware targets, optimizing performance without altering model accuracy. The article points to an accompanying paper that proposes a unified transformation approach that optimizes model architectures through program transformations, avoiding expensive retraining and achieving significant performance gains without compromising accuracy.

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]

This paper proposes a novel algorithm twice optimization for interval type-2 fuzzy neural networks with asymmetric membership functions (TOIT2FNN-AMF), for nonlinear system identification problems. The proposed TOIT2FNN-AMF uses an asymmetric Gaussian interval type-2 membership function to enhance the network's ability to describe and solve nonlinear and uncertain problems. The twice optimization algorithm consists of structure learning and parameter learning. Firstly, this paper proposes a multi-strategy adaptive differential evolution (MSADE) algorithm as the first optimization algorithm, which is used to determine the structure and the initial values of the parameters of the TOIT2FNN-AMF. It applies the root mean square error (RMSE) of the TOIT2FNN-AMF as the fitness function to determine the structure (number of rules) and initial parameters of the IT2FNN by searching for the RMSE values under different structures. When the fitness value reaches the minimum, that is, the RMSE value of the TOIT2FNN-AMF, the corresponding number will become the optimal one of fuzzy rules of the TOIT2FNN-AMF. Then, the second optimization algorithm of the TOIT2FNN-AMF turns into a hybrid optimization algorithm composed of an adaptive moment estimation (Adam) algorithm and recursive least squares (RLS) algorithm. Adam is used to optimize the antecedent parameters of TOIT2FNN-AMF rules, so as to maintain rapid convergence without generating oscillation during the training process; RLS is used to optimize the consequent parameters of TOIT2FNN-AMF rules, so that the network parameters can be optimized rapidly. In this way, the problems of excessive parameters to be adjusted and excessive slow convergence of the network can be solved. Finally, this paper evaluates the proposed TOIT2FNN-AMF by testing on problems of nonlinear system identification and chaotic time-series prediction. The simulation results are compared with those of similar methods in the existing literatures, which demonstrates that the proposed TOIT2FNN-AMF model yields a lower RMSE value and a simpler network structure than the other type-2 fuzzy neural networks (T2FNNs). [ABSTRACT FROM AUTHOR]

Stochastic configuration networks (SCNs) have universal approximation capability and fast modeling properties, which have been successfully employed in large-scale data analytics. Based on SCNs, Stochastic configuration networks with block increments (BSC) use the node block increments mechanism to improve training speed but increase the complexity of the model. This paper presents a parallel configuration method (PCM), develops an extension of the original BSC with chaos theory and proposes stochastic configuration networks with chaotic maps (SCNCM), and establishes a hierarchical learning strategy (HLS) to enhance the compactness and construction speed of the model. Firstly, PCM randomly assigns the input weights w and biases b of hidden layer nodes by using uniform and normal distributions. In PCM, an iterative learning algorithm is intended to generate the scope control set and improve configuration efficiency. Secondly, the paper presents two kinds of stochastic configuration networks with chaotic maps, which are SCNCM-I and SCNCM-II. SCNCM-I adjusts block size by using multiple error values and chaotic maps to improve the training speed. Based on SCNCM-I, SCNCM-II utilizes node removal mechanism to enhance the compactness. Finally, HLS integrates with SCNCM-I, SCNCM-II, and the Harris-hawks optimization algorithm (HHO). The purpose of training is to enhance the training speed and compactness for three algorithms. The experiments are conducted on four benchmark data sets and an industrial application shows its effectiveness. [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]

Purpose: Novel aircraft propulsion configurations require a greater integration of the propulsive system with the airframe. As a consequence of the closer integration of the propulsive system, higher levels of flow distortion at the fan face are expected. This distortion will propagate through the fan and penalize the system performance. This will also modify the exhaust design requirements. This paper aims to propose a methodology for the aerodynamic optimization of the exhaust for novel embedded propulsive systems. To model the distortion transfer, a low order throughflow fan model is included. Design/methodology/approach: As the case study a 2D axisymmetric aft-mounted annular boundary layer ingestion (BLI) propulsor is used. An automated computational fluid dynamics approach is applied with a parametric definition of the design space. A throughflow body force model for the fan is implemented and validated for 2D axisymmetric and 3D flows. A multi-objective optimization based on evolutionary algorithms is used for the exhaust design. Findings: By the application of the optimization methodology, a maximum benefit of approximately 0.32% of the total aircraft required thrust was observed by the application of compact exhaust designs. Furthermore, for the embedded system, it is observed that the design of the compact exhaust and the nacelle afterbody have a considerable impact on the aerodynamic performance. Originality/value: This paper presents a novel approach for the exhaust design of embedded propulsive systems in novel aircraft configurations. To the best of the authors' knowledge, this is the first detailed optimization of the exhaust system on an annular aft-mounted BLI propulsor. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang–Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article. [ABSTRACT FROM AUTHOR]

This paper studies the event-based passive filtering issue for a series of time-delayed complex networks in the discrete-time domain. These networks feature singularly perturbed and Markov jump parameters, where the Markov chain models abrupt changes in node couplings and structural parameters, while the singularly perturbed parameter addresses discrepancies in time scales. Considering the limited communication bandwidth resource, a dynamic event-triggered mechanism is applied. This paper aims to design a reliable filter to estimate the states of complex networks, ensuring the filtering error system's stochastic stability and achieving an expected passive performance. Using convex optimization techniques and Lyapunov methodology, we derive sufficient criteria to ensure the stability of the filtering error system and the existence of such a filter. To validate the feasibility of the proposed method, both numerical and a practical examples are presented in the simulation part. [ABSTRACT FROM AUTHOR]

Constrained integer stochastic optimization problems (CISOP) are optimization problems where the cost function is stochastic and the constraints are deterministic. Solving the CISOP using conventional optimization techniques is time-consuming when the design space is huge. Ordinal optimization offers an efficient technique suitable for solving the CISOP. In this paper, an approach that uses golden jackal optimization assisted by ordinal optimization (GJOO) is developed for resolving the CISOP in a relatively short time. The GJOO is composed of three phases: metamodel, global search, and ranking and selection. At first, the polynomial chaos expansion is used as a metamodel to rapidly assess a solution. Next, advanced golden jackal optimization is adopted to determine N excellent solutions from the entire design space. At last, the modified optimal computing budget allocation is adopted to determine a distinguished solution from the N excellent solutions. The GJOO is utilized to the shipping/receiving docks optimization problem of a container freight station in air cargo. The performances of the GJOO are compared with those of five meta-heuristic methods. Empirical results demonstrate the efficiency and robustness of the GJOO algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the Inner–Outer Direct Search (IODS) optimization technique is extended in the fuzzy environment to solve a fuzzy system of nonlinear equations. The proposed approach of fuzzy IODS converts the fuzzy system of nonlinear equations to an unconstrained fuzzy optimization problem. Then, the unconstrained fuzzy optimization problem is studied through the IODS technique. To validate the proposed algorithm, convergence analysis is performed. Further, three different fuzzy system of nonlinear equations are considered to demonstrate the algorithm. The fuzzy system of nonlinear equations is divided into various cases viz. fully fuzzy (when both the coefficient matrix and right-side vector are fuzzy) and only fuzzy (when either of the coefficient matrix or the right-side vector is fuzzy). For each individual case the numerical and graphical convergence of the obtained solutions were established. One electrical circuit problem is investigated to obtain the current in fuzzy environment and the same is compared with an existing method. It is observed that the closed form of the fuzzy solution contains the solution of the corresponding crisp system as well as the compared method. Finally, it can be noted that the proposed IODS approach can be applied to find the united fuzzy solutions for various science and engineering problems with easy implementation. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm (WOA), as an advanced optimization algorithm with simple structure, has been favored by various fields. However, there are some disadvantages of WOA, such as slow convergence speed, low precision and falling into local optimal value easily. In this paper, a novel improved whale optimization algorithm (IWOA) with multi-strategy and hybrid algorithm is proposed to overcome above shortcomings. Firstly, IWOA initializes the population by chaotic mapping to avoid the initial population distribution of WOA deviating from the optimal value. Secondly, IWOA combines the pheromone of the black widow algorithm and the opposition-based learning strategy to modify the population, which improves the convergence speed and the global performance of WOA respectively. Finally, the adaptive coefficients and the new update modes replace the original update modes, which makes the structure of WOA simpler and more accurate. In addition, the convergence of IWOA is also proved in this paper. On the one hand, to demonstrate the effectiveness of IWOA, 23 benchmark functions are used to test various performance of the algorithm. On the other hand, in order to prove the superiority of IWOA, the experimental results are compared and analyzed with other optimization algorithms. Simulation results show that IWOA proposed in this paper owns excellent performance in convergence speed, stability, accuracy and global performance, compared with other algorithms. [ABSTRACT FROM AUTHOR]

A time minimization transportation problem deals with the resource efficiency to minimize time taken by the transport systems to deliver the commodity from sources to destinations. In this paper, a two-level time minimization transportation problem has been considered that categorizes the source–destination links into Level-I and Level-II with respect to the higher and lower level priority. The optimal delivery schedule of Level-I is followed up with the same for the Level-II cells. The paper proposes a solution procedure consisting of new algorithms that have been hybridized within the Particle Swarm Optimization to solve the problem making efficient use of resources. The solution procedure provides a methodical approach to the transport enterprises.This procedure does away with the rigid constraints, such as the location and number of non-zero allocations, required to be met by the traditional techniques of solving the transportation problem. The procedure generates pairs of Level-I and Level-II times at each iteration and the best pair(s) amongst these is/are marked out as the optimal solution of the problem. The solution procedure is explained through a numerical illustration. [ABSTRACT FROM AUTHOR]

This paper studies model predictive security control (MPSC) for networked switched systems under denial-of-service (DoS) attacks. Most of existing works only adjust the triggering scheme when being attacked. Different from them, this paper proposes a novel timing-response event-triggering scheme (TR-ETS) to reduce the impact of attacks on system performance, which can not only configure system resources adaptively, but also accurately detect attack information and compensate the attacked data. Specifically, the proposed scheme includes two event-based triggers, which can dynamically and jointly regulate the communication/calculation ability, generate virtual attack sequences and acquire the number of passive packet loss. Then, based on the triggered states, a class of model predictive controllers is designed to optimize the control action. Due to possible strong attacks, a security control framework including network and local loops be introduced and a permissable type-switching mechanism (PTM) is used. Under the permissable controllers (i.e., network and local controllers), sufficient conditions for the stability of closed-loop switched systems are derived. In addition, a set of model predictive optimization algorithm using linear matrix inequalities (LMIs) technique is addressed. Finally, the effectiveness of the proposed method is verified by illustrative examples. [ABSTRACT FROM AUTHOR]

In this paper, the problem of observer-based model predictive control (MPC) for a multi-channel cyber-physical system (CPS) with uncertainties and hybrid attacks is investigated via interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model. Both denial-of-service (DoS) and false data injection (FDI) attacks are studied due to the vulnerability of wireless transmission channels. The objective of the addressed problem is to improve the security performance of the multi-channel CPS under malicious attacks, which has not been adequately investigated in the existing MPC algorithms. Moreover, uncertainties which appear not only in the membership functions but in both state and input matrices are considered. In this paper, different from the method that FDI attacks are handled by the bounded functions, an off-line observer is designed to actively defend against the FDI attacks. Meanwhile, an on-line MPC optimization algorithm, which minimizes the upper bound of objective function respecting input constraints, is presented to obtain the secure controller gains. Finally, an illustrative example is provided to verify the effectiveness and superiority of presented approach. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to deal with the exact computation of the Jacobian of a time criteria from a numerical simulation of power electronics structures, for the sizing by gradient-based optimization algorithm. Design/methodology/approach: Runge Kutta 44 is used to solve the state equations. The generic approach combines numerical and symbolic approaches. The modelling of the static converter is based on ideal switches. Findings: The paper extends the state equations to derivate any state variable according a sizing parameter. The integral expressions used for some sizing performances (e.g. average or RMS values) mix symbolic and numerical approaches. Choices are made for the derivatives of the extrema of which the search is not a continuous process. The use of an object-oriented implementation allows to have generic formulation of some design performances. Research limitations/implications: The paper aims to propose and to test formulations of sizing criteria and their gradients; so, the modelling of the study case is carried out manually. Due to generic modelling approach used for the power electronics, the model is not completely continuous. So, the derivatives according some parameters (e.g. switch controls) must be carried out by finite differences. However, as the global behaviour is continuous, it is not critical. Practical implications: The proposed formulations can be easily applied on simple static converter applications. For applications with large state equations, it should be possible to use the basic model of switches used in simulation tools of power electronics. The solving process and the sizing criteria formulation (with their derivatives) are generic and can be instantiate for any study. Originality/value: The approach proposes formulations giving a numerical sizing dynamic model with a Jacobian computed, if possible, by an exact derivation useful for optimization studies. The approach gives fast simulation and fast computation of the derivatives by combining numerical and analytical approaches. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to decrease the computation time that the large number of simulations involved in a parametric sweep when the model is in a three-dimensional environment. Design/methodology/approach: In this paper, a new methodology combining the PCE and a controlled, elitist genetic algorithm is proposed to design IPT systems. The relationship between the quantities of interest (mutual inductance and ferrite volume) and structural parameters (ferrite dimensions) is expressed by a PCE metamodel. Then, two objective functions corresponding to mutual inductance and ferrite volume are defined. These are combined together to obtain optimal parameters with a trade-off between these outputs. Findings: According to the number of individuals and the generations defined in the optimization algorithm in this paper, it needs to calculate 20,000 times in a 3D environment, which is quite time-consuming. But for PCE metamodel of mutual inductance M, it requires at least 100 times of calculations. Afterward, the evaluation of M based on the PCE metamodel requires 1 or 2 s. So compared to a conventional optimization based on the 3D model, it is easier to get optimized results with this approach and it saves a lot of computation time. Originality/value: The multiobjective optimization based on PCEs could be helpful to perform the optimization when considering the system in a realistic 3D environment involving many parameters with low computation time. [ABSTRACT FROM AUTHOR]

This paper introduces the Ulimisana Optimisation Algorithm enabled Population Based Training (PBT-UOA) framework which allows for hyperparameters to be fine-tuned using a population based meta-heuristic algorithm at the same time as parameters are being optimised. Models are trained until near-convergence on the updated hyperparameters and the parameters of the best performing model are shared to warm start the other models in the next hyperparameter tuning iteration. In the PBT-UOA, all models are trained using the same dataset. This framework performed better than the Bayesian Optimisation algorithm. This paper also introduces the Ulimisana Optimisation Algorithm enabled Federated Learning (FL-UOA) framework which is an extension of the PBT-UOA. This framework is introduced to address the challenges of scattered datasets and privacy that is presented by the increase in connected end-devices. The FL-UOA learns on local data in scattered end-devices without sending datasets to a central server. The training datasets in local end-devices are used to evaluate models trained in other end-devices. The performance metrics are used to update the Social Trust Network (STN) of the FL-UOA framework. The FL-UOA outperformed the classic Federated Learning framework. This STN updating technique was tested in Machine Learning (ML) Unfairness to see how well it functioned as a regularisation term. This was achieved by training different models on subsets that contained datasets representing only specific sensitive groups. Results showed that by updating the hyperparameters while learning the parameters on the dataset scattered across different devices, the FL-UOA, takes advantage of diversified learning and reduces the ML Unfairness for models trained on group specific datasets. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) that integrate niching techniques are among the most effective methods for multimodal optimization problems. However, most algorithmic contributions are based on empirical performance observations rather than rigorous mathematical convergence support; this makes most existing methods parameter sensitive. Inspired by a recently proposed deterministic global optimization method, granular sieving (GrS), an extended global optimization method named collaborative GrS (Co-GrS) and a novel deterministic multi-EA design framework are proposed in this paper. The innovations are threefold. (1) Existing EAs are stochastic methods, and this paper introduces the principle of deterministic global optimization into EA for the first time in the literature. (2) A deterministic multi-EA framework is designed and implemented in the paper; from the perspective of population evolution, an easy-to-operate survival-of-the-fittest strategy based on mathematical principles is established in Co-GrS. (3) Unlike existing stochastic EAs, where the reproducibility of optimal solutions is achieved in a statistical sense, Co-GrS does not involve random parameters, and it automatically runs the algorithm only once with pre-set fixed parameters to find all optimal solutions. The experimental results demonstrate the effectiveness and competitiveness of our method compared to 16 state-of-the-art multimodal algorithms on the CEC'2013 benchmark suite. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) is widely used for trajectory tracking in networked dynamical systems, which execute repetitive tasks. Traditional norm optimal ILC (NOILC) based on the lifted approach provides an analytical expression for the optimal ILC update law, but it raises a computational complexity issue. As the trial length N (i.e., the number of sampling points in one trial) increases, the computational cost of the lifted approach increases exponentially, which is obviously impractical for long trials. To address this issue, this paper proposes a non-lifted norm optimal ILC (N-NOILC) approach by developing a new non-lifted cost function to improve computationally efficiency. The N-NOILC approach achieves monotonic convergence in the iteration domain, and the computational complexity decreases from O (N 3) of the lifted NOILC approach to O (N). Therefore, the proposed approach can be applied to large repetitive tasks. Based on the N-NOILC approach, this paper develops a centralized as well as a distributed algorithm for networked dynamical systems. Simulations are presented to validate the effectiveness of two algorithms and demonstrate the significant advantage of the N-NOILC approach in computational efficiency. [ABSTRACT FROM AUTHOR]

Purpose: The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS). Design/methodology/approach: This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied. Findings: The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations. Practical implications: The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system. Originality/value: Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques. [ABSTRACT FROM AUTHOR]

Purpose: In the vast majority of published papers, the optimal allocation of photovoltaic distributed generation (PVDG) units and reconfiguration problems are proposed along with the number of PVDG used in the simulation. However, optimisation without selecting the number of PVDG units installed in the distribution grid is insufficient to achieve a better operational performance of power systems. Moreover, multi-objective installation of PVDG units and reconfiguration aims to simultaneously relieve congestion problems, improve voltage profile and minimise the active and reactive power losses. Therefore, this paper aims to propose a new modified camel algorithm (NMCA) to solve multi-objective problems considering radial distribution system to achieve secure and stable operation of electric power system with good performance. Design/methodology/approach: In this paper, the decision variables include the location and size of PVDG units with specific rang to determine the number of PVDG units needed to install and open network lines determined using NMCA based on the L_∞ technique. This also satisfies the operating and radial constraints. Furthermore, a benchmark comparison with different well known optimisation algorithms has been made to confirm the solutions. Finally, an analysis of the findings was conducted, and the feasibility of solutions was fully verified and discussed. Findings: Two test systems – the institute of electrical and electronics engineers (IEEE) 33-bus and IEEE 69-bus, were used to examine the accuracy and effectiveness of the proposed algorithm. The findings obtained amply proved the efficiency and superiority of the NMCA algorithm over the other different optimisation algorithms. Originality/value: The proposed approach is applied to solve the installation PVDG unit's problem and reconfiguration problem in the radial distribution system, satisfying the operating and radial constraints. Also, it minimises active and reactive power losses and improves voltage profile. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to deal with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque while keeping low the amount of material used, by means of gradient-based free-form shape optimization. Design/methodology/approach: The presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. This paper presents an extension of a standard gradient-based free-form shape optimization algorithm to the case of multiple objective functions by determining updates, which represent a descent of all involved criteria. Moreover, this paper illustrates a way to obtain an approximate Pareto front. Findings: The presented method allows to obtain optimal designs of arbitrary, non-parametric shape with very low computational cost. This paper validates the results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm. Originality/value: This paper applies the presented gradient-based multi-objective optimization algorithm in the context of free-form shape optimization using the mathematical concept of shape derivatives. The authors obtain a set of Pareto-optimal designs, each of which is a shape that is not represented by a fixed set of parameters. To the best of the authors' knowledge, this approach to multi-objective free-form shape optimization is novel in the context of electric machines. [ABSTRACT FROM AUTHOR]

Proximal policy optimization (PPO) algorithm is a deep reinforcement learning algorithm with outstanding performance, especially in continuous control tasks. But the performance of this method is still affected by its exploration ability. Based on continuous control tasks, this paper analyzes the original Gaussian action exploration mechanism in PPO algorithm, and clarifies the influence of exploration ability on performance. Afterward, aiming at the problem of exploration, an exploration enhancement mechanism based on uncertainty estimation is designed in this paper. Then, we apply exploration enhancement theory to PPO algorithm and propose the proximal policy optimization algorithm with intrinsic exploration module (IEM-PPO). In the experimental parts, we evaluate our method on multiple tasks in MuJoCo phsysical simulator, and compare IEM-PPO algorithm with PPO and PPO with intrinsic curiosity module (ICM-PPO). The experimental results demonstrate that IEM-PPO algorithm performs better in terms of sample efficiency and cumulative reward, and has stability and robustness. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive dynamic multiobjective optimization algorithm for handling dynamic multiobjective optimization problems with variable environmental change types. Most of the existing dynamic multiobjective optimization problems (DMOPs) only deal with a single change type in the environment. Therefore, we design a set of DMOPs that has variable and mixed change types. Next, this paper proposes an adaptive dynamic multiobjective optimization algorithm (DMOA) focusing on the change types, to solve DMOPs with variable change types. It can detect the different types of environmental changes. The main purpose of a DMOA is to find the Pareto-optimal set (PS) of each environment. Therefore, the change types of DMOPs mainly contain two categories: PS changes over time and PS remains constant. After detecting the change type, an adaptive response strategy is activated to react to environmental changes. If PS changes over time, a classification prediction (CP) strategy is active to respond to environmental changes. If PS remains constant, a dynamic mutation (DM) strategy works to react to environmental changes. The proposed algorithm is extensively studied through comparison with several advanced DMOAs, thereby demonstrating its effectiveness in working out complex DMOPs with variable change types and on the parameter-tuning problem of PID controllers for dynamic systems. [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]

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]

Limitations of the standard backstepping control technique are overcome in this paper by yielding global conditions for regional ones; specifically, flexible cascade arrangements that do not require the system to be put in a pure- or strict-feedback form to ensure controllability beforehand, handling of nonlinear MIMO blocks without invertible input distribution matrices, calculation of virtual and real control laws regardless of the non-affine-in-control nature of subsystems, and systematic numerical synthesis of Lyapunov-based control laws at each step of the cascade connection via linear matrix inequalities. All these benefits derive from exploiting exact convex rewriting of nonlinear terms, splitting available and non-available signals for control implementation, and applying the direct Lyapunov method to cast conditions as a convex optimization problem. Examples of academic and practical interest illustrate the advantages of the novel methodology over former approaches. [ABSTRACT FROM AUTHOR]

Purpose: Cloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm. Design/methodology/approach: Cloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance. Findings: The fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services. Practical implications: The findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps. Originality/value: Using a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper. [ABSTRACT FROM AUTHOR]

Neuromorphic processors have gained momentum recently due to their high energy efficiency in artificial intelligence applications compared to DNN accelerators. Most neuromorphic processors are executing SNNs (Spiking Neural Networks). Liquid State Machine (LSM), as the spiking version of reservoir computing, shows advantages and great potential in image classification, speech recognition, language translation, etc.. Comparing with other SNN models, LSM has the characteristics of easy to train and low resource utilization, which is suitable for low-power and resource-constrained edge computing scenarios. In this paper, we propose a novel design of a neuromorphic processor, LSMCore, aiming at LSM acceleration. LSMCore supports both training and inference of LSM. It consists of 256 input neurons, 1024 liquid neurons, and 1.31M synapses. Besides, multiple optimization techniques, including weight quantization for reducing storage, zero-skipping for decreasing dynamic sparsity, and mini-batch training are adopted in this processor. The experimental results show that the frequency of LSMCore achieves 400 MHz, the power is 4.9W and the area is 18.49 mm2 with a 40nm library. Comparing with the baseline, LSMCore achieves up to $80.7\times $ ($49.6\times $), $91.3\times $ ($56.3\times $), and $83.1\times $ ($56.8\times $) speedup on MNIST, N-MNIST, and Free Spoken Digital Dataset (FSDD) respectively for training (inference), while the accuracy of LSMCore on these three datasets are 96.8%, 97.6%, and 90% respectively. [ABSTRACT FROM AUTHOR]

• An adaptive forecast combination of highly diversified models is proposed. • The hyperparameter tuning is conducted through Bayesian optimization. • Our model significantly outperforms the classic and latest methods on extensive datasets. Among various time series (TS) forecasting methods, ensemble forecast is extensively acknowledged as a promising ensemble approach achieving great success in research and industry. Due to the high diversification of individual model assumptions, heterogeneous information fusion contributes to generating effective and robust forecasts for Economics, Meteorology, and Transportation. This paper proposes a Bayesian optimization-based dynamic ensemble (BODE) that overcomes the single model-based methods limitation and provides a dynamic ensemble forecast combination for TS with time-varying underlying patterns. The proposed BODE method combines ten disparate model candidates, including statistical methods, machine learning (ML)-based models, and the latest deep neural networks (DNN). We take into consideration their prediction performance for the recent past to adjust their weights for combination and apply the model-based Bayesian optimization algorithm (BOA) for the combination hyperparameter (HP) tuning to endow our method with higher adaptability and better generalization performance. Besides, the frequency impact of TS data on the ensemble forecast methods is under-researched in the current literature. Therefore, four groups of distinct seasonal TS datasets are investigated in this paper. The empirical result demonstrates that our method performs robustly better performance with the main reasons analyzed in a detailed ablation study. [ABSTRACT FROM AUTHOR]

In a very recently presented paper, Turky and Abdullah [5] proposed a novel multi-population harmony search with external archive (MHSA-ExtArchive) for dynamic optimization problems. In the experimental results, the authors claimed that their approach could outperform several state-of-the-art algorithms. They also showed the superiority of their method by means of numerical experiments on Moving Peaks Benchmark (MPB). Despite the interesting idea of applying multi-population scheme on harmony search and using a new type of external archive for dealing with dynamic problems, we believe that there are two very important shortcomings in the result analysis, which we point out in this short note. The main motivation of the present note is to contribute toward preventing the same mistakes from happening by the other researchers. [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]

[Display omitted] • Comparing to the other research papers, this paper compares the performance of the oldest, newest, more minor and well-known algorithms on deterministic chaos generators in one massive and unique study. • Paper show that by precision tuning, the original chaotic series convert into short N periodic time series (PTS). Thus no randomness as usually understand is there. These series are then used instead of classics pseudorandom numbers with positive impact. • Paper reveal the clearly visible positive impact of PTS on evolutionary algorithms (EAs) dynamics, which is visible almost on all algorithms used in this paper. Compared with the same EAs with classic random generators. • Paper open the question of whether standard random (nonchaotic) processes are really necessary for algorithm dynamics and suggest relations between randomness in EAs and noise in dynamical system control and theory. • Paper open, sketch and suggest new ideas and strategies on how to understand algorithm dynamics as the discrete feedback dynamical systems. Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions. [ABSTRACT FROM AUTHOR]

In this paper, a chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm is proposed. The improved algorithm is used to solve the problems that the whale optimization algorithm's convergence accuracy and convergence speed are insufficient. Firstly, the proposed algorithm is initialized by a method based on chaotic maps and quadratic opposition-based learning strategy to obtain a population with better ergodicity. Secondly, by introducing an adaptive variable speed adjustment factor, each search link unites to form a negative feedback regulation network, thereby effectively balancing the algorithm's exploration ability and exploitation ability. Finally, 20 benchmark test functions and 3 complex constrained engineering optimization problems were used to conduct extensive tests on the improved algorithm. The results show that the improved algorithm has better performance than others in terms of convergence speed and convergence accuracy in a majority of cases, and can effectively jump out of the local optimum. [Display omitted] • Firstly, this paper proposed a quadratic Opposition-Base Learning strategy based on Bernoulli map, and successfully obtained a higher quality initial whale population. • Secondly, this paper proposed a new variable speed adjustment factor and a cooperative convergence factor, and combine them with individual fitness to form a negative feedback adjustment network. Experiments show that the algorithm has good self-adjusting ability. • Finally, the range of some of the original random numbers in whale optimization algorithm has been changed. This adjustment allows the algorithm to have a more variable search range. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach: The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on "how exactly the matching process is done for evaluating the document." Finally, the e-learners are classified based on their grades. Findings: On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value: This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors' knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations. Design/methodology/approach: Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm. Findings: An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC. Originality/value: In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Design/methodology/approach: Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance. Findings: The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function. Originality/value: The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach: To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings: In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value: The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper. [ABSTRACT FROM AUTHOR]

This paper considers the reliability analysis and the bi-objective optimal problem for a redundant system with two dependent failures: common cause failure (CCF) and load-sharing failure, where the system contains N active components, W warm standbys and C cold standbys, a K -mixed redundancy strategy is considered. In the system, there is a repair team providing variable repair rates according to differential situations: when there are standby components available in the system, the repair team assigns a regular repairman to repair the failed components, with the known fact that regular repairman is not as skilled as an expert repairman in doing some complex repair; when all the spares are exhausted, the repair team assigns an expert repairman to attend failed components; when the system breaks down due to a CCF's occurrence, the repair team provides a very special repair service to restore the system to normal working state. The steady-state distribution of the system is obtained by matrix-analytic method. The sets of equations satisfied by the reliability functions and transient availabilities from arbitrary initial state are respectively presented and solved by Markov renewal theory, Laplace–Stieltjes transform (LST) and Laplace transform (LT) technique. Based on the obtained results, sensitivity analysis is performed by some numerical experiments. From economic view point, the bi-objective optimization problem is constructed and solved by NSGA-II algorithm for the minimal expected total cost and maximal availability. [ABSTRACT FROM AUTHOR]

[Display omitted] • A framework is proposed for population-based optimization algorithms' convergence. • DIRECT's partition and population evolutions are repeated in this framework. • The global convergence of the framework is proved. • Framework is applied successfully on three population-based optimization algorithms. Population-based optimization algorithms, such as genetic algorithm and particle swarm optimization, have become a class of important algorithms for solving global optimization problems. However, there is an issue that the global convergence is often absent for most of them. This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve this troubling problem. In this framework, regular partitions and evolutions of populations are implemented alternatively. Specifically, the initial population is generated from a regular partition on the search space; after several generations of evolution of the population, the evolution result is returned to join in the regular partition again, and a new population is generated. Repeat such progress until some stop condition is satisfied. Global convergence is guaranteed for the framework. Then this convergence framework is applied to particle swarm optimization, differential evolution, and genetic algorithm. The modified algorithms are globally convergent and perform better than the original version. [ABSTRACT FROM AUTHOR]