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Feature selection is a real-world problem that finds a minimal feature subset from an original feature set. A good feature selection method, in addition to selecting the most relevant features with less redundancy, can also reduce computational costs and increase classification performance. One of the feature selection approaches is using meta-heuristic algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2022 that were designed and implemented on a wide range of different data for solving feature selection problem. Evaluation criteria, fitness functions and classifiers used and the time complexity of each method are also depicted. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used. The results of this study and the identified research gaps can be used by researchers in this field. [ABSTRACT FROM AUTHOR]

A multi-fidelity optimisation strategy has been developed in the present work, and its performance is illustrated through a series of test cases. The strategy is based on hybrid methods such that two genetic optimisation algorithms are employed, each coupled to a different fidelity level with transfer of information between them. The aim is that the low fidelity model, being less accurate but with a lower computational cost, performs a comprehensive search along the design space guiding the high fidelity model to the optimum region. This strategy has been shown to reduce the computational time of an optimisation through analytical test cases as well as numerical cases. The analytical cases have been used to tune the parameters that define the multi-fidelity strategy, while the numerical cases are employed to apply the method to engineering problems, focusing on the aerodynamic performance of an airfoil. The speed-up shows a certain dependency to the models relation, both regarding their similarity level as well as the relative computational cost. For cases exhibiting a significant dissimilarity between models, wherein the low fidelity model is notably inaccurate, the attained speed-up diminishes, and numerous instances demonstrate an absence of speed-up. However, for most cases, even with poor model similarity the optimisations are accelerated by an order of 2, while values up to 3–5 were found for higher similarity levels. Hence, the developed strategy shows a relevant decrease of computational cost of an optimisation procedure although its performance is affected by the models relative accuracy. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop an advanced neural network algorithm as a new optimisation for the optimal design of truss structures. The central concept of the algorithm is based on biological nerve structures and artificial neural networks. The performance of the proposed method is explored in engineering design problems. Two efficient methods for improving the standard neural network algorithm are considered here. The first is an enhanced initialisation mechanism based on opposite-based learning. The second relies on using a few tunable parameters to provide proper exploration and exploitation abilities for the algorithm, enabling better solutions to be found while the required structural analyses are reduced. The new algorithm's performance is investigated by using five well-known restricted benchmarks to assess its efficiency in relation to the latest optimisation techniques. The outcome of the examples demonstrates that the upgraded version of the algorithm has increased efficacy and robustness in comparison with the original version of the algorithm and some other methods. [ABSTRACT FROM AUTHOR]

Real-time hardware-in-the-loop-(HIL) simulation integration is now a fundamental component of the power electronics control design cycle. This integration is required to test the efficacy of controller implementations. Even though hardware-in-the-loop-(HIL) tools use FPGA devices with computing power that is rapidly evolving, developers constantly need to balance the ease of deploying models with acceptable accuracy. This study introduces a methodology for implementing a full-bridge inverter and buck converter utilising the associate-discrete-circuit-(ADC) model, which is optimised for real-time simulator applications. Additionally, this work introduces a new approach for choosing ADC parameter values by using the artificial-bee-colony-(ABC) algorithm, the firefly algorithm (FFA), and the genetic algorithm (GA). The implementation of the ADC-based model enables the development of a consistent architecture in simulation, regardless of the states of the switches. The simulation results demonstrate the efficacy of the proposed methodology in selecting optimal parameters for an ADC-switch-based full-bridge inverter and buck converter. These results indicate a reduction in overshoot and settling time observed in both the output voltage and current of the chosen topologies. [ABSTRACT FROM AUTHOR]

The increasing penetration of photovoltaic technology in the electricity market requires the development of a methodology that facilitates the optimisation of photovoltaic plants with single-axis trackers. This paper presents an optimisation methodology that takes into account the most important design variables of single-axis photovoltaic plants, including irregular land shape, size and configuration of the mounting system, row spacing, and operating periods (for backtracking mode, limited range of motion, and normal tracking mode). Equations for the determination of the optimal row spacing and operating periods have been developed and is presented in detail. A packing algorithm that takes into account the irregular land shape and the possible configurations of the mounting systems is also presented. The objective function is the total area of the photovoltaic field and the optimisation is performed by a packing algorithm. As the economic aspect of energy generation also plays a key role in decision-making, the levelised cost of energy has been used to assess the economic viability of the optimal layout of the mounting systems. The results show that the proposed methodology and packing algorithm are able to optimise the photovoltaic plant with single-axis solar tracking and provide reliable results after a reasonable computation time. The methodology was demonstrated in detail for a Spanish photovoltaic plant (Granjera photovoltaic power plant), including the optimal layout of the mounting systems and the cost analysis for this layout. The optimal layout of the mounting systems could increase the amount of energy captured by 91.18% in relation to the current of Granjera photovoltaic power plant. The mounting system configuration used in the optimal layout is the one with the best levelised cost of energy efficiency, 1.09. The presented optimisation methodology can be utilised to facilitate the optimal design of commercial photovoltaic plants with single-axis trackers. Therefore, questions such as: what is the optimal distribution of mounting systems?, how much energy will this distribution produce?, and at what cost will it produce it?, can be answered by using the proposed methodology. • The optimal layout of single-axis solar trackers in large-scale PV plants. • A detailed analysis of the design of the inter-row spacing and operating periods. • The optimal layout of the mounting systems increases the amount of energy by 91%. • Also has the best levelised cost of energy efficiency, 1.09. [ABSTRACT FROM AUTHOR]

This work introduces a novel privacy preservation scheme. In large databases, the data sanitisation process preserves the stored sensitive data safely from unauthorised access and users by hiding it. Moreover, the statistical features are extracted. Further, the normalised data and features are processed under the data sanitisation process. For the sanitisation process, the optimal key is produced by utilising the Deep Belief Network (DBN) with Chaotic Map-adopted Poor and Rich Optimisation (CMPRO) model. It is the modified version of the classical PRO algorithm. As a novelty, chaotic map and cycle crossover operation is included in the CMPRO algorithm. Privacy, modification degree, data preservation ratio, and hiding failure are considered as the objectives for the key generation process. Then, the data restoration process restores or recovers the sanitised data, and it is the reverse process. Then, the outcomes of the adopted scheme are analysed over the traditional systems based on certain measures. Especially, the sanitisation effectiveness of the proposed approach for data 1 in test case 2 and it is 54.56%, 51.82%, 47.94%, 49.59%, 18.17%, 43.32%, 47.03%, 47.03%, 55.79%, 21.84%, 47.33%, and 32.13% better than the existing CNN+CMPRO, RNN+CMPRO, LSTM+CMPRO, BiLSTM+CMPRO, DBN+PRO, DBN+SSA, DBN+SMO, DBN+LA, DBN+SSO, DBN+J-SSO, DBN+BS-WOA, and DBN+R-GDA schemes. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer–earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model's parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations' uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period. [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]

Online updating is an important statistical method for the analysis of big data arriving in streams due to its ability to break the storage barrier and the computational barrier under certain circumstances. The quantile regression, as a widely used regression model in many fields, faces challenges in model fitting and variable selection with big data arriving in streams. Chen et al. (2019, Annals of Statistics) has proposed a quantile regression method for streaming data, but a strong additional condition is required. In this paper, renewable optimized objective functions for regression parameter estimation and variable selection in a quantile regression are proposed. The proposed methods are illustrated using current data and the summary statistics of historical data. Theoretically, the proposed statistics are shown to have the same asymptotic distributions as the standard version computed on an entire data stream with the data batches pooled into one data set, without additional condition. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods. [ABSTRACT FROM AUTHOR]

It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring. [ABSTRACT FROM AUTHOR]

The use of low earth orbit (LEO) satellites to enhance the performance of global navigation satellite system navigation and positioning services has become a popular research topic. In this study, NSGA‐III optimisation algorithm was used to design two hybrid configurations of 177 and 186 LEO constellations for enhancing the BeiDou Satellite Navigation System (BDS). Under the enhanced effect of optimisation constellation, the global average geometric dilution of precision (GDOP) of BDS was reduced to 0.8 ± 0.1, and the maximum GDOP was reduced from 2.4 to less than 1.1 (54.2% reduction). In order to verify the contribution of the two constellations to the convergence time and positioning accuracy of BDS precise point positioning (PPP), a LEO enhanced BDS PPP simulation experiment was carried out using International GNSS Service data from five stations. The results show that after 10 min of static positioning, both LEO constellations improved the positioning accuracy of BDS from the decimetre level to less than 5 cm. The maximum improvement for 177 and 186 LEO was 95.0% and 96.9%, respectively. Additionally, the convergence time for 177 and 186 LEO reduced to less than 3.5 and 3 min, and the maximum improvement was 93.5% and 95.2%, respectively. Overall, both constellations can improve the positioning accuracy and convergence time of BDS PPP. [ABSTRACT FROM AUTHOR]

Ultra-fine blast-furnace slag binders have recently been introduced to repair masonry. The reduced particle diameter of these binders makes them especially suitable for use as grouts, since this characteristic enables these grouts to fill even the smallest voids. The current necessity and effectiveness of Finite Element Analysis in any process concerning construction, repair or reinforcement of building structures remains unquestionable. In this way, the calibration of Finite Element material models for their correct performance has become compulsory. Regarding quasi-brittle materials, such as mortar and grouts, the Microplane model is recommended to reproduce their behaviour. This paper is targeted towards obtaining Microplane model coefficients to exactly reproduce the behaviour of ultrafine blast-furnace slag grout samples. To this end, several compressive tests have been carried on in order to obtain the experimental stress–strain curves that define the behaviour of these samples. Furthermore, reverse engineering by means of an optimisation algorithm successfully attained the possible coefficients to reproduce this material with the Microplane model. The correctness of these coefficients has been verified with a new campaign composed of compressive tests, Double Punch tests, and flexural tests. These tests have been reproduced by Finite Element Analysis, thereby confirming the accuracy of the set of coefficients. Thus, two are the main conclusions obtained: (1) the framework for the modelling of ultra-fine blast-furnace slag grout elements based-on the Microplane model has been proposed, implemented and validated; and (2) a value for the coefficients of the abovementioned model has been proposed. [ABSTRACT FROM AUTHOR]

In a fuel cell electric vehicle (FCEV) powered by a metal hydride tank, the performance of the tank is an indicator of the overall health status, which is used to predict its behaviour and make appropriate energy management decisions. The aim of this paper is to investigate how to evaluate the effects of charge/discharge cycles on the performance of a commercial automotive metal hydride hydrogen storage system applied to a real FCEV. For this purpose, a mathematical model is proposed based on uncertain physical parameters that are identified using the stochastic particle swarm optimisation (PSO) algorithm combined with experimental measurements. The variation of these parameters allows an assessment of the degradation level of the tank's performance on both the quantitative and qualitative aspects. Simulated results derived from the proposed model and experimental measurements were in good agreement, with a maximum relative error of less than 2 % . The validated model was used to establish the correlations between the observed degradations in a hydride tank recovered from a real FCEV. The results obtained show that it is possible to predict tank degradations by developing laws of variation of these parameters as a function of the real conditions of the use of the FCEV (number of charging/discharging cycles, pressures, mass flow rates, temperatures). [ABSTRACT FROM AUTHOR]

Implementing energy storage solutions is crucial to address the intermittency challenges of marine renewable energy. Buoyancy energy storage technology (BEST) holds potential, but its development remains in its infancy. Additionally, optimisation has not been implemented to improve the design. Therefore, a transit search (TS)-based optimisation model is developed to optimise both the small-scale (volume of tank less than 500 m3) and large-scale (volume of tank less than 30,000 m3) fabric BEST to maximise the power density and energy density. The optimisation results obtained through the transit search (TS) algorithm surpass those from the gray wolf (GW), whale, and artificial bee colony (ABC) algorithms in terms of consistency. The optimised large-scale fabric BEST design demonstrates improvements, with a 56% increase in power density from 345 W/m3 to 539 W/m3 and a 58% increase in energy density from 201 Wh/m3 to 318 Wh/m3 compared to the pre-optimised design from the literature. Moreover, linear relationships between the tank's submerged volume-to-water volume ratio and the extra weight limit were investigated through sensitivity analysis. The optimised fabric BEST shows competitive energy density when compared to other emerging energy storage technologies. Lithium-sulphur has higher energy density, but fabric BEST offers better safety and ease of installation. [ABSTRACT FROM AUTHOR]

In this study, the system with fractional power nonlinearity is introduced into the theory of aperiodic stochastic resonance (ASR). The fractional exponent is a key parameter and its effect on the ASR phenomenon excited by aperiodic binary signal is investigated in this system. Compared to the classical bistable system, the system with fractional power nonlinearity shows better performance. It can adjust not only the noise intensity but also the fractional exponent to enhance weak signal. In the field of signal transmission, pure aperiodic binary signal is usually submerged in the noise and the signal is unknown. Thus, an effective method is proposed based on ASR and moving average. By the method, the unknown aperiodic binary signal can be recovered in the noise background. To improve the efficiency of the signal recovery, the adaptive ASR is realised with the help of adaptive particle swarm optimisation (APSO) algorithm to optimise the parameters. The method may provide some reference to the engineering field. [ABSTRACT FROM AUTHOR]

Purpose: Metaheuristic algorithms have been commonly used as an optimisation tool in various fields. However, optimisation of real-world problems has become increasingly challenging with to increase in system complexity. This situation has become a pull factor to introduce an efficient metaheuristic. This study aims to propose a novel sport-inspired algorithm based on a football playing style called tiki-taka. Design/methodology/approach: The tiki-taka football style is characterised by short passing, player positioning and maintaining possession. This style aims to dominate the ball possession and defeat opponents using its tactical superiority. The proposed tiki-taka algorithm (TTA) simulates the short passing and player positioning behaviour for optimisation. The algorithm was tested using 19 benchmark functions and five engineering design problems. The performance of the proposed algorithm was compared with 11 other metaheuristics from sport-based, highly cited and recent algorithms. Findings: The results showed that the TTA is extremely competitive, ranking first and second on 84% of benchmark problems. The proposed algorithm performs best in two engineering design problems and ranks second in the three remaining problems. Originality/value: The originality of the proposed algorithm is the short passing strategy that exploits a nearby player to move to a better position. [ABSTRACT FROM AUTHOR]

Image compression is an intensively studied subject in computer vision. The deep generative model, especially generative adversarial networks (GANs), is a popular new direction for this subject. In this study, the authors propose a new compression method based on a generative model and focus on its application by GANs. The decoder in the proposed method is modified from the GAN generator model, which can produce visually real‐like synthetic images. It is one of the two models in GANs, which is trained through a two‐players' contest game. The encoder is an optimisation algorithm called backpropagation‐to‐the‐input, which derives from an image inpainting algorithm based on generative models. In the proposed method, the authors turn the encoding process into an optimisation task to search for optimal encoded representations. Compared with traditional methods, the proposed method can compress images from certain domains into extremely small and shape‐fixed encoded space but still retain better visual representations. It is easy and convenient to apply without any retraining or additional modification to the generative models. [ABSTRACT FROM AUTHOR]

The Hashin's strength criteria are usually employed in first ply failure and damage-onset analysis of fibre-reinforced composites. This work presents optimality conditions of local material orientations for these criteria, in terms of principal stresses and material strength parameters. Each criterion (matrix tensile/compressive, fibre tensile/compressive modes) has its conditions separately derived, analytically, based on a fixed stress field assumption. The conditions found show that orientations which coincide and do not coincide with principal stress directions may minimise local failure indices. These solutions are employed in a proposed algorithm, named HA-OCM (Hashin Optimality Criteria Method), which selectively satisfies the matrix failure modes (either tensile or compressive), iteratively and finite element-wise in composites. It is demonstrated that the HA-OCM is able to design single-layer plane structures with improved failure loads in comparison with designs following only maximum (in absolute) principal stress orientations. Results show that the material orientations have a trend to end up either aligned or at 90° with maximum in absolute principal stress directions. Global optima for compliance are, however, not guaranteed. To give an idea of gains in terms of failure loads, some HA-OCM designs show improvements of 71% and 140%, for example, in comparison with principal stress design. [ABSTRACT FROM AUTHOR]

The growing demand for the cloud community market towards attracting and sustaining the incoming and the available cloud users is addressed actively to meet the competitive environment. There is a good scope for improving the provider capabilities in the cloud in order to satisfy the users with attractive benefits. The study introduces an effective virtual machine (VM) migration strategy using an optimisation algorithm in such a way to facilitate the user selection of the providers based on their budgetary requirements in running their own platforms. The constraints associated with the selection of the provider include cost, revenue, and resource, which are altogether confined as an elective factor. The optimisation algorithm employed for the VM migration is referred to as Taylor series‐based salp swarm algorithm (Taylor‐SSA) that is the integration of the Taylor series with SSA. The evaluation of the method is progressed using three setups by varying the number of providers and users. The cost, the revenue, and the resource of the proposed method are analysed and concluded that the proposed method acquired a minimal cost, maximal resource gain and revenue. [ABSTRACT FROM AUTHOR]

This paper presents network load path optimisation for the weight minimisation of compression-only thrust networks, allowing for the design of material efficient surface structures. A hybrid evolutionary and function-gradient optimisation process finds the optimal internal force state of the network, by manipulating the force densities of a selected number of edges based on the network indeterminacy. These selected edges are the independent sets, and are found through the Reduced Row Echelon form of the network's equilibrium matrix. It was found that networks can have certain independent sets that have a significant influence on both the stability of the optimisation algorithm, and in the final load path/volume of the structure. Finding the most effective independent sets was handled by data-driven methods, applied to many thousands of independent set trials. This provided insight into the behaviour of the underlying network and dramatically increased the rate of finding successful independent sets. The importance and weights of the network edges highlighted key areas of the network that allowed structural judgement and improvements to be made. [ABSTRACT FROM AUTHOR]

This study is concerned with the problem of finite‐time H∞ filter design for a class of Itô stochastic systems with Markovian switching and distributed time‐varying delays. Firstly, a partially mode‐dependent filter is designed to accommodate to unreliable network transmission. The attention is focused on deriving sufficient conditions for the filtering error system to ensure the finite‐time boundedness and to satisfy a prescribed H∞ disturbance attenuation. Then based on stochastic functional theory, the existence of H∞ filter is presented by solving existing linear matrix inequalities optimisation problems. Furthermore, the result is extended to the case where the mode information is completely transmitted. Finally, a numerical example is provided to show the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

[Display omitted] This paper presents the parameter identification procedure of carbon nanotubes (CNTs) reinforced interphase in fuzzy fibre reinforced polymer (FFRP). The procedure was completed with ANSYS Workbench 19.2 software by combining Mechanical ADPL and Goal-Driven Optimisation. Firstly, a three-phase representative volume element (RVE) containing carbon fibre, CNTs reinforced interphase and epoxy resin was developed as a collection of Mechanical APDL commands. This RVE model was simulated to evaluate the elastic constants of FFRP lamina. CNTs reinforced interphase was characterised by transversely isotropic model. Interphase properties were parametrised and became input parameters in the Goal-Driven Optimisation. FFRP lamina elastic constants were set as the output parameters. Multi-objective Genetic Algorithm (MOGA) was used to identify the interphase properties, so that the output FFRP lamina elastic constants match the objective and constraints. The optimisation algorithm converged after 585 evaluations. Five potential candidate point, which met required objectives and constraints, were found. The identified interphase properties agreed well with the literature (an average percentage error of around 2%). This inverse procedure shows the potential to identify the interphase properties in nano-engineered composites, which are extremely difficult to measure experimentally. [ABSTRACT FROM AUTHOR]

• Sugarcane bagasse ash can be used as a partial replacement of cement in concrete mixtures. • Various AI methods have been employed to predict the compressive strength of concrete containing sugarcane bagasse ash. • AI models trained on concrete datasets with sugarcane bagasse ash, fly ash, slag, and none to assess their impact. • Used multi-objective genetic algorithms to optimise sugarcane bagasse ash concrete for cost-effective compressive strength. Sugarcane bagasse ash is an agricultural and industrial waste material produced in millions of tonnes annually. While traditionally used as a fertilizer or buried underground, incorporating this material into concrete production not only reduces cement usage and addresses environmental concerns but also enhances the mechanical properties of concrete. This research aims to investigate the predictive capabilities of various Artificial Intelligence (AI) models, including radial basis function neural network (RBFNN), artificial neural network (ANN), and support vector regression (SVR), for estimating the compressive strength of concrete containing sugarcane bagasse ash (SCBA). A dataset comprising 1819 data points from previous studies was collected, consisting of three main groups: 340 data of concrete with SCBA, 459 data of concrete without SCMs, and 1020 concrete data incorporating other supplementary cementitious materials (SCMs). The dataset was utilised in three different ways to evaluate the influence of data on model performance. Firstly, models were trained solely on SCBA data (Group A); subsequently, SCBA data was combined with concrete data without any SCMs (Group B); finally, the entire dataset was utilised (Group C). Statistical analysis revealed that the SVR model trained on SCBA data and the RBFNN model trained on group B data demonstrated the best results and accuracy. However, further refinement of the concrete without SCMs data in group B led to improved model performance, surpassing even that of the SVR model trained on group A data. [ABSTRACT FROM AUTHOR]

Average dwell time (ADT) of switched systems is dependent on two parameters: the decay coefficient of the subsystems and the increase coefficient of the Lyapunov functions of different subsystems, namely, λ and μ, respectively. In the literature, the two parameters are prescribed because popular computation techniques cannot deal with bilinear conditions easily. A new method for the computation of ADT is presented in this study, where a suggested optimisation algorithm is proposed to obtain a loose ADT condition. The advantages of the method lie in twofold: (i) the parameters λ and μ do not need to be given, and (ii) the presented ADT conditions are tractable to solve. The method is also extended to mode‐dependent ADT (MDADT), ADT in asynchronous control, and ADT and MDADT of positive switched systems. [ABSTRACT FROM AUTHOR]

The energy crisis and environmental pollution are the most serious threats to human survival. Currently, many countries and regions have set the goal of carbon neutrality by 2030–2060. Multi-energy systems (MESs), which integrate different energy systems such as electricity, natural gas and district heating networks, have received significant support and development to expedite this process. However, due to the complexity of its structure, the consideration of such a concept necessitates an appropriate model to describe the planning and optimal energy flow throughout the system. Another critical direction for future MES development is the rapid and accurate calculation of MES models. Numerous research institutions and large corporations have concentrated their efforts in this area, which has resulted in the publication of thousands of papers. This article aims to provide readers with a comprehensive and critical overview of the typical MES model and its associated optimisation algorithms to assist them in identifying appropriate models and associated solution methods for their research or engineering problems. We summarise the concept and potential three-layer structure of MESs and introduce their fundamental components in depth. Subsequently, according to different research or application objectives, we divide MES models into three categories and discuss their application scenarios, modelling methods and solution techniques. Additionally, we report six significant obstacles encountered in current real-world MES projects based on our practical engineering experience and propose seven potential development directions for future optimisation of the operating technology. • Providing readers with an overview of the MES model and related algorithms. • Summarizing the concept and potential three-layer structure of MESs. • Dividing MES models into three categories, discussing their modelling and solving. • Reporting six significant obstacles encountered in current MES projects. • Proposing seven potential development directions for future technology. [ABSTRACT FROM AUTHOR]

In order to increase the competitiveness of wave energy industry, device's efficiency and cost have to be both optimised. This study is a contribution to this approach by presenting the optimisation of a quayside rectangular float. This configuration has been chosen since quayside systems present the advantage of greatly reducing maintenance and grid connection costs. Moreover, rectangular shapes enable us to validate the optimisation algorithm with a linear potential model. Thus, the evolutionary algorithm CMA-ES (Covariance Matrix Adaptation-Evolutionary Strategy) is used to perform Capture Width (CW) optimisations in regular and irregular waves. Results with and without a vertical wall are compared. In an open sea configuration, at a given frequency, the shape that maximises efficiency is a horizontal flat rectangle with a minimum draft and a breadth equals to a half wavelength. The presence of the wall doubles the CW compared to the case in open sea. Moreover, for a quayside case, one optimal shape for a given draft has a breadth twice smaller than the open sea case. In addition, the resonances of the water column in the gap between the float and the dike, that occur in regular waves, lead to various possible optimised shapes. However, only the shape corresponding to a minimum draft and a quarter of the wavelength wide is also recovered in irregular waves. This shape also appears to be less dependent to the clearance distance between the float and the vertical wall. Finally, bi-objective optimisation results are realised in irregular waves for a rectangular quayside float in order to maximise the CW and minimise the WEC (Wave Energy Converter) cost. A significant cost reduction is possible by reducing the float immersed surface and volume. It is shown that a reduction of the float's breadth of 50% from the optimal value only reduces the CW of about 7%. • Optimal shape of a rectangular float in front of a vertical wall is found. • Compared to open sea cases, the absorbed efficiency is doubled and the width reduced. • Effect of gap resonances on the maximum absorbed power is highlighted. • CMA-ES algorithm is performed using mono and multi-objective optimisation. • Pareto fronts based on shape and cost, are obtained for a quayside float. [ABSTRACT FROM AUTHOR]

This research study proposed a novel algorithm, called time-counter, to optimise inspection intervals of production systems. The developed algorithm was applied to two active draglines. The algorithm evaluated random uptime/downtime characteristics of the draglines and the planned dragline halts in the mine. Physical and random non-physical costs were included and annual maintenance cost minimisation was achieved. Dragline-1 and Dragline-2 inspection intervals were optimised as 232 and 184 h and their maintenance costs were reduced by 5.9 and 6.2% compared to the current interval, respectively. This study is the first initiative in cost-effective inspection interval optimisation of a mining machinery. [ABSTRACT FROM PUBLISHER]

The aim of this study is to improve the performance of a wire fault diagnosis approach based on time‐domain reflectometry (TDR). In this approach, the TDR response collected from measurements for a given (faulty) coaxial cable network is used as a reference for the cable network model response computed by an accurate analytical transmission line (ATL) method. The ATL model is iteratively tuned according to the outcome of the optimisation algorithm proposed. The advantage of the proposed ATL method over the more classic finite difference time‐domain method is the extremely faster computational time which represents a significant factor for the diagnosis of faults in wiring networks. The proposed ATL method accurately provides the features of the resistance–inductance–conductance–capacitance‐based model of a RG‐58 coaxial cable employed as a device under test. The ATL model has been validated by comparison with measurement based and circuit simulations. Furthermore, seven experiments have been investigated in order to evaluate the performance of the ATL method for the diagnosis of wiring networks. The obtained results reveal that the ATL method together with the efficient optimisation algorithm is a reliable, efficient and fast method for the diagnosis of wiring networks. [ABSTRACT FROM AUTHOR]

Modelling dynamic behaviours of the lithium‐ion (Li‐ion) battery and supercapacitor in electric vehicle applications is a key aspect for the emulation of the hybrid power supply. In this study, a dynamical model based on two non‐linear equivalent circuits is developed to describe the characteristics of the battery and supercapacitor during both steady‐state and transient conditions. The necessary parameters for the proposed model are extracted from measurement data in time and frequency domain using an optimisation algorithm. The developed model is coupled to power electronics devices fed by DC power supply to carry out a laboratory emulator of the hybrid power supply. This tool is mainly used for testing and verification of the electric vehicle performances with convenient and reproducible way. The proposed emulator avoids time‐consuming preconditioning and safety problems generally caused by the misuse of electrochemical components such as the Li‐ion battery. The modelling and experimental results show a good performance of the hybrid power supply emulator and confirm their feasibility over a wide range of operating points. [ABSTRACT FROM AUTHOR]

Finding the authentic predicting tools of eliminating the uncertainty of wind speed forecasts is highly required while wind power sources are strongly penetrating. Recently, traditional predicting models of generating point forecasts have no longer been trustee. Thus, the present paper aims at utilising the concept of prediction intervals (PIs) to assess the uncertainty of wind power generation in power systems. Besides, this paper uses a newly introduced non-parametric approach called lower upper bound estimation (LUBE) to build the PIs since the forecasting errors are unable to be modelled properly by applying distribution probability functions. In the present proposed LUBE method, a PI combination-based fuzzy framework is used to overcome the performance instability of neutral networks (NNs) used in LUBE. In comparison to other methods, this formulation more suitably has satisfied the PI coverage and PI normalised average width (PINAW). Since this non-linear problem has a high complexity, a new heuristic-based optimisation algorithm comprising a novel modification is introduced to solve the aforesaid problems. Based on data sets taken from a wind farm in Australia, the feasibility and satisfying performance of the suggested method have been investigated. [ABSTRACT FROM PUBLISHER]

This paper addresses the energy management strategy (EMS) for a fuel cell hybrid electric vehicle (FC‐HEV). The fuel cell system (FCS) is a multi‐physics system, and consequently, its energetic performances depend on the degradation and on the operating conditions. The maximum power (MP) and the maximum efficiency (ME) points of the FCS are unique but they move with operating condition variations. Thus, developing an extremum seeking process (ESP) for both MP and ME tracking is a challenging task. In the ESP, models are identified online by using an adaptive recursive least square (ARLS) method to seek a variation in the FCS performances. Then an optimisation algorithm is used on the updated model to find the MP and the ME points. The ESP is incorporated into a hysteresis power splitting control (HPSC). A MP mode or a ME mode can be set based on the energy storage level (battery pack). The effectiveness of the proposed MP‐ and ME‐ESP EMS is demonstrated by conducting experimental studies on two FCSs with different levels of degradation. It was demonstrated that the classical EMS based on maps are not valid when the operating parameters vary because of the level of degradation change. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid method for solving the proposed multi‐level optimisation problem, as described in Part I. The hybrid method employs a novel two‐stage multi‐dimensional melody search algorithm (MSA) with an innovative alternative improvisation procedure (AIP). This hybrid method handles the different levels of the proposed multi‐level framework. The two‐stage multi‐dimensional MSA is a recently developed optimisation algorithm, which is derived from the harmony search algorithm (HSA). Unlike the HSA, the MSA uses different player memories and a group improvisation procedure for finding the best substitution of pitches within a melody. These two features can enhance the performance of the MSA in comparison with the HSA. In addition, to improve the efficiency of the proposed MSA, an innovative improvisation scheme based on the AIP is used for generating new melodies. The proposed methodology has been applied on the 46‐bus south Brazilian electric power grid and the IEEE 118‐bus test system under two different scenarios in order to demonstrate the feasibility and capabilities of the newly developed framework. The obtained results from case studies validate sufficiency and profitableness of the offered multi‐level framework. [ABSTRACT FROM AUTHOR]

To fully benefit from a multi-processor system, tasks need to be scheduled optimally. Given that the task scheduling problem with communication delays, P | p r e c , c i j | C m a x , is a well known strong NP-hard problem, exhaustive approaches are necessary. The previously proposed A* based algorithm retains its entire state space in memory and often runs out of memory before it finds an optimal solution. This paper investigates and proposes two memory limited optimal scheduling algorithms: Iterative Deepening A* (IDA*) and Depth-First Branch and Bound A* (BBA*). When finding a guaranteed near optimal schedule length is sufficient, the proposed algorithms can be combined, reporting the gap while they run. Problem specific pruning techniques, which are crucial for good performance, are studied for the two proposed algorithms. Extensive experiments are conducted to evaluate and compare the proposed algorithms with previous optimal algorithms. [ABSTRACT FROM AUTHOR]

Load modelling has significant effect on various power system studies. This study presents methodologies for modelling the loads under large as well as small disturbances using the measurements from phasor measurement units. A computationally efficient algorithm to estimate the parameters under small disturbances is presented using linearised model for the loads and eigenvalue sensitivity. An efficient optimisation algorithm is used for fast estimation of the load model parameters. Promising results are obtained from the simulation carried out on a small test system as well as on a practical larger power system, which have been compared with some of the existing methods to establish the superiority of the proposed method (PM) in terms of computational speed and accuracy. [ABSTRACT FROM AUTHOR]

This article investigates the optimal results of new improved fractional order integrators (FOIs) of different orders. Mathematical models of FOIs have been first developed by a single-step procedure of direct linear interpolation of fractional integrators based on Al-Alaoui operator in fractional domain itself, instead of using three steps of the well-known conventional method, namely, digital interpolation, series expansion and truncation. Later, these transfer functions (TFs) are optimised for their coefficient values for finding a minimum error function by particle swarm optimisation (PSO) algorithm. Simulation results of magnitude responses, phase responses and relative magnitude errors (dB) for all the proposed half integrators have validated the effectiveness of this new technique of interpolation of fractional order operators, mixed with PSO algorithm. A parallel comparison has been also drawn between the proposed optimised half integrators and those obtained by discretisation of PSO optimised integer order digital integrators (DIs) to properly support the proposed novel combination of interpolation and PSO, both applied together in fractional domain. [ABSTRACT FROM AUTHOR]

In this paper, adaptive bacterial foraging algorithms and their application to solve real world problems is presented. The constant step size in the original bacterial foraging algorithm causes oscillation in the convergence graph where bacteria are not able to reach the optimum location with large step size, hence reducing the accuracy of the final solution. On the contrary, if a small step size is used, an optimal solution may be achieved, but at a very slow pace, thus affecting the speed of convergence. As an alternative, adaptive schemes of chemotactic step size based on individual bacterium fitness value, index of iteration and index of chemotaxis are introduced to overcome such problems. The proposed strategy enables bacteria to move with a large step size at the early stage of the search operation or during the exploration phase. At a later stage of the search operation and exploitation stage where the bacteria move towards an optimum point, the bacteria step size is kept reducing until they reach their full life cycle. The performances of the proposed algorithms are tested with various dimensions, fitness landscapes and complexities of several standard benchmark functions and they are statistically evaluated and compared with the original algorithm. Moreover, based on the statistical result, non-parametric Friedman and Wilcoxon signed rank tests and parametric t -test are performed to check the significant difference in the performance of the algorithms. The algorithms are further employed to predict a neural network dynamic model of a laboratory-scale helicopter in the hovering mode. The results show that the proposed algorithms outperform the predecessor algorithm in terms of fitness accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is proposed for damage detection of structures with closely-spaced eigenvalues. The proposed method uses a transformed form of the condensed frequency response function matrix each of whose columns is obtained as the sum of the unwrapped instantaneous Hilbert phase of the corresponding decomposed column of the original matrix using Empirical Mode Decomposition (EMD) algorithm. A new sensitivity-based model updating equation is then developed, which uses the constructed new matrix as input. The constructed sensitivity-based equation is solved via the least squares method through iterations to update unknown structural damage indices in a finite element model of the structure. To demonstrate the capability of the proposed method, the problem of damage detection in a composite laminate plate and a spatial truss structure, as examples of structures with closely-spaced eigenvalues, is solved. Moreover, the results obtained from the proposed method are compared against two other methods from the literature. The results show that the proposed method is far more effective at updating damage indices when incomplete highly noisy data is available. • A Hilbert transform-based method was proposed for model-updating problems. • A robust-to-noise sensitivity-based damage detection method was proposed. • The proposed method uses unwrapped instantaneous Hilbert phase of FRFs as input. • A method for finding optimum excitation locations on structure is proposed. • The superiority of the proposed method over other methods was demonstrated. [ABSTRACT FROM AUTHOR]

It has been shown that closed-loop linear system identification by indirect method can be generally transferred to open-loop ARARMAX (AutoRegressive AutoRegressive Moving Average with eXogenous input) estimation. For such models, the gradient-related optimisation with large enough signal-to-noise ratio (SNR) can avoid the potential local convergence in maximum likelihood estimation. To ease the application of this condition, the threshold SNR needs to be quantified. In this paper, we build the amplitude coefficient which is an equivalence to the SNR and prove the finiteness of the threshold amplitude coefficient within the stability region. The quantification of threshold is achieved by the minimisation of an elaborately designed multi-variable cost function which unifies all the restrictions on the amplitude coefficient. The corresponding algorithm based on two sets of physically realisable system input–output data details the minimisation and also points out how to use the gradient-related method to estimate ARARMAX parameters when local minimum is present as the SNR is small. Then, the algorithm is tested on a theoretical AutoRegressive Moving Average with eXogenous input model for the derivation of the threshold and a gas turbine engine real system for model identification, respectively. Finally, the graphical validation of threshold on a two-dimensional plot is discussed. [ABSTRACT FROM AUTHOR]

Hydro‐Québec's electrical transmission system is an extensive, international grid located in Québec, Canada with extensions into the northeastern United States of America. For large power systems such as this, one of the major issues is to maintain the steady‐state performance of the network. From this point of view, flexible AC transmission system (FACTS) devices could be effective tools to improve power system security by reducing the power flow on overloaded lines, which in turn would result in an increased loadability of the power system, reduced transmission line losses, improved stability and security and, ultimately, a more energy‐efficient transmission system. Therefore in this study, the authors will present the effects of different types of FACTS devices on the performance of Hydro‐Québec's power system. The optimal locations and rating of these FACTS controllers will be determined with a view to improving network security using an optimisation algorithm based on a genetic algorithm. The effects of six different FACTS devices including static VAR compensator (SVC), thyristor‐controlled series capacitor (TCSC), thyristor‐controlled voltage regulator (TCVR), thyristor‐controlled phase‐shifting transformer (TCPST), unified power flow controller (UPFC) and static synchronous compensator (STATCOM) with energy storage are compared. Using the presented results, the effects of different types of FACTS devices on the Hydro‐Québec network will be analysed and compared with those of a STATCOM equipped with energy storage from the viewpoints of static loadability and losses. [ABSTRACT FROM AUTHOR]

This study presents a new method for the optimal design of single‐tuned harmonic passive filters considering the probabilistic nature of power system magnitudes. For this purpose, the algorithm uses the measurements acquired by a power quality analyser. The Gaussian mixture model has been applied to actual harmonic measurements to obtain a precise statistical representation which is crucial in the proposed design procedure. The optimisation algorithm considers both probabilistic and deterministic variables involving all the harmonic components including the minor or non‐characteristic harmonics. Moreover, the objective function of the optimisation algorithm is formulated taken into account all the harmonic terms overpassing other simplified formulations based only on the fundamental component. This study includes a case study with different optimisation scenarios to clearly highlight the benefits of each of the proposed contributions. [ABSTRACT FROM AUTHOR]

Highlights: [•] An optimal energy dispatch model of a PV–diesel–battery hybrid system is proposed. [•] The cost function minimizes fuel costs subject to given constraints. [•] We analyse energy flows in the system in a 24-h period. [•] Effect of daily and seasonal variations in demand on fuel cost is shown. [ABSTRACT FROM AUTHOR]

This study presents a secondary voltage control based on an optimisation algorithm to locate the control buses and regulate the voltage. Control buses are the buses where compensators will be installed on these buses to regulate voltage and avoid voltage violations. Firstly, partitioning algorithm using fuzzy C‐means has been implemented on the power network. Partitioning techniques split the power system into regions to avoid the propagation of disturbances between regions by using local controllers. Then, a number of buses are labelled as control buses displaying the critical point for voltage control in each region. The control algorithm is a decentralised controller which tries to eliminate voltage violations in power system resulting from load variations and disturbances. The decentralised controllers are implemented using flexible AC transmission system (FACTS) devices such as static synchronous compensator (STATCOM). The methodology is applied to the IEEE 118‐bus network. The results show the performance and ability of the partitioning algorithm and bus control selection to regulate the voltage and avoid propagation of disturbances between regions. [ABSTRACT FROM AUTHOR]

Abstract: Quarry yield can be increased by choosing the appropriate cutting direction for the primary blocks. This increase will reduce the extraction costs and the resulting environmental impact. This paper presents a computational approach to automatically predict and optimise the cutting of ornamental commercial rock quarry from primary blocks. Our work complements previous ones, which are focused on the primary blocks prediction, since we start with primary blocks and optimise their cutting for ornamental purposes. The developed method uses data from the three main families found in an exploited deposit and numerical procedures to calculate the appropriate advance direction for the exploitation yield optimisation. Specifically, the family system is numerically modelled to determine the maximum block to be extracted and its surrounding parallelepiped (SP). Then a 3D mesh is created for use in the rock-cutting process and to determine the optimum advance direction. Modelling the rock-cutting process permits the results for any cutting parameter and advance direction to be obtained automatically. The obtained data have allowed us to observe large variations in the exploitation yield by modifying the cutting advance direction and how the developed method automatically predicts the optimum advance direction for maximising exploitation yield. Furthermore, the developed method has been implemented in a computer tool, which presents the results graphically. The automatic prediction of the cutting output is of significant practical utility because it enables exploitation yield to increase while reducing dump size. These improvements imply economic and environmental benefits because more commercial blocks will be extracted from a single exploitation, and the disposable volume will decrease. [Copyright &y& Elsevier]

Transient stability constrained optimal power flow (TSC‐OPF) is originally a non‐linear optimisation problem with variables and constraints in time domain, which is not easy to deal with directly because of its huge dimension. This study presents an efficient approach to realise TSC‐OPF by introducing an independent dynamics simulation algorithm into the optimisation procedure. In the new approach, the simulation algorithm is used to realise the dynamics constraints and to deduce the transient stability constraint, whereas the optimisation algorithm verifies the steady state and the transient stability constraints together. The new TSC‐OPF has just one more constraint than that of a conventional OPF and can be solved by a conventional OPF algorithm with small modification. Moreover, the new approach makes it easy to improve the accuracy and efficiency of TSC‐OPF, because of its flexibility in choosing machine models and simulation methods, which is important for large power systems. In the study, the proposed approach is tested with two small power systems, where the two‐axis machine model and a mixed time step simulation method are used to assess system transient stability with a 1‐ms time resolution. [ABSTRACT FROM AUTHOR]

This study addresses the problem of designing a guaranteed cost controller for non‐linear discrete quadratic systems. More specifically, the main contribution concerns a sufficient condition for the existence of a state‐feedback controller achieving a certain guaranteed cost, whenever the state trajectories start from a prescribed set of initial conditions. The controller design requires the solution of a convex optimisation problem involving linear matrix inequalities, which can be efficiently solved via available optimisation algorithms. Finally, the proposed design methodology is illustrated via an example concerning the attitude control of an unmanned aerial vehicle. [ABSTRACT FROM AUTHOR]

In this study, transmission of JPEG2000 images using an unequal power allocation (UPA) scheme and orthogonal frequency division multiplexing (OFDM) over block‐fading frequency‐selective channels is presented. A distortion model is provided to evaluate the contribution of each coding pass (CP) in the construction of the received image. The optimisation algorithm exploits the hierarchical structure of the JPEG2000 images and uses the distortion model along with the channel state information for allocating optimal values of power for each CP to minimise the end‐to‐end distortion. Furthermore, the actual total power consumed for transmission is measured and compared with the total power initially assigned. For the purpose of simulations, the authors set the number of OFDM subcarriers to be 16, the length of the cyclic prefixes equal to the channel memory length and analyse the quality of the received image in a 2‐tap and 3‐tap frequency‐selective channel, with and without our proposed UPA technique in an OFDM system. The results show an improvement of up to 10.5 dB in the decoded image quality when the UPA scheme is used. In addition, our system manages to maintain similar quality for the received image in a multi‐tap channel scenario. [ABSTRACT FROM AUTHOR]

The knowledge of tensile load in reinforcement tie-rods is of crucial importance to assure structural integrity and safety. This work addresses the problem of identifying this tensile load in structural tie-rods of ancient masonry arches and vaults. The proposed procedure is a nondestructive and non-invasive method. It consists in matching the first six natural frequencies of the tie-rod, acquired by a classical accelerometer, with numerically obtained frequencies. This matching procedure is accomplished by an optimisation algorithm in which the length of the rod, the presence of concentrated masses along it and an elastic foundation at the edges are the optimisation variables. In this way, the main unknown, i.e. the axial load, is determined by a mathematical algorithm that automatically minimises the difference between experimental and numerical results on the basis of the choice of multiple parameter combinations. A 'local zoom technique' is adopted around a parameter set, which gives a minimum, in order to determine the load with good approximation. A deep investigation about the stress state in the tie-rods is also carried out via a nonlinear finite element analysis. [ABSTRACT FROM AUTHOR]

Metaheuristics (MHs) have been widely used for aeroelastic optimisation of aircraft wings and other types of aircraft structures. Using such methods offers some advantages e.g. flexibility for coding, robustness, global optimisation capability, and a derivative-free feature. Moreover, unconventional design problems can be posed when using metaheuristics. This paper proposes a new hybrid algorithm, named HDEEO-LP, with a learning control parameter for aeroelastic optimisation. The new optimiser is obtained from hybridising differential evolution and the recently invented equilibrium optimisation, while a learning scheme for control parameter tuning is integrated. The new method is tested against a number of established and recently invented MHs, such as a grey wolf optimiser (GWO), a salp swarm algorithm (SSA), an equilibrium optimiser (EO), an artificial bee colony (ABC), teaching–learning based optimisation (TLBO), water cycle algorithm (WCA), self-adaptive spherical search algorithm (SASS) using the CEC-RW-2020 test suite and the Goland wing aeroelastic optimisation. The results reveal that the proposed hybrid algorithm is among the top performers. [ABSTRACT FROM AUTHOR]