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Thermo-elastic systems have a wide range of applications in various engineering fields. Traditional single-objective topology optimization (TO) design of thermos-elastic structures is difficult to achieve the combined optimum of multiple properties, and the non-periodic TO design of results are complex and difficult to fabricate. This paper introduces a multi-objective TO formulation based on density for designing thermo-elastic structures with periodic constraints, aiming to overcome the aforementioned issues. This paper assesses two competing weighted objective functions, the first function corresponds to structural and thermal compliance, while the second pertains to regional temperature and global stress. To solve the optimization issue, we utilize the p-norm function with a modified coefficient for evaluating the maximum temperature and stress values. Additionally, the adjoint variable method is employed to evaluate the sensitivity of various objectives, while the method of moving asymptotes (MMA) is used to update the design variables. Then the influence of different weight coefficients and subregion numbers regarding thermos-elastic coupled analysis is demonstrated through numerical examples. The results show that the complexity of the subregion features decreases as the number of subregions increases, and that changing both the number of subregions and the weighting coefficients results in changes in the overall structural performance. Finally, the geometric model is reconstructed using the TO results, and the structural performance is validated through the simulation of the reconstructed model. The results indicate that the TO results obtained by the method of this paper have predefined performance. [ABSTRACT FROM AUTHOR]

The drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control. [ABSTRACT FROM AUTHOR]

Over the years, several achievements have been made in power generation and optimising hybrid renewable energy systems (HRES) to achieve nature conservation, achieve energy security, and reduce carbon emissions. However, there are many complexities in Renewable energy (RE) conversion, sizing, design, and implementation, that require optimisation techniques to achieve optimal results in terms of reliability, cost, and environmental protection over time. This paper presents an overview of research trends in Optimization methods in HRES which are classified into modern and conventional methods. These two classifications are further divided into control methods, Artificial intelligence, Iterative and mathematical operations. However, all mentioned techniques have inherent advantages and disadvantages which will be discussed in this survey. In addition, the review paper explored different types of research in computing intelligence (CI), an aspect of Artificial Intelligence (AI) that involves the development of nature-inspired algorithms for optimisation. Finally, general optimisation criteria, system sizing methods used in RES, Mathematical modelling of RES, and gaps for future work to achieve sustainability were also presented. [ABSTRACT FROM AUTHOR]

The domain of material science has made great strides in recent years, especially in the fields of metallurgy and ceramic materials and the production of highly trustworthy, cost-effective and economically useful components for use in many industries. For the production of contours and intricate forms in conductive materials, Electrical Discharge Machining (EDM) is by far the most versatile and cost-effective alternative to standard machining processes. In-depth discussion and analysis of the following topics may be found throughout this review paper study. Surface topography and machining properties are investigated in this literature review to determine the impact of mechanical, chemical, electrochemical, and thermal material removal techniques. Different characteristics of dielectric mediums are also covered. Non-conductive and conductive ceramic composite performance characteristics, surfacetexture, mechanical and electrical qualities and geometrical tolerances are investigated as a function of electrode material. Through the perspective of composite materials, a variety of electrical discharge devices' performance metrics and properties are compared and contrasted. The performance metrics and characteristics of a wide variety of electrical discharge machines are compared and contrasted in light of the composite materials in order to identify their distinguishing characteristics. EDM research is being conducted on a variety of sophisticated conductive materials, to explore their unpredictable effects on EDM and their specialized applications. Methods for optimizing the study of composite material and their effects on EDM's numerous aspects include Design of Experiments, Analysis of Variance (ANOVA), Response Surface Methodology, Taguchi with Grey Relational Analysis, and so on. X-ray Diffraction (XRD) and Energy-Dispersive X-ray analysis (EDAX) are used to study the topography of a variety of composite materials and the latest optimization strategies are also investigated using a variety of algorithms. [ABSTRACT FROM AUTHOR]

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

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

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

Water distribution networks (WDNs) require large capital investment and ongoing operational costs, resulting in their optimisation being a highly researched field. Despite the benefits tanks bring to networks, most optimisation models omit them as decision variables due to the complexity they can introduce to heuristic approaches. This paper addresses the least-cost optimisation of WDN design and operation through the development and application of the Small-network Configurator for Optimising Pump Energy consumption (SCOPE). The SCOPE algorithm incorporates pipes, pumps and tanks as decision variables and solves the optimisation problem through an iterative approach that pairs EPANET simulation results with subsequent hydraulic calculations to converge on the pumping and storage configuration which yields the lowest energy consumption. The SCOPE output can then be used to seed further optimisation techniques. This approach has been tested on the synthetic Nametown and real-life Dili networks and demonstrated annual cost savings of 10% and 25%, respectively. [ABSTRACT FROM AUTHOR]

Twenty-first century is an era of cutting-edge technologies. From artificial intelligence to nanotechnology, modern life has gathered the pace to match up with the shrinking time and fast-moving world. Likewise, a Heterogeneous Network finds its own space in this century of inventions. Every individual, industry, and institute needs to be connected to the internet cloud. Mobile applications have changed the dynamics of every individual's life. From personal management to professional proficiency, mobile applications play an important role in almost every aspect. Numerous applications available these days demand continuous bandwidth connection. To remain always connected, there is a need for an effective handoff procedure. In this paper, we have proposed a convex optimization technique for optimizing the call drop probability, remaining energy and handoffs in heterogeneous networks. The proposed algorithm outperformed the already existing M-OPTG and M-WO algorithms. [ABSTRACT FROM AUTHOR]

The conventional grid has been combined with a number of renewable energy solutions. As a result, the modern power system is constantly evolving with the help of various power electronics topologies. Due to the growing complexity and new system configuration, the modern power grid is subjected to many challenges and the risk factors are increasing exponentially. In order to evaluate the performance of grid-connected solar photovoltaic (PV) energy systems with battery energy storage system (BESS), highly efficient buck-boost-flyback integrated converter (BBFIC) experiments have been conducted. The whole cybernetics methodology utilized for real-time, closed-loop simulation, validation with other researchers, and numerous optimization techniques are applied in the research work. This paper is developing a new framework that focuses on increasing the voltage gain of the BBFIC for grid-tied PV-BESS. For enhancing the voltage gain of BBFIC, it is planned to optimize the duty cycle of the converter. For optimization purposes, this work deploys a hybrid algorithm termed as "alpha score based on salp-grey wolf optimization (AS-GWO)" that combines the concepts of both "grey wolf optimization (GWO) as well as salp swarm algorithm (SSA)." Finally, it has been demonstrated that the results are superior when compared to other approaches in terms of numerous aspects of measurements. [ABSTRACT FROM AUTHOR]

For distribution networks with fuzzy network structure or large scale, traditional reliability assessment is limited by data collection and lack of data samples. Compared with traditional methods, the distribution network reliability prediction method can use fewer data to calculate and obtain reliability results, and its operation is simpler and more practical. In this paper, a distribution network original parameters and reliability prediction method based on wavelet neural network (WNN) and quantum particle swarm optimization algorithm (QPSO) is proposed. Firstly, given the blindness of mother wavelet selection, this paper analyses the error and running time through example analysis and selects the most suitable mother wavelet for distribution network reliability prediction. According to the characteristics of premature convergence of QPSO, the evolutionary speed factor and aggregation factor are introduced to modify the scaling factor to control the convergence of the algorithm. The improved QPSO is used to optimize the initial values and thresholds of the WNN. It can reduce their influence on the prediction results. Finally, the analysis results of different examples show that the method has higher forecast accuracy, better generalization ability, and stability. This method also provides new scientific ideas for the reliability prediction of distribution networks. [ABSTRACT FROM AUTHOR]

The efficient design of the aerodynamic surfaces in the aircraft allows the optimal performance of the vehicle and the reduction of the fuel consumption. Among these surfaces, the wing is the main contributor to the force which lifts the aircraft enabling the flight. However, the application of this force out of the center of gravity generates a moment that must be balanced through a force applied in the Horizontal-Tail-Plane (HTP) reaching the longitudinal trim of the aircraft. Traditionally, the design of the wing and HTP have been performed iteratively attaining suboptimal or time ineffective results. In this paper, we solve this problem through the application of a Genetic Algorithm for the combined optimization of the wing and the HTP by adjusting the aspect ratio, the taper ratio, the twist angle and the incidence angle of both surfaces to produce the optimal balance of lift adjusting its distribution to an elliptical configuration and enabling the longitudinal trim. Results show the automatic adjustment of the parameters of the aerodynamic surfaces thus fulfilling the objectives of this paper. [ABSTRACT FROM AUTHOR]

The lock valve plays an important role during normal shift and power failure in heavy-dutyautomatic transmissions. A new kind of lock valve was designed in this paper, and the main purpose of this paper is to improve the working performance of the lock valve. Firstly, the AMESim simulation model of the lock valve was built based on the analysis of the working circuit of the lock valve, and the influence of several design parameters on the lock valve working performance were analysed, which provided a reliable theoretical basis for the subsequent optimisation research. Additionally, thecomplete second-orderresponse surface model between design parameters and output oil pressure response time and control oil pressure fluctuation is established based on ISE and ITAE criteria. Finally, the particle swarm optimisation(PSO) algorithm and Box-Behnkenresponse surface methodology were applied to the optimisation of the mathematical model. The optimisation results have shown that the two optimisation methods can significantly improve the output oil pressure response speed of the lock valve, but the optimisation results of Box-Behnkenresponse surface methodology are more stable than the PSO algorithm in the control oil pressure, and the growth fluctuation is smaller. [ABSTRACT FROM AUTHOR]

Mathematical optimization can be used to increase the effectiveness of economic planning in socialist economies. Cockshott and Cottrell [Towards a New Socialism, Spokesman: 1993] have proposed a model of socialism in which optimal planning is made responsive to consumer demand. A point of contention has been the emphasis on labor values in their model. The use of labor values could mean that the environmental impact of production is insufficiently reflected in planning targets. This paper discusses how alternative values (opportunity cost valuations, OCs) can be calculated using linear optimization and presents a computer simulation of a socialist economy based on these values. An agent-based consumer model was developed to model the behavior of consumers. An alternative version of the simulation based on labor values is used for comparison. It is found that in specific circumstances the use of OCs does indeed result in a stronger emphasis on more environmentally friendly production than the labor value model. Relevant literature on optimal planning, distribution under socialism and valuation will be reviewed, followed by a presentation of the simulation and a discussion of some of its results for a series of small test economies. [ABSTRACT FROM AUTHOR]

We have designed and implemented an optimized PID controller for an adaptive cruise control system in this paper. The mathematical model for a cruise control system has been developed, and it is observed that it is a nonlinear first-order model with dead time. The objective functions chosen for optimizing the PID controller are ITE, ITAE and ITSE. The design of the optimized PID controller is based on the particle swarm optimization technique and teacher learning-based optimization technique. The results are scientifically compared with the conventionally tuned PID and fuzzy-based controllers. The optimized Proportional Integral Derivative controller shows better performance than a conventional PID and fuzzy-based controller. The overshoot of the system has been reduced to 0% from 46%, and the rise time has been reduced to 0.6150 s. This is the new work in the literature that will be quite useful for the performance enhancement of the cruise control system. [ABSTRACT FROM AUTHOR]

Solar photovoltaic and wind power systems are very much dependent on climate variations. Wind and solar photovoltaic systems are unreliable without storage units like batteries and diesel generators as a backup. The addition of storage devices increases the reliability of the hybrid system consisting of solar photovoltaic and wind turbines. During cloudy and slow windy days, sufficient battery bank capacity is required to meet the load demand. This review paper gives new ways of hybrid energy generation. It discusses several optimization approaches and ideas for hybrid networks. Hybrid systems are gaining more popularity and fame in the current energy crisis scenario and environmental pollution. This research has provided a comprehensive assessment of existing optimization strategies, particularly those associated with the isolated microgrid in the literature. Artificial intelligence offers noteworthy optimization for microgrid operation without long-term weather data, as evidenced by the current optimization pattern for hybrid renewable sources. [ABSTRACT FROM AUTHOR]

This paper explores the earlier detection of CHF, and its allied Cardio Vascular Diseases (CVD) like Atrial Fibrillation (Afib) from nature-inspired optimization techniques using cardiac signals. The Beth Israel Deaconess Medical Centre (BIDMC) CHF, MIT-BIH-Afib, and Normal Sinus Rhythm (NSR) databases from the Physionet repository are utilized in this research. The Flower Pollination Algorithm (FPA), Binary Flower Pollination Algorithm (BFPA), and BFPA with Greedy Crossover approaches are applied to extract the features of CHF, Afib, and NSR cardiac classes ECG signals. Then extracted features are given into appropriate classifiers such as the SVM (Linear), SVM (Polynomial), SVM (RBF), WKNN, and NBC classifiers. The classifiers' performance is assessed and compared by the standard performance benchmarks, such as Accuracy, F1 Score, Geometric Mean, Kappa Coefficient and Error Rate. The SVM (RBF) classifier with BFPA features achieved 97.71% overall average accuracy along with a 0.9540 kappa coefficient in {CHF} vs. {NSR} cardiac abnormality detection. The SVM (RBF) classifier with BFPA features reached 89.59% overall average accuracy along with a 0.7935 kappa coefficient in {Afib} vs. {NSR} cardiac class detection. As a result, the SVM (RBF) classifier with BFPA features outclasses all other classification techniques in detecting {CHF} vs. {NSR} cardiac abnormality classes. [ABSTRACT FROM AUTHOR]

In this paper, critical analysis of performance comparison of various optimization techniques for a MRPID (Multi Resolution Proportional Integral Derivative) thermal system controller is presented. This controller controls the uncertainty or mismatch between real and reference temperature. Wavelet coefficients of error between the two temperatures and their corresponding gains are added to produce a control signal for the thermal system. The main purpose of the proposed method is to optimize the tuning parameters of a wavelet-based MRPID controller to regulate the switching pulse of the thermal system. Modeling and simulations are carried out using MATLAB/Simulink@2015. Figures-of-merit such as disturbance rejection, system stability and transient response are also compared using various techniques like Particle swarm optimization (PSO)-MRPID, Genetic Algorithm (GA)-MRPID (existing techniques), Firefly-MRPID controllers and Elephant herding optimization (EHO) based Recurrent neural network (RNN)-MRPID algorithm. Firefly-MRPID and EHO with RNN-based MRPID are proposed for determining unique (best) operating conditions of the thermal system in this paper. [ABSTRACT FROM AUTHOR]

This paper presents the development of hybrid machine learning models to forecast the natural flows of water bodies. Five models were considered under the analysis: extreme gradient boosting (XGB), extreme learning machine (ELM), support vector regression (SVR), elastic net linear model (EN), and multivariate adaptive regression splines (MARS). The grey wolf optimization algorithm (GWO) optimized all of the models' internal parameters. A feature selection approach was embedded in the hybrid model to reduce the number of input variables. The hybrid model performed the forecasts considering one, three, five, and seven days ahead on data collected from Cahora Bassa dam, Mozambique. In the experiments conducted in this paper, XGB outperformed EN, ELM, MARS, and SVR, presenting lower prediction error and uncertainty. The proposed XGB model arises as an alternative to help with flow prediction, which is crucial for hydroelectric power plant activity. [ABSTRACT FROM AUTHOR]

Methods for optimization of islanded microgrid systems are usually based on hourly models where each subcomponent is described by a simple algebraic model. There are many studies on this topic, which are usually based on the minimization of total lifetime cost by determining the number of required batteries, wind turbines, PV panels, the positioning of PV panels, etc. In this paper, we further improve the modeling of the microgrid system optimization process by developing a simplified algebraic model that uses one parameter to simulate accelerated battery degradation with respect to depth of discharge. The model consists of simply linearly increasing the degradation of the battery when the state of charge (SOC) becomes lower than a fixed value, and the only model constant is the factor of degradation f. The objective of the paper is to examine the effect of the degree of degradation on the obtained optimal microgrid system parameters. The analysis was performed for several different systems, and the results show that optimal parameters of the system and the overall system cost strongly depend on battery degradation characteristics. The overall system cost can be reduced by 1–6% for lower battery degradation rates and up to 20% for high degradation factor cases. Increasing the degradation factor also has an influence on the ratio of wind turbines to PV panels, and the optimal size of the battery system. [ABSTRACT FROM AUTHOR]

This paper presents a gradient-informed design optimization of nuclear reactor core components based on neutronics objectives with both continuous and discrete materials. The main argument in favor of using gradient-informed design optimization is that it scales well with increasing dimensionality of the design space. First, a challenge problem with 121 free parameters is solved with a gradient-informed method and then with a genetic algorithm. Then, a challenge problem to optimize the flux profile of a simplified assembly with eight axial zones is solved. Both challenge problems are solved using directly calculated derivatives from Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI) in the SCALE package. This work also demonstrates how a discrete optimization problem—selection of materials for 121 voxels—can be lifted into a continuous problem with mixed materials. In the continuous space, adjoint-based gradients are well-defined, and gradient descent is applicable. Then, a forcing function is introduced that with the selection of an appropriately sized hyperparameter can be used to guide the optimized continuous solution back into a discrete solution. This paper presents an account of the challenges that were faced when applying a gradient-informed optimization algorithm using a Monte Carlo calculation to estimate the gradient information and compares a gradient descent optimization method to a genetic algorithm optimization of the same geometry. Overall, this work demonstrates the potential use of adjoint-based gradient calculations in design optimization of nuclear systems. [ABSTRACT FROM AUTHOR]

The paper is focused on the numerical analysis of the two-point inverse stationary heat flow problem, namely the identification of the thermal load. Determination of selected parameters is possible on the basis of temperature measurements, done at specified few locations. This problem may be modelled as the optimization problem, standard numerical analysis of which requires multiple solutions of boundary value problems. However, the novel solution approach, proposed here, is based on the well-known concept of the Monte Carlo method with an appropriate random walk technique. It yields explicit stochastic relations combining computed temperatures and all load parameters. Such relations may be directly applied in most standard optimization algorithms, replacing time-consuming solutions of systems of algebraic equations. Moreover, one may construct the semi-analytical approach, in which the unknown load parameters are obtained explicitly, allowing for the elimination of sensitiveness to initial solutions or requirements for admissible load intervals. The paper is illustrated with results of several benchmark problems with simulated measurement data and various numbers of unknown load parameters. The results comparison, between standard element-free methods with selected optimization algorithms as well as the proposed Monte Carlo solution approach is presented and is especially focused on CPU times. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

Panax notoginseng is a valuable herb with geographical indication, and the quality and price of P. notoginseng from different origins are very different. Therefore, this paper proposes a rapid and accurate method for identifying the origins of P. notoginseng by collecting the roots of P. notoginseng. This paper improves the whale optimization algorithm in terms of global convergence and convergence speed, introduces the Levy flight strategy and reconstructed whale synergy factor A, and applies it to the parameter optimization of support vector machines, to obtain a high-performance classification model. The improved whale optimization algorithm model identifies the origin of P. notoginseng by discriminating their terahertz spectra. Compared with the commonly used genetic algorithm and the original whale optimization algorithm, improvement in the whale optimization algorithm was able to avoid falling into local optimum solutions more effectively while having a high convergence rate. Accordingly, the improved whale optimization algorithm optimized support vector machine model obtained an overall accuracy of 98.44%, which was significantly higher than the 95.31% overall accuracy of the genetic algorithm optimized support vector machine model and the 96.88% overall accuracy of the whale optimization algorithm optimized support vector machine model. It was concluded that terahertz spectroscopy together with machine learning would be a promising technique for identifying the origins of P. notoginseng. [ABSTRACT FROM AUTHOR]

For any industry, an important part of quality control is the detection and identification of defects of the products. During the manufacturing process, a wide range of defects occur on or in the products. These defects have an impact on its esthetics and functionality of the end-product. Their ability to compete in the market and the manufacturing costs are to be compromised if they are not detected properly. In many industries, defect detection is done manually by a few trained inspectors. The inspection results of this kind are highly subjective, and their measurements are seldom repeatable. The availability of fast and specialized computing hardware nowadays has facilitated the emergence of image processing algorithms to do online and real time quality inspections. As a result, many industries, such as textiles, steel, semiconductors, wood, leather, etc., are looking forward to machine-vision based inspection systems to address the issues pertaining to manual inspection. A comprehensive literature review of defects Identification, Localization, and Classification methods proposed to implement in such imaging systems is presented in this paper. The advantages and disadvantages of these suggested and implemented these methods are compared. The performance metrics used for the evaluation of these methods are also presented. In addition, the utilization of Optimization Techniques and Artificial Neural network structures in these systems is investigated. This review can help the researcher engaged in the area of automatic texture inspection systems to investigate the texture identification, localization, and classification methods. [ABSTRACT FROM AUTHOR]

In recent years, many studies have been used in feed-forward neural network (FNN) to develop decision-making systems. The primary objective is to get the least error by finding the best combination of control parameters. It has been observed that FNNs using meta-heuristics techniques always converges very quickly towards the optimal positions but suffers from slow searching speeds at later stages of generation. Due to slow convergence, it is a prevalent phenomenon that traditional optimization does not ensure to find global optima. As a result, it falls under local optima. Recently, another meta-heuristic optimization-based algorithm called sine cosine algorithms (SCA) was introduced to solve the aforementioned issues. The algorithm is fundamentally predicated on two trigonometric functions, one being sine and the other being cosine. However, like other traditional approaches, SCA has a tendency to be stuck in sub-optimal regions due to poor exploration and exploitation capabilities. This paper proposes an improved version of SCA named chaotic oppositional SCA (COSCA) by integrating with chaos theory and oppositional based learning into the SCA optimization process. It is an incipient training method employed to train an FNN. Three benchmark problems are used to examine the precision and performance of FNNs equipped with COSCA, COPSO, OSCA, SCA, PSO, and backpropagation. The experimental results showed that, relative to other meta-heuristic optimization techniques, the COSCA technique is able to improve performance. [ABSTRACT FROM AUTHOR]

This paper presents a task orchestration framework for multi-agent systems utilising linear temporal logic (LTL) and cross entropy optimisation, a stochastic optimisation technique used for rare-event sampling. We define task orchestration as a combination of task decomposition, allocation and planning for a quadcopter or team of quadcopters given a high-level specification. Specifically, we consider tasks that are complex and consist of environment constraints, system constraints, or both, that must be satisfied. We first approach motion planning for the single agent case where transition systems for the environment allow tasks to be developed as linear temporal logic (LTL) specifications. Trajectories are then generated via motion primitives for a single quadcopter and optimised via cross entropy to ensure optimal satisfaction of a cost function. We extend this work to the multi-agent case where a team of homogeneous quadcopters are considered to satisfy an LTL specification. In order to provide faster computations and initial cost-agnostic sampling, we formulate the online version of multi-agent task allocation via cross entropy for tasks specified in LTL specifications. The results of this framework are verified in simulation and experimentally with a team of quadcopters. [ABSTRACT FROM AUTHOR]

This paper presents an approach to optimise the long-term production scheduling of an open pit mine with multiple processing streams, while accounting for investment decisions under mineral supply uncertainty. The solution approach consists of first solving the linear relaxation using an extension of the Bienstock-Zuckerberg algorithm to the stochastic optimisation. Then, a rounding heuristic based on the topological sorting is applied, followed by a parallel multi-neighbourhood Tabu search. The proposed method is applied to a multi-product open pit mine deposit, with the possibility of investing in new shovels, trucks or crushers to increase related capacities. [ABSTRACT FROM AUTHOR]

Fractional-order circuits are the emerging area for analyzing the parametric response of the filters and its performance improvement can be analytically observed by using an optimization technique. In this paper, the fractional-order band-pass filter, using fractional-order capacitor (FC) and fractional-order inductor (FI), is investigated. The characteristics of the filter with desired fractional-order of FC and FI bearing range from 0.1 ≤ α ≤ 1.0 to 0.1 ≤ β ≤ 1.0, respectively having a step change of 0.1, are simulated. The frequency parameters of the proposed band-pass filter having order with in this range of α and β resulted during simulation. The quality factor is optimized from the results of the fractional-orders α and β for the given FC and FI, respectively. The theoretical quality factor in this integer-order filter is compared with its optimized Q value in its fractional-order counterpart. The proposed filter is modeled by using the best orders of α = 0.4 and β = 1.0 for FC and FI, respectively with optimized Q-factor. Finally, the frequency parameters of the filter as modeled are obtained through simulation using MATLAB/Simulink R2011a. [ABSTRACT FROM AUTHOR]

In this paper, the Penguins Search optimisation (PeSOA) Algorithm is used to identify optimal control parameters of the Chua circuit. The PeSOA algorithm, which is one of the nature-inspired algorithms, is mainly based on the collaborative hunting concept of penguins. In this algorithm, each penguin individually starts its search process, then communicates its position and the number of fish found to his group. The main objective of this strategy is to synchronise dives among the group in order to achieve a global solution. In this paper, a PeSOA algorithm is adopted to explore the search space for locating the optimum intervals and identify the unknown Chua's system parameters without any partial knowledge of the internal structure. The identified parameters, obtained by minimising the objective function between the estimated and the output values of the system, are used to obtain stable oscillations. The obtained results show that the PeSOA algorithm gives accurate results and the identified parameters produce a stable oscillation of Chua's chaotic circuit. [ABSTRACT FROM AUTHOR]

Mixed pixels are the main reason for the low accuracy of traditional remote sensing applications. Hyperspectral image unmixing can explore the sub-pixel information of mixed pixels, which is an effective measure to solve the problems of low precision. Current unmixing methods only consider the correlation of local or global pixels and do not fully utilize images information. Based on the above problems, we proposed a novel sparse unmixing model named combining low-rank constrain for similar superpixels and total variation sparse unmixing (CLRSS-TV) in this current research paper. The adjacent similar pixels are clustered first into superpixels, then the superpixels with the same semantic information are merged into a collection. The spatial information of the image is fully considered by twice clustering. Weighted low-rank constraint is imposed on each collection to consider the spectral correlation of pixels. Moreover, the TV regularization and l2,1 norm are utilized to improve the smoothness and row sparsity, respectively. Experimental results reveal that the proposed method is not only competitive with the four state-of-the-art algorithms in simulated and actual datasets, but also has good anti-noise ability and universality. However, this paper still fails to overcome the problem of parameters selection in sparse unmixing, so the future work study intends to adopt the intelligent optimization algorithm to obtain the optimal parameters. [ABSTRACT FROM AUTHOR]

This paper introduces a new modified local pattern descriptor to extract road from rural areas' aerial imagery. The introduced local pattern descriptor is actually the modification of the proposed local vector pattern (P-LVP), and it is named as Modified-PLVP (M-PLVP). In fact, M-PLVP extracts the texture features from both road and non-road pixels. The features are subjected to train the Deep belief Network (DBN); thereby the unknown aerial imagery is classified into road and non-road pixel. Further, to improve the classification rate of DBN, morphological operations and grey thresholding operations are performed and so that the road segmentation is performed. Apart from this improvement, this paper incorporates the optimisation concept in the DBN classifier, where the activation function and the count of hidden neurons are optimally selected by a new Trail-based WOA (T-WOA) algorithm, which is the improvement of the Whale Optimisation Algorithm (WOA). Finally, the performance of proposed M-PLVP is compared over other local pattern descriptors concerning measures like Accuracy, Sensitivity, Specificity, Precision, Negative Predictive Value (NPV), F1Score and Mathews correlation coefficient (MCC), False positive rate (FPR), False negative rate (FNR), and False Discovery Rate (FDR), and proves the betterments of M-PLVP over others. [ABSTRACT FROM AUTHOR]

This paper proposes a developed honey badger algorithm (DHBA) to solve the optimal power flow (OPF) problem. DHBA is based on the concept of honey badger algorithm (HBA), which improves problem-solving ability. The improvement is based on two main aims. These are, respectively, to develop a robust HBA using the dynamic fitness-distance balance technique and to improve the honey-finding behavior of honey badgers with a new spiral movement in the HBA algorithm. In this paper, simulation applications are performed on two IEEE (30/57 bus) test systems through 7 case studies (with statistical analysis) to prove the efficiency and the robustness of the DHBA. The efficiency of the proposed DHBA is tested in these two power systems and the obtained results are compared with other methods published in the literature. The obtained test results have clearly demonstrated the DHBA algorithm's potential to solve the OPF problem compared to the standard HBA. Active power losses, one of the important results of the study, are calculated as 2.9062 MW with standard HBA and 2.853 MW with DHBA (for the IEEE 30 test bus system). [ABSTRACT FROM AUTHOR]

This paper proposes an intellectual classification system to classify normal and abnormal CT scan lung images. Prediction of cancer cells from lung image is such a difficult task for physicians and researchers. A novel technique for lung tumour detection with Grey Wolf Optimized & Whale Optimization Algorithm – Support Vector Machine (GWO & WOA-SVM) is proposed. CT scan images are used as input images. Investigations focused on the segmentation and classification part to find the lung lesion region. For finding the lung abnormality and lesion region in the lung, four different stages are used. The first stage is the image acquisition process. The second stage is the image pre-processing and enhancement stage, here Advanced Clustering (AC) technique is used. The next stage is the segmentation process, here the advanced surface normal overlap (ASNO) lung segmentation algorithm is used. Finally, the lung lesion is classified using the hybrid classifier followed by a different optimization technique. The research gap for this paper is to find the lung lesion using an optimization-based hybrid classification technique. Nowadays, classification decision and treatment of lung tumours are based on symptoms and radiological appearance. Here Hybrid Classifier (KNN-SVM) is used to classify 700 images; it is observed from the results that the Hybrid classifier KNN-SVM demonstrated the highest classification accuracy rate of 97.6% among other methods. [ABSTRACT FROM AUTHOR]

This paper proposes the robust and comfortable day-ahead demand side management (DSM) methodology with an aim to normalize the demand curve pertaining to residential, industrial and commercial consumers. The ideal normalized demand curve is one in which demand follows the inverse relation to the respective price. In this paper, the demand curve normalization problem is formulated as an optimization problem which minimizes the square of the error between the demand curve obtained after DSM and ideal demand curve. The constraints of the optimization problem include the modeling of demand shifting from high to low price periods, local generation and working habits of consumers. Due to the probabilistic nature of the forecasted day-ahead hourly prices, these can exhibit variability from the mean values; hence a robust optimization technique has also been incorporated such that the proposed DSM methodology remains robust under worst case of price variability. The simulation results are obtained (with varying degrees of discomfort and robustness) for an aggregator, which involves shiftable devices belonging to residential, commercial and industrial consumers. The intuitive and interesting benefits of the proposed methodology are presented and compared with existing approaches pertaining to the non-robust, un-comfortable, comfortable, demand flattening, and demand normalization based DSM. [ABSTRACT FROM AUTHOR]

Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join (kNNJQ) or closest pairs queries (kCPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in Apache Sedona, using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed kNNJQ and kCPQ distributed algorithms are efficient, scalable, and robust in Apache Sedona. Finally, Sedona is also compared to other similar cluster computing systems, showing the best performance for kCPQ and competitive results for kNNJQ. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with distributed finite-time optimisation of multi-agent systems with unknown high-frequency gain signs under switching digraphs. First, a distributed finite-time gradient estimator-based optimal signal generator is constructed by adopting finite-time stability theory and graph theory. Then, considering the existence of unknown high-frequency gain signs in multi-agent systems, we propose new distributed asymptotic optimisation algorithms via the Nussbaum-type function. Additionally, in order to accelerate convergence, the novel distributed finite-time optimisation algorithms are presented. Both of the newly proposed algorithms can realise optimal consensus of multi-agent systems with unknown high-frequency gain signs under switching digraphs. Finally, two simulation examples are illustrated to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

A parallel version of Ant Lion Optimizer algorithm using the CUDA platform is introduced in this paper. Efficient kernel, memory and thread management approaches have been developed to maximize its performance. The new algorithm was tested against the canonical algorithm on 15 scalable problems of different sizes, with a total of 172 experiments. The solution costs and the execution times were compared using relative percentage difference and significance tests. The results showed the CUDA antlion algorithm significantly improves upon the execution time, while retaining the same solution quality. [ABSTRACT FROM AUTHOR]

This paper presents the effects of aspect ratio (0.1 ≤ A R ≤ 0.7) , Rayleigh number ( 10 2 ≤ R a ≤ 10 6 ) and inclination angle 0 ° ≤ γ ≤ 90 ° of the heated rectangular cylinder on heat transfer and fluid flow characteristics due to natural convection in air around heated rectangular cylinders of different sizes inside a cold square enclosure. Galerkin finite element method was adopted for the solution of the developed model. The walls of the enclosure were maintained at an isothermal cold temperature while the boundaries of the rectangular cylinder were kept at a constant hot temperature. The results are presented in the form of isothermal contours, stream functions, local and average Nusselt numbers. It has been found that for the range of aspect ratio and Rayleigh number considered, the rate of heat transfer increases with increasing aspect ratio and Rayleigh number. Furthermore, the orientation angle of the rectangular cylinder was found to generally augment heat transfer rate except at A R value of 0.1 , where the average Nusselt number declines beyond γ = 45 ° for all the range of Rayleigh numbers considered. Results from the present study could be applied to nuclear reactor technology and cooling of electronic chips system optimisation. [ABSTRACT FROM AUTHOR]

The elastoplastic behavior of the contact interface, powerfully influencing the contact stress distribution, is investigated to reinforce the uniformity of the contact stress distribution in this paper. Since the contact interface elastoplastic behavior has predominant and direct effects on contact deformation, whereas different contact deformation distributions can result in completely different contact stress distributions, it is theoretically feasible to enhance the uniformity of the contact stress distribution by optimizing the contact interface elastoplastic behavior. To realize this aim, a heuristic-based elastoplastic behavior optimization algorithm is developed, in which the elastoplastic behavior of the contact interface is optimized by changing the yield strength of the materials around contact interface. The uniformity of the contact stress is represented by its variance, and is defined as the objective function. Bilinear material model and von Mises yield criterion are adopted. Two contact cases are presented to evaluate the effectiveness of the proposed elastoplastic behavior optimization algorithm in terms of improving the uniformity of the contact stress. Some new phenomena during the elastoplastic behavior optimization are also observed and explained. [ABSTRACT FROM AUTHOR]

This paper considers the distributed cluster optimisation problem of multi-agent systems (MASs). Different from existing optimisation problems, the MASs are divided into several clusters, and each cluster has its own optimisation objective which is the sum of their local objective functions. Firstly, based on communication topology, the distributed optimisation problem with two clusters is considered and a kind of distributed control protocol is proposed. By using the fixed-time stability theory and the convexity of function, it is proved that the MASs can not only achieve cluster consensus in a fixed-time interval, but also solve the optimisation problem asymptotically. Secondly, the MASs with multiple clusters are further considered. We extend the proposed protocol to a more general case and also prove that MASs can achieve cluster consensus in fixed-time and asymptotically reach the optimal solution of the optimisation objective. Finally, some numerical simulations are presented to show the efficacy of the theoretical results. [ABSTRACT FROM AUTHOR]

For a three-link vertical underactuated manipulator with first active joint, few methods can swing its endpoint up from the vertical downward equilibrium point and stabilise it at the vertical upward equilibrium point. This paper proposes a single controller design method based on integrated trajectory to realise the above control objective effectively. To obtain the trajectories of each link from the starting states to ending states, we first plan a forward and a reverse trajectory with adjustable parameters for the active link, where the forward trajectory is from the starting states to intermediate states and the reverse one is from the ending states to intermediate states. Based on the system coupling characteristic, the adjustable parameters are optimised by the intelligent optimisation algorithm, so getting the trajectories of the passive links from the starting states to ending states. To ensure that the system with the gravity can remain at the ending states, we plan the constant trajectory for each link. Then, we integrate the optimised forward trajectory, the optimised reverse trajectory and the constant trajectory of each link into the continuous trajectory. Next, according to the integrated trajectory, we construct a linear time-varying error system, and design the single controller to make the system follow the integrated trajectory. Thus, the swing-up and stable control of the system is realised. Finally, simulation results demonstrate the validity of the control method. [ABSTRACT FROM AUTHOR]

This study investigated the derivatives of interlock knitted structures by changing stitch type, location, and their percentage (%) on different performance properties. In the current study, 11 different interlock knit structures were developed by using multiple combinations of knit, tuck, and miss stitches and different types of testing, i.e., physical, mechanical, thermo-physiological comfort, tactile comfort, and dimensional properties, were performed and investigated. Analysis of variance (ANOVA) was performed to determine the statistical significance of structure on the properties. Based on the results, it was concluded that stitch type, percentage, and location have a prominent influence on the abovementioned properties of knit fabrics, which is also elaborated by interaction plots. The present research used a multi-response optimization technique, i.e., principal component analysis (PCA), to identify the best structure for industrial applications like uniforms, based on the optimization of abovementioned properties. Based on the results, it was suggested that a sample with an interlock cross relief structure was found to be the best in all aspects among all samples, followed by interlock pique and double tuck, which were ranked as 2nd and 3rd, respectively. This paper highlights the possible application of different interlock knitted structures according to end use for manufacturers. [ABSTRACT FROM AUTHOR]

Recently the shape-restricted inference has gained popularity in statistical and econometric literature to relax the linear or quadratic covariate effect in regression analyses. The typical shape-restricted covariate effect includes monotone increasing, decreasing, convexity or concavity. In this paper, we introduce the shape-restricted inference to the celebrated Cox regression model (SR-Cox), in which the covariate response is modelled as shape-restricted additive functions. The SR-Cox regression approximates the shape-restricted functions using a spline basis expansion with data-driven choice of knots. The underlying minimization of negative log-likelihood function is formulated as a convex optimization problem, which is solved with an active-set optimization algorithm. The highlight of this algorithm is that it eliminates the superfluous knots automatically. When covariate effects include combinations of convex or concave terms with unknown forms and linear terms, the most interesting finding is that SR-Cox produces accurate linear covariate effect estimates which are comparable to the maximum partial likelihood estimates if indeed the forms are known. We conclude that concave or convex SR-Cox models could significantly improve nonlinear covariate response recovery and model goodness of fit. [ABSTRACT FROM AUTHOR]

This paper introduces a B-type inverter and an improved Y source DC-DC converter that use a selective harmonic elimination (SHE) method based on whale optimization to inject power into the grid with reduced harmonic content. The suggested topology contains four unidirectional switches, two bidirectional switches and three DC sources to produce the required output voltage level. The proposed topology can be functioned with equal and unequal DC sources. The developed structure can generate the negative voltages without the use of an H-bridge. It has more redundant states, and it is partially fault tolerant in nature. The proposed structure provides a high value of level to switch ratio. A single input multiple output high gain modular Y-source DC-DC converter is implemented for effective utilization of the PV modules in the suggested system. The triggering angles for the suggested system is generated using the SHE based whale optimization algorithm (WOA). Using the SHE-WOA method a total harmonic distortion (THD) of 4.31 % is obtained. The developed system is integrated with the grid and for grid synchronization PI based WOA method is used. Simulation study has identified the presence of only 2.47% of harmonics in the grid current and 2.03% in experimental analysis. Both these harmonic distortions are less than the IEEE-519 standards. [ABSTRACT FROM AUTHOR]

This paper proposes the combined control of lateral and roll carbody motion to improve the ride quality in a high-speed train during curve negotiation. The active suspension consists of an active anti-roll device based on hydraulic interconnected actuators and on a hydraulic active lateral suspension. Three control strategies are proposed, and the parameters defining the control laws are defined using Genetic Algorithm optimisation, with ride quality estimation performed based on numerical simulation of the curving behaviour of the active vehicle. The performance of the vehicle in passive configuration and with the active suspension is also assessed using numerical simulation, showing that the use of combined lateral and roll control enables the vehicle to run at 340 km/h providing the same or even higher level of ride quality as the passive vehicle running at 300 km/h. Simulation results also provide an estimate of the mechanical power required to operate the active suspension and prove that the use of active control is neutral towards running safety. [ABSTRACT FROM AUTHOR]

In this paper, a radial basis function neural network (RBF-NN) automatic sonar target recognition system is proposed. For the RBF-NN training phase, a whale optimisation algorithm (WOA) developed with a fuzzy system has been used (which is called FWOA). The reason for using the fuzzy system is the lack of correct identification of the boundary between the two stages of exploration and exploitation. Thus, the tuning of the effective parameters of the WOA is left to the fuzzy system of the Mamdani type. RBF-NN was trained by chimp optimisation algorithm (ChOA), genetic algorithm (GA), Evolution Strategy (ES), league championship algorithm (LCA), grey wolf algorithms (GWO), gravitational search algorithm (GSA), and WOA to compare the proposed algorithm. The measured criteria are convergence speed, ability to avoid local optimisation, and classification rate. The simulation results showed that FWOA with 97.49% classification accuracy rate in sonar data performed better than the other seven benchmark algorithms. [ABSTRACT FROM AUTHOR]

In power electronics, the alleviation of the converter losses is the major target to accomplish higher efficiency and lower thermal stress that can pave the way to lifetime enhancement of devices. Hence, this paper intends to implement a novel variable switching frequency system for switching loss minimisation in a 3-phase Voltage Source Inverter (VSI). Here, the count of commutations is minimised by varying the switching frequency over the fundamental period. Here, the switching loss is reduced by optimising the modulation index m and the reference angle θ of VSI with a novel hybrid optimisation model. The proposed novel hybrid optimisation model is constructed by hybridising the concept of Group search Algorithm (GSO) and Rider Optimisation Algorithm (ROA) and hence referred to as Bypass updated Group search Algorithm (BU-GSO). Finally, the performance of BU-GSO is evaluated over the traditional models in terms of convergence analysis, Total Harmonic Distortion (THD) as well. Moreover, the evaluation is accomplished with inductive load variation and restive load variation, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses distributed learning of object shapes using multiple robots, and proposes a systematic design procedure for distributed optimization algorithms with data-independent performance certificates. We start with formulating the object shape learning as a distributed classification problem based on so-called kernel method. A distributed algorithm, continuous-time alternating direction method of multipliers, is then applied to the problem, wherein poor transient performances are observed. To improve the performance, we reformulate the classification problem so that singular values of sub-blocks in the algorithm are appropriately scaled. We then propose a systematic design procedure of the algorithm based on the concept of loop-shaping. The procedure is further extended so that the performance is independent of the data, and its effectiveness is verified through a numerical example. The proposed method is finally demonstrated through simulation on a high fidelity simulator. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method of source localization over a diffusion field based on distributed sparse optimization. We consider a sensor network over a planer field to measure spatiotemporal data of the diffusion process. We formulate the problem of estimation of the initial distribution (i.e. localization) as a distributed regularized least squares problem over multi-agent networks, assuming that the initial distribution is sparse in the space domain. For this problem, we propose a distributed sparse optimization algorithm called Cooperative Iterative Shrinkage Thresholding (CoopIST) algorithm. We show that the states of the agents asymptotically agree on an optimal solution of the regularized least squares problem by the proposed CoopIST algorithm. We also investigate the convergence rate in terms of the error of the time-averaged total cost. In addition, we present simulation results of a source localization problem with a two-dimensional diffusion process to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]