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To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem‐solving. Other papers focus on the cultural and institutional practices that make radical CI possible. The notion that cognition is collective has a long pedigree but is enjoying considerable attention in the cognitive sciences as multiple fields of study deliver evidence for the central role of community and culture in cognition. To introduce the special issue of topiCS surveying this groundswell, we propose the concept of "radical collective intelligence," the view that some of the key representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. This concept clarifies how the volume's contributions either rethink long‐studied cognitive processes (memory, metacognition, reasoning) or contemplate how radical CI can arise. [ABSTRACT FROM AUTHOR]

Supply chain management is impacted profoundly by digitalization in the forms of artificial intelligence (AI) or robotics applications (Bell & Griffis, 2011; Choi et al., 2021; Fragapane et al., 2021; Klumpp & Zijm, 2019). The emergent model from the implemented data analysis highlights the prevalence of marketing messages emphasizing AI trustworthiness and suggests several marketing tactics that could be used to market AI to supply chain workers internally, improving AI acceptance and use. This paper addresses the documented difference between AI adoption and AI use by exploring how AI technologies can be marketed within an organization. Based on the findings, a middle-range model of AI trustworthiness and a subsequent call for research related to the effects of AI trustworthiness on internal, upstream, and downstream activities in the supply chain is proposed. [Extracted from the article]

The goal of any well‐established deregulated power sector is to satisfy their customer by delivering the power with maintaining the best quality at a reasonable price. This paper presents the impression of the wind farm (WF) and compressed air energy storage (CAES) hybrid systems in the congested deregulated power system. The main motto of this work is to enhance social welfare by using a market‐clearing mechanism of the WFCAES hybrid system. To determine the effect of the hybrid system, initially the market‐clearing power (MCPO) problem is measured without considering the WF‐CAES hybrid system, and then the same work has been carried out with considering the WF‐CAES hybrid system. The bus sensitivity factor (BSF) decides the optimal position of the WF‐CAES hybrid system in the congested day‐ahead electrical system. The study is followed out through generator sensitivity factor (GSF) with the application of artificial bee colony algorithm (ABC) and slime mould algorithm (SMA) to calculate optimal market clearing price (MCP) and market clearing volume (MCV). The market‐clearing power problem has been solved in a modified IEEE‐30 bus test system. The SMA algorithm is been implemented in the market‐clearing mechanism for the first time which is the novelty of this paper. [ABSTRACT FROM AUTHOR]

Among multi‐hop technology, wireless sensor network (WSN) has been extensively investigated owing to its potential application in vivid fields. However, a key issue probing WSN is node location that is also the major area of interest in the present paper. The paper takes advantage of cuckoo search (CS) as the swarm intelligence technique used to address the issues of identification of malicious or unknown nodes within the network. The distance vector (DV)‐hop is used to determine the distance between the anchor sensor node and the unknown or the node with compromised nature. Then, artificial neural network architecture is used to distinguish the nodes based on the characteristics. This is followed by the evaluation of the proposed scheme to offer reliable data transmission using CS optimized data aggregation scheme. The simulation analysis over 1000 deployed nodes shows that CS significantly decreases the localization error to 0.494 and localization time to 0.058 s along with 15%–20% improvement in the throughput and packet delivery ratio. This shows that the proposed CS optimized architecture is successful in identifying the position of unknown nodes as well as compromised nodes that significantly improved the reliability of the data transmission. [ABSTRACT FROM AUTHOR]

Fortunately, driven by the emerging technologies, a systematic satellite-aerial-terrestrial integrated network has become an ideal choice for future Internet architecture through integrating terrestrial networks and nonterrestrial networks to achieve unified and efficient resource utilizing and network constructing. In the aspect of architecture design, because the UAV is the feasible options for deploying 5G services on demand, the paper entitled "The role of UAVs and mmWave in 5G: recent advances and challenge" by Shah Khalid Khan et al comprehensively surveys the state-of-art techniques in 5G-UAV relay networks, especially focuses on UAV relay architectures, identifies the relevant gaps, and challenges in the deployment of 5G-UAV relay networks. Despite the cellular system, unmanned aerial vehicle networks (UAVs), and satellite communication have made brilliant achievements and promoted great progress of human society, it would still exist diverse challenges in the face of future intelligent connection and global random access for the continuous independent development of terrestrial and nonterrestrial networks. [Extracted from the article]

The world's increasing dependence on computer‐assisted education systems has raised significant challenges about student assessment methods, such as automated test design. The exam questions should test the students' potential from various aspects, such as their intellectual and cognitive levels, which can be defined as attributes of the questions to assess student knowledge. Test design is challenging when various question attributes, such as category, learning outcomes, difficulty, and so forth, are considered with the exam constraints, such as exam difficulty and duration. In this paper, four contributions are provided to overcome test design challenges for the student assessment. First, a tool is developed to generate a synthetic question pool. Second, an objective function is designed based on the considered attributes. Third, the popular swarm and evolutionary optimization methods, namely particle swarm optimization, genetic algorithm, artificial bee colony, differential search algorithm are comparatively studied with novel methodologies applied to them. Finally, as the state of the art methods, artificial bee colony, and differential search algorithm are further modified to improve the solution of the test design. To perform the proposed algorithms, a dataset of 1000 questions is built with the proposed question attributes of the test design. Algorithms are evaluated in terms of their successes in both minimizing the objective function and running time. Additionally, Friedman's test and Wilcoxon rank‐sum statistical tests are applied to statistically compare the algorithms' performances. The results show that the improved artificial bee colony and the improved differential search provide better results than others in terms of optimization error and running time. [ABSTRACT FROM AUTHOR]

With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm. [ABSTRACT FROM AUTHOR]

Cloud computing provide services dynamically according to the contract between service providers and users. However, Inappropriateness of scheduling task on VMs can lead huge resource waste and load unbalance, which becomes a seriously challenging problem. Current Swarm intelligence algorithms like genetic algorithm (GA), particle swarm optimization (PSO) are combination of random initialization and local search algorithm. It avoids inconsistent results for different problem instances. However, existing Swarm intelligence works sometimes search the optima without analysing task scheduling situations comprehensively, global search efficiency is low and convergence is too early. In this paper, we propose SNSK‐IPSO algorithm, which develops as a two‐phases algorithm: enumerating all distributed solutions between VMs and tasks, finding the optimal solution through IPSO. It not only minimizes the execution time, but also improves resource utilization and load balance. Several experiments demonstrate that our novel algorithm outperforms others in terms of achieving load balance, higher resource utilization and lower execution times. [ABSTRACT FROM AUTHOR]

Consideration of collectives raises important questions about human rationality. This has long been known for questions about preferences, but it holds also with respect to beliefs. For one, there are contexts (such as voting) where we might care as much, or more, about the rationality of a collective than the rationality of the individuals it comprises. Here, a given standard may yield competing assessments at the individual and the collective level, thus giving rise to important normative questions. At the same time, seemingly rational strategies of individuals may have surprising consequences, or even fail, when exercised by individuals within collectives. This paper will illustrate these considerations with examples, provide an overview of different formal frameworks for understanding and assessing the beliefs of collectives, and it will illustrate how such frameworks can combine with simulations in order to elucidate epistemic norms. The paper examines the accuracy and rationality of beliefs of individuals in collectives and of the collectives themselves. [ABSTRACT FROM AUTHOR]

Extracting the parameters of a lead‐acid battery under real‐world operating conditions is a significant part of solar photovoltaic (PV) engineering. Usually, the battery management system handles the battery system based on its model. However, its model's parameters can change due to its electrochemical nature. Hence, enhancing the model parameters' accuracy is required to achieve a reliable and accurate model. This research employs an improved methodology for extracting lead‐acid battery data outdoors. The suggested method combines numerical and analytical formulations of parametric battery models for solar PV energy storage. The Shepherd model, which considers the battery's non‐linear properties, is selected in this paper. Based on a modern meta‐heuristic marine predator algorithm, the parameters of two solar lead‐acid batteries are discovered using an optimal parameter identification technique (MPA). The MPA exhibits its capability in terms of fast convergence and accuracy. The acquired test results are compared to those produced by the salp swarm algorithm, artificial eco‐system optimizer, hunger games search, a new optimization meta‐heuristic method that inspires the behavior of the swarm of birds called COOT, and honey badger algorithm in terms of efficiency, convergence speed, and identification accuracy. The findings demonstrated that the MPA outperformed the other optimizers in identifying ability. This optimizer achieved 99.99% identification efficiency for both Bergan and Banner battery types, making it an excellent battery identification option. [ABSTRACT FROM AUTHOR]

Lately, crowdsourcing has emerged as a viable option for getting work done by leveraging the collective intelligence of the crowd. With many tasks posted every day, the size of crowdsourcing platforms is growing exponentially. Hence, workers face an important challenge in selecting the right task. Despite the task filtering criteria available on the platform to select the right task, crowd workers find it difficult to choose the most relevant task and must glean through the filtered tasks to find the relevant tasks. In this paper, we propose a framework for recommending tasks to workers. The proposed framework evaluates the worker's fitment over the tasks based on the worker's preference, past tasks he/she has performed, and tasks done by similar workers. We also proposed an approach to estimate the right price for a crowdsourced task for a specific worker. We evaluated our approach on the datasets collected from popular crowdsourcing platforms. Our experimental results show that the recommendation made by our framework for task and price is significantly better as compared with the baseline approach. [ABSTRACT FROM AUTHOR]

Summary: The ability of high splitting gain of dense small cells contributed to the rapid establishment of ultradense networks (UDNs). Its higher efficiency to deal with high traffic data demand made UDN a most‐promising technology for the future 5G environment. However, the UDN creates concern about user association, which causes more complexities in providing a high data transmission rate and low latency rate. To tackle these complexities, in this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods. The performance evaluation is carried out with regard to network lifetime, end‐to‐end delay, packet delivery ratio, sum rate overall energy consumption, latency, and minimum rate and maximum rate of the network. The proposed AEMDPG‐ARO algorithm gives better performance with reduced time complexity and better metrics rate in the result analysis. The minimum latency achieved by the proposed AEMDPG‐ARO algorithm is about 0.1 s. Key findings: The AEMDPG‐ARO approach is proposed to solve user association problems in UDN.Data sample for this study is obtained through the WINNER channel model.AEMDPG‐ARO approach achieves high data transmission and less latency rate.It offers better performance with reduced time complexity and better metrics rate. [ABSTRACT FROM AUTHOR]

Xuzhou, as an industrial centre and typical resource‐based city in China, is facing serious pressure on development transformation. The embedding of ecosystem services (ES) into land use planning (LUP) is of great significance to realize the coupling of ecology and society. This paper proposed a two‐step spatially explicit optimization approach of integrating ES into LUP, with consideration of macro‐requirements, spatial heterogeneity, and the spatially explicit basis. The first step was to construct a linear optimization model to obtain the land quantity structure corresponding to the maximized ES value. The second step was to spatially allocate land use structure to maximize the suitability of spatial units providing ES. The results showed that the land use structure corresponding to the maximization ES value of Xuzhou was obtained to satisfy the welfare of habitant and to create the ecological competitiveness. The optimal spatial layout of Xuzhou with maximum spatial suitability of providing ES was acquired through spatial optimization of this approach. ES was matched to the units with the high spatial suitability, and the spatial potential of ES was released. The conflicts among supporting services, provisioning services, regulating services, and cultural services were well managed with the equipment of multi‐objective trade‐off technology. The proposed ES embedding approach has good performance in the optimal allocation of land resources for ES maximization and in managing trade‐offs during multi‐objectives programming. Therefore, it is expected to be widely used for ES‐oriented LUP formulation. [ABSTRACT FROM AUTHOR]

The development of integrated circuits for displays and other applications requires semiconductor device models and appropriate parameter extraction techniques to predict and understand the circuit behavior. These techniques are paramount in reducing design errors and shortening the product development cycle. This paper presents an algorithm that employed swarm intelligence in exploring an automated and accurate parameter extraction technology. First, an automatic parameter extraction of Rensselaer Polytechnic Institute (RPI) Model for polysilicon thin-film transistor (Poly-Si TFT) is achieved by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Compared with the best solution of the GA algorithm for automatic parameter extraction, the PSO outperformed the GA. However, it still prematurely converges to the suboptimal solution henceforth cannot obtain the expected solution accuracy. Second, the mutual learning particle swarm optimization (MLPSO) algorithm is proposed that introduces the concept of "mutual learning." The new algorithm aims to find the global optimum in getting suitable trade-off between exploration and exploitation. In addition, the MLPSO algorithm implemented the novel random initialization and fitness function in simplifying the complex manual processes and the empirical calibration, and it led to achieving automatic and accurate parameters extraction. [ABSTRACT FROM AUTHOR]

Multi‐objective optimization problems frequently appear in many diverse research areas and application domains. Metaheuristics, as efficient techniques to solve them, need to be easily accessible to users with different expertise and programming skills. In this context, metaheuristic optimization frameworks are helpful, as they provide popular algorithms, customizable components and additional facilities to conduct experiments. Due to the broad range of available tools, this paper presents a systematic evaluation and experimental comparison of 10 frameworks, covering from multi‐purpose, consolidated tools to recent libraries specifically designed for multi‐objective optimization. The evaluation is organized around seven characteristics: search components and techniques, configuration, execution, utilities, external support and community, software implementation and performance. An analysis of code metrics and a series of experiments serves to assess the last two features. Lesson learned and open issues are also discussed as part of the comparative study. The outcomes of the evaluation process reveal a contrasted support to recent advances in multi‐objective optimization, with a lack of novel algorithms and variety of metaheuristics other than evolutionary algorithms. The experimental comparison also reports significant differences in terms of both execution time and memory usage under demanding configurations. [ABSTRACT FROM AUTHOR]

Summary: Vehicular ad hoc networks (VANETs) are the ultimate solution for preventing road accidents, which result in the loss of precious human life worldwide. In this context, effective communication between the vehicular nodes is essential due to the varying network topology and high vehicular mobility inherent with VANETs. Cluster‐based routing is identified to be a significant approach for achieving efficient routing and improving communication proficiency in VANETs. In this paper, a beetle colony optimization algorithm–based clustering scheme (BCOACS) is proposed for generating optimized clusters for facilitating reliable data dissemination. This BCOACS algorithm includes two vital strategies such as beetle antenna search (BAS) and swarm intelligence for attaining inter‐cluster and intra‐cluster communications. In specific, BAS strategy that includes random search attributed toward gradient direction is used for intra‐cluster communication without using the complete amount of gradient information. On the other hand, a swarm intelligence strategy that encompasses a collective approach of self‐organized and decentralized agents is used for inter‐cluster communication with the view to minimize the load on each cluster head (CH) and to extend the clusters' lifetime. The simulation outcomes of the proposed BCOACS scheme confirmed improved performance in optimizing the number of constructed clusters independent of the increase in the network grid size, transmission range, and number of vehicular nodes in the network compared to the benchmarked approaches. The results also confirmed that the proposed BCOACS scheme achieved a maximized throughput of 13.42%, with reduced delay and protocol overhead of 18.96% and 19.45%, better than the benchmarked schemes used for investigation. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment. [ABSTRACT FROM AUTHOR]

The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one of the main contributors is the Internet of Things. Smart factories and healthcare have both benefited in terms of leveraged quality of service and productivity rate. However, there is always a trade‐off and some of the largest concerns include security, intrusion, and failure detection, due to high dependence on the Internet of Things devices. To overcome these and other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion detection, computer‐aided diagnostics, and so forth. However, efficiency of machine learning models heavily depend on feature selection, predetermined values of hyper‐parameters and training to deliver a desired result. This paper proposes a swarm intelligence‐based approach to tune the machine learning models. A novel version of the firefly algorithm, that overcomes known deficiencies of original method by employing diversification‐based mechanism, has been proposed and applied to both feature selection and hyper‐parameter optimization of two machine learning models—XGBoost and extreme learning machine. The proposed approach has been tested on four real‐world Industry 4.0 data sets, namely distributed transformer monitoring, elderly fall prediction, BoT‐IoT, and UNSW‐NB 15. Achieved results have been compared to the results of eight other cutting‐edge metaheuristics, that have been implemented and tested under the same conditions. The experimental outcomes strongly indicate that the proposed approach significantly outperformed all other competitor metaheuristics in terms of convergence speed and results' quality measured with standard metrics—accuracy, precision, recall, and f1‐score. [ABSTRACT FROM AUTHOR]

In this paper, we are proposing two novel swarm intelligence approaches for solving discrete problems. Two different continuous evolutionary algorithms inspired by the behaviour of elephants, namely elephant herding optimization and elephant swarm water search algorithm, are studied and analysed in order to propose discrete versions of the latter called discrete elephant herding optimization and discrete elephant swarm water search algorithm. As an illustration of how our proposals can work on discrete problems, a case study on association rule mining is carried out where the proposed discrete algorithms are modelled and applied on the problem in order to extract interesting association rules from large‐scale databases. Extensive experiments on eight different real‐life and relevant datasets with various sizes showed that both our proposals yield good results that compete and sometimes outperform state of the art algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel and robust predictive method using modified spider monkey optimization (MSMO) and probabilistic neural network (PNN) for face recognition. The limitation of the traditional spider monkey optimization (SMO) approach to obtaining an optimal solution for classification problems is overcome by enhancing the performance of SMO by modifying the perturbation rate with a non‐linear function, thereby improving the convergence of SMO. The framework comprises image preprocessing, feature extraction using dual tree complex wavelet transform (DT‐CWT), feature selection using the modified spider monkey optimization algorithm (MSMO), and classification using PNN. The proposed method is tested on the Yale and AR Face datasets. Experimental outcomes reveal that the proposed framework attain an accuracy of 99.4% with appreciable sensitivity, specificity, and G‐mean. To examine the efficacy of MSMO, parametric studies are conducted, which showed that MSMO converges faster with high fitness when compared to similar evolutionary algorithms like Genetic Algorithm (GA), Grey Wolf Optimization Algorithm (GWO), Particle Swarm Algorithm (PSO), and Cuckoo Search (CS) in selecting the optimal feature set. The MSMO‐PNN method outperforms similar state‐of‐the‐art methods, which reveals that the method proposed is competitive. The proposed model is robust to Gaussian and salt–pepper noise, obtaining the highest accuracy of 97.89% for varied noise density and variance. [ABSTRACT FROM AUTHOR]

Summary: The next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate. [ABSTRACT FROM AUTHOR]

Recipe prediction is one of the most critical steps in the fabric dyeing industry. The conventional Kubelka–Munk model and neural network techniques have been widely used in recipe prediction systems. However, there are some limitations to these two methods: predictions using the Kubelka–Munk model may not be robust enough; and neural networks require large amounts of training data. Therefore, this paper investigates a novel recipe prediction method for fabric dyeing based on feature‐weighted support vector regression and particle swarm optimisation. Feature‐weighted support vector regression improved with particle swarm optimisation was first developed to predict the CIELab coordinates for given dye concentrations, expressed as c1c2...cn⇒L*a*b*. Particle swarm optimisation was utilised again in the recipe prediction stage to search for the optimal recipe in an iterative process. The optimisation criterion is to minimise the colour differences (CMC [2:1]) between the CIELab value calculated by feature‐weighted support vector regression improved with particle swarm optimisation and the target CIELab of a swatch. Dyeing data based on two different fabrics (cotton and taffeta) were used in the experiment. The proposed method revealed good results with a slight average colour difference between the target and reproduced colours. The absolute percentage errors in predicting concentrations were less than 5% in most of our experimental recipes. In addition, the comparative experimental results illustrate that our method had higher accuracy and better practical applicability than other methods. [ABSTRACT FROM AUTHOR]

A high yield estimation is necessary for designing analogue integrated circuits. In the Monte‐Carlo (MC) method, many transistor‐level simulations should be performed to obtain the desired result. Therefore, some methods are needed to be combined with MC simulations to reach high yield with high speed at the same time. In this paper, a four‐stage yield optimisation approach is presented, which employs computational intelligence to accelerate yield estimation without losing accuracy. Firstly, the designs that met the desired characteristics are provided using critical analysis (CA). The aim of utilising CA is to avoid unnecessary MC simulations repeating for non‐critical solutions. Then in the second and third stages, the shuffled frog‐leaping algorithm and the Non‐dominated Sorting Genetic Algorithm‐III are proposed to improve the performance. Finally, MC simulations are performed to present the final result. The yield value obtained from the simulation results for two‐stage class‐AB Operational Transconductance Amplifer (OTA) in 180 nm Complementary Metal‐Oxide‐Semiconductor (CMOS) technology is 99.85%. The proposed method has less computational effort and high accuracy than the MC‐based approaches. Another advantage of using CA is that the initial population of multi‐objective optimisation algorithms will no longer be random. Simulation results prove the efficiency of the proposed technique. [ABSTRACT FROM AUTHOR]

Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distinctive features of human intelligence. In this paper, we show that information accumulation in multigenerational social networks can be produced by a form of distributed Bayesian inference that allows individuals to benefit from the experience of previous generations while expending little cognitive effort. In doing so, we provide a criterion for assessing the rationality of a population that extends traditional analyses of the rationality of individuals. We tested the predictions of this analysis in two highly controlled behavioral experiments where the social transmission structure closely matched the assumptions of our model. Participants made decisions on simple categorization tasks that relied on and contributed to accumulated knowledge. Success required these microsocieties to accumulate information distributed across people and time. Our findings illustrate how in certain settings, distributed computation at the group level can pool information and resources, allowing limited individuals to perform effectively on complex tasks. Blurb: Many of the problems people face are difficult to solve under the limited time and resources available to them. We show that individuals can overcome these limitations by following a simple social learning heuristic that yields distributed Bayesian inference at the population level. We test our model in two large behavioral experiments, comparing observed knowledge accumulation with the Bayesian ideal in multigenerational microsocieties. [ABSTRACT FROM AUTHOR]

Summary: Despite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real‐time location‐based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best‐fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization. [ABSTRACT FROM AUTHOR]

In this paper, an improved quantum‐behaved particle swarm optimization (QPSO) approach for modeling antenna impedance is illustrated. In the proposed study, the enhanced weighted quantum particles swarm optimization (EWQPSO) is introduced with the aim to achieve local convergence acting on a reduced number of free parameters. To verify the performance of the proposed EWQPSO, several tests involving Ipersphere, Alpine, De Jong, Zakharov, Salomon functions were carried out. The obtained results demonstrated that the convergence is achieved more quickly by the EWPSO than other optimization algorithms based on QPSO. A lumped element equivalent circuit was designed to model the terminal impedance of a broadband planar sinuous antenna in the frequency range from 1 to 3 GHz. The developed EWQPSO algorithm is then used to recover all the parameters characterizing the equivalent circuit. The resulting circuit exhibited a good impedance fidelity over the whole frequency range. Key Points: Quantum–inspired PSO for solving complex electromagnetic problemsLumped element equivalent circuit for modeling the driving point antenna impedanceEffective methodology for synthesising broadband equivalent circuits [ABSTRACT FROM AUTHOR]

This paper unveils the simulation and optimum design of five models based on photovoltaic (PV), diesel generator (DG), and battery energy systems for rural area farmhouse load demand (agriculture and household). The choice for designing an optimal energy system is constructed on minimum technical considerations like loss of load probability (LOLP), economic (cost of electricity [COE] with net present cost [NPC]), environmental (CO2 aspects) using a novel flamingo swarm intelligence algorithm (FSIA). The optimization results are compared by particle swarm intelligence algorithm, improved particle swarm intelligence algorithm, genetic algorithms, and hybrid optimization of multiple energy resources (HOMER‐Pro). FSIA shows PV/DG/battery energy system to be an optimal system as regards NPC (Rs. 8 056 734), COE (12.14 Rs./kWh), CO2 (13 784 kg/y), and LOLP (0.00000501 per year). The saving of the fuel cost and CO2 emission from PV/DG/battery to DG only system is 66.10% and 61.26%, respectively. [ABSTRACT FROM AUTHOR]

Linear Regression (LR), as one of the essential Machine Learning (ML) models, incurs massive data crunching during the training phase based on many data points. Considering the computationally intensive nature in the LR models, an optimized dedicated hardware IP core design can be very effective. This paper proposes the following novelties: (a) an optimized hardware IP core design of linear regression‐based machine learning model using high‐level synthesis (HLS). More specifically, independent application specific datapath architectures of hardware IP for computing optimal bias and intercepts and cost function in LR‐ML are presented here; (b) an optimized hardware IP core design of LR based ML model by deducing dependency graph from its corresponding mathematical foundation; (c) register transfer level (RTL) design, using HLS, of the optimized LR based ML hardware IP core for computing cost function; (d) linear regression IP core design using multi‐layered tree‐height transformation (THT) and swarm intelligence based architectural exploration for optimized HLS design. [ABSTRACT FROM AUTHOR]

Summary: Recent research introduces data gathering using mobile data collectors to conserve energy and elevate the hotspot problem. However, many of them suffer from buffer overflow due to the limited memory capacity of objects, even more, when dealing with heterogeneous Internet of Things (IoT) devices. Emerging bio‐inspiration technologies provide a novel direction for data collection and make it more intelligent and available. In this paper, we present an intelligent data gathering schema by taking into consideration buffer overflow called Center Gravity Mobile data collector based on Salp Swarm Algorithm (CGMSSA). To conduct data collection to improve the network performance, we adopt a hybridized distributed bio‐inspired technique to elect chefs and salp swarm intelligence to control data collector movement. First, we select chefs using grey wolf optimization. Then, several groups are formed. Then, a mobile data collector is adopted to access chefs, following the buffer overflow value and gather information. Considerable experiments are conducted to demonstrate that our solution can efficiently enhance the lifetime of the network and increase data throughput. [ABSTRACT FROM AUTHOR]

Summary: Industrial internet‐of‐things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high‐order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high‐order optimal density selection (SHODS3O‐CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization‐based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high‐order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods. [ABSTRACT FROM AUTHOR]

6G networks are envisioned to provide an extremely high quality‐of‐service (QoS). Then, future 6G network must operate in the Ka‐band, where more bandwidth and radio channels are available, and noise and interferences are lower. But even in this context, 6G base stations must adjust the transmission power to ensure the signal‐to‐noise ratio is good enough to enable the expected QoS. However, 6G networks are not the only infrastructure operating in that band. Actually, many scientific instruments are also working on those frequencies. Considering that 6G networks will be transmitting a relevant power level, they can interfere very easily with these scientific instruments. Therefore, in this paper we propose a new solution to enable the interferenceless coexistence between 6G networks and scientific instruments. This solution includes a three‐dimensional model to analyse future positions of user devices. Using this information and an interference model, we design a decision model to adapt the transmitted power, so the QoS achieves the expected level. Besides, when the transmitted power is high enough to interfere with close scientific instruments, a scheduling algorithm based on swarm intelligence is triggered. This algorithm calculates the optimum distribution of time slots and radio channels, so the scientific instruments can operate, and the 6G networks can still provide the required QoS. An experimental validation is provided to analyse the performance of the proposed solution. Results show a complete coexistence may be achieved with an interference level of −26 dBm and a QoS above 95% of the expected level. [ABSTRACT FROM AUTHOR]

Automatic parameter extraction of RPI Model for Polysilicon Thin‐Film Transistors is achieved by genetic algorithm(GA) and Particle swarm optimization(PSO) algorithm, and the solution of two algorithms are compared. Furthermore, mutual learning particle swarm optimization (MLPSO) algorithm is proposed, which simplifies the complex manual processes and the empirical calibration, and achieves accurate parameters extraction. [ABSTRACT FROM AUTHOR]

The article discusses the evolving perspective in cognitive science towards understanding how individual cognition is connected to collective efforts and cultural evolution. The editors of the journal issue, Steven A. Sloman and Nathaniel Rabb, have compiled a collection of papers that explore how cognitive processes and functions, such as memory and problem-solving, are influenced by and shape group coordination. The article emphasizes the importance of cumulative culture and language in human uniqueness and highlights the shift in cognitive science towards studying the organization and distribution of knowledge. The introduction to the journal issue is described as thrilling, captivating, and thought-provoking. [Extracted from the article]

In Wireless Sensor Networks (WSN), there are three conflicting important issues, including extensive coverage, low node redundancy, and energy consumption for secondary deployment. To balance network coverage and longevity, a WSN coverage model with three objectives is developed. A novel multi‐strategy gold rush optimizer is proposed for the coverage optimization problem in WSN. First, the fusion search method becomes extensive and flexible by combining the migration strategy with the Levy flight mechanism and joining a follower group. Then, the dynamic opposite learning strategy is used to increase population diversity in the panning stage. In addition, a multi‐player collaborative strategy is utilized to improve search ability of the algorithm in the collaboration stage. Finally, the random differential mutation method is used to try to assist the algorithm in escaping local optima. The simulation results show that this algorithm compared with other algorithms performs better on all three objectives considered. [ABSTRACT FROM AUTHOR]

Alignment of each optical element at a synchrotron beamline takes days, even weeks, for each experiment costing valuable beam time. Evolutionary algorithms (EAs), efficient heuristic search methods based on Darwinian evolution, can be utilized for multi-objective optimization problems in different application areas. In this study, the flux and spot size of a synchrotron beam are optimized for two different experimental setups including optical elements such as lenses and mirrors. Calculations were carried out with the X-ray Tracer beamline simulator using swarm intelligence (SI) algorithms and for comparison the same setups were optimized with EAs. The EAs and SI algorithms used in this study for two different experimental setups are the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). While one of the algorithms optimizes the lens position, the other focuses on optimizing the focal distances of Kirkpatrick-Baez mirrors. First, mono-objective evolutionary algorithms were used and the spot size or flux values checked separately. After comparison of mono-objective algorithms, the multi-objective evolutionary algorithm NSGA-II was run for both objectives - minimum spot size and maximum flux. Every algorithm configuration was run several times for Monte Carlo simulations since these processes generate random solutions and the simulator also produces solutions that are stochastic. The results show that the PSO algorithm gives the best values over all setups. [ABSTRACT FROM AUTHOR]

Dike swarms are ubiquitous on terrestrial planets and represent the frozen remnants of magma transport networks. However, spatial complexity, protracted emplacement history, and uneven surface exposure typically make it difficult to quantify patterns in dike swarms on different scales. In this study, we address this challenge using the Hough transform (HT) to objectively link dissected dike segments and analyze multiscale spatial structure in dike swarms. We apply this method to swarms of three scales: the Spanish Peaks, USA; the Columbia River Flood Basalt Group (CRBG), USA; the Deccan Traps Flood Basalts, India. First, we cluster dike segments in HT space, recognizing prevalent linearly aligned structures that represent single dikes or dike packets, with lengths up to 10 − 30x the mapped mean segment length. Second, we identify colinear and radial dike segment mesoscale structures within each data set, using the HT to segment swarms into constituent spatial patterns. We show that for both the CRBG and Deccan Traps, a single radial or circumferential swarm does not well characterize the data. Instead, multiple and sometimes overlapping mesoscale linear and radial features are prevalent suggesting a complex history of crustal stresses. The HT can provide useful insights in a variety of geologic settings where many quasi‐linear features, at any scale, are superimposed spatially. Plain Language Summary: Dikes act as pipelines to transport magma from the deep Earth to the surface where it can erupt. Some of the largest concentrations of dikes on Earth occur in ancient continental flood basalts (CFBs), areas of massive volcanic output, but the spatial complexity and scale of these dike swarms has been a barrier to understanding the patterns within. We develop a new method to characterize distributions of linear features, such as dike swarms, inspired by tools and algorithms from image processing. We apply this tool to two CFBs, the Columbia River Flood Basalt Group, USA, and the Deccan Traps, India, as well as a smaller swarm in the Spanish Peaks, USA. We find numerous small packets of aligned segments and larger, radial, and linear patterns of dikes. Key Points: Superimposed spatial patterns in dike swarms are revealed by the Hough TransformDeccan Traps and Columbia River Flood Basalts exhibit multiscale overlapping dike swarm structuresLinear and radial mesoscale swarm structures are identifiable in Hough space [ABSTRACT FROM AUTHOR]

Multiethnic group countries are distinctive in that ethnic identification/loyalty and national identification/loyalty sometimes contend. In addition, the different ethnic groups that make up these countries though interdependent compete for relevance. Such a mode of social relationship creates uncertainty and heightens the consciousness of group survival. These characteristics make African countries rich sites for the investigation of intergroup relations. Given this context, the need for closure and cultural intelligence were examined in relation to ethnic identification. Data were collected via the use of questionnaires from a sample of undergraduate students drawn from different ethnic groups. Findings show that the need for closure and cultural intelligence are directly related to ethnic identification. Also, cultural intelligence was a significant moderator of the relationship between need for closure and ethnic identification. Findings suggest that cultural intelligence may encourage an open‐mindedness which could help promote successful social interactions in multigroup countries. In addition, the results of this study support theoretical and empirical positions that have advanced group interdependence as a potent tool for intergroup cooperation. [ABSTRACT FROM AUTHOR]

The use of quantum‐based NMR methods to complement and guide the connectivity and stereochemical assignment of natural and unnatural products has grown enormously. One of the unsolved problems is related to the improper calculation of the conformational landscape of flexible molecules that have functional groups capable of generating a complex network of intramolecular H‐bonding (IHB) interactions. Here the authors present MESSI (Multi‐Ensemble Strategy for Structural Identification), a method inspired by the wisdom of the crowd theory that breaks with the traditional mono‐ensemble approach. By including independent mappings of selected artificially manipulated ensembles, MESSI greatly improves the sense of the assignment by neutralizing potential energy biases. [ABSTRACT FROM AUTHOR]

Balancing value creation and value capture is a fundamental strategic issue for the management of open innovation. Insufficient compensation for created value may hinder the participation of a firm or individual in open innovation. It can thus provide an obstacle to the open innovation process as a whole. Hackathons provide an attractive setting for studying value appropriation in open innovation by actors of different types and with varying bargaining power. We define hackathons as idea competitions on specific topics in the form of a time‐limited event. These competitions have gained more popularity throughout the years and have recently become more prominent. Therefore, an abductive empirical study was carried out in an international set‐up with multiple embedded cases of hackathons. Results indicate that hackathons offer coupled open innovation processes. The value captured by the initiator of a hackathon in the form of inbound open innovation is balanced by outbound knowledge flows towards participants as well as with sideways knowledge flows between participants, which are a result of the generation of collective intelligence. Collective intelligence is thus identified as an alternative mechanism for value capture from open innovation. [ABSTRACT FROM AUTHOR]

This article proposes an optimization‐based approach to selecting the sampling periods for classes of linear time‐invariant (LTI) regulators in a cascaded multi‐rate control structure, to account for practical implementation on numeric systems. Starting from the Shannon‐Nyquist sampling theorem, which proposes the minimum required sampling frequency for completely recovering the original analog signal from its discrete samples, the scope of the sampling process, viewed from a control perspective, is to ensure asymptotic stability of the closed‐loop system, with the smallest possible degradation of the initially‐proposed performance indices as possible according to a set of well‐picked criteria. As such, a configurable optimization‐based approach is proposed, solved with the mixed‐integer artificial bee colony (MI‐ABC) algorithm, for selecting the sampling periods by specifying the emphasis between closed‐loop fidelity and ease of implementation, with an intuitive visualization method. The proposed solution is illustrated on two case studies, with emphasis on the construction of the proposed functionals and their practical implications. [ABSTRACT FROM AUTHOR]

Due to rising of power demands and distributed renewable power saturation, determining optimal capability of the battery energy storage system (BESS) and demand response (DR) inside the microgrid (MG) is critical. To overcome these issues, research proposed in this manuscript employs a hybrid swarm intelligence approach that incorporates game theory. Both BESS and DR concepts are used in the hybrid method operation. The proposed method employs multi‐agent guiding particle swarm optimization (MAPS) and Halton sequence and social motivation strategy based on the grey wolf optimizer (HSGW). The HSGW method helps in minimizing power utilization and energy loss. The results show that, when compared with the traditional methodology, the proposed MAPS‐HSGW method provides the cost‐effective selection between the agents and also decreases the operational cost by about 7.5%. Moreover, it was discovered that the proposed MAPS‐HSGW method outperforms other techniques in terms of results' quality and computational time metrics. [ABSTRACT FROM AUTHOR]

The factors controlling earthquake swarm duration are remain unclear, especially in the long‐living ones. A severe earthquake swarm struck the tip of the Noto peninsula, Japan. Ten M > 4.0 earthquakes occurred, and the sequence has continued more than 4 years. We investigated the spatiotemporal characteristics of the swarm using relocated hypocenters to elucidate the factors causing this long duration. The swarm consists of four seismic clusters—northern, northeastern, western, and southern—the latter of which began first. Diffusive hypocenter migrations were observed in the western, northern, and northeastern clusters with moderate to low diffusivities, implying a low‐permeability environment. Rapid diffusive migration associated with intermittent seismicity deep within the southern cluster suggests the presence of a highly pressurized fluid supply. We conclude that the nature of this fluid supply combined with intermittent seismicity from the southern cluster and a low‐permeability environment are the key causes of this long‐living swarm. Plain Language Summary: Earthquake swarms are sequences of several earthquakes occurring in a concentrated area over a given period. Unlike other major earthquakes, which have one main shock and several subsequent aftershocks, swarms lack a clear mainshock event. The causes of long‐lasting earthquake swarms are not sufficiently understood. In the northeastern tip of the Noto Peninsula in Japan, more than 20,000 earthquakes occurred between May 2018 and June 2022, including 10 events over magnitude 4.0. To understand the controlling factors of this long‐living earthquake swarm, we investigated the spatiotemporal characteristics of the swarm using high‐resolution relocated hypocenter locations. The hypocenters of the swarm are spatially separated in four clusters and initiated from the southern cluster. We also observed a diffusive pattern in hypocenter distribution, which is typical of earthquake swarms surrounding volcanoes or fluid injection wells, implying the existence of fluid as a driving factor of the swarm. In the southern cluster specifically, we found several intermittent seismic activities with rapid diffusive changes in hypocenter distribution, suggesting the presence of a highly pressurized, deep‐source fluid supply. The intermittent fluid supply from the southern cluster toward the others and the relatively low‐permeability environment are key factors in the longevity of this earthquake swarm. Key Points: An energic and long‐living earthquake swarm has been observed in the northeastern tip of the Noto peninsula, JapanObserved diffusive hypocenter migrations suggest that pore fluid pressure migration is a driving factor of the swarmIntermittent seismicity at the bottom of the initial cluster suggests that a geyser‐like fluid supply is a key factor in swarm longevity [ABSTRACT FROM AUTHOR]

Summary: This article proposes an intellectual quorum sensing‐centered bacterial horde optimization (QBHO) exert for assessing and authenticating the optimization problems. Quorum detection realizes the finest place of the bacteria by apprehending the most awful point within the hunt area. This mechanism is utilized in this study for problem‐solving. With this bacterial behavior and location perceptive, the superlative optimum result is obtained. In this anticipated QBHO algorithm, the competence of bacteria is enhanced to be beneficial so that results can be enhanced. The fundamental group‐built optimization procedures for instance, particle swarm, ant cluster, biogeography‐built algorithm, simulated bee constellation procedure and typical bacterial herd optimization through the distinctive considerations remain simulated and then associated through the intended practice for its ratification of results with QBHO. The projected QBHO practice is exploited to ensue with the seven‐typical benchmark with unimodal and multimodal trial functions intended to prove its practicability and ideality. The realized findings imply that the suggested QBHO approach surpasses the deemed optimization procedures for complex standard functions. Furthermore, the intended procedure might be appropriate to several engineering challenges, notably intended for intricate concurrent optimization problems. [ABSTRACT FROM AUTHOR]

Regression testing remains a promising research area for the last few decades. It is a type of testing that aims at ensuring that recent modifications have not adversely affected the software product. After the introduction of a new change in the system under test, the number of test cases significantly increases to handle the modification. Consequently, it becomes prohibitively expensive to execute all of the generated test cases within the allocated testing time and budget. To address this situation, the test suite reduction (TSR) technique is widely used that focusses on finding a representative test suite without compromising its effectiveness such as fault‐detection capability. In this work, a systematic review study is conducted that intends to provide an unbiased viewpoint about TSR based on various types of search algorithms. The study's main objective is to examine and classify the current state‐of‐the‐art approaches used in search‐based TSR contexts. To achieve this, a systematic review protocol is adopted and, the most relevant primary studies (57 out of 210) published between 2007 and 2022 are selected. Existing search‐based TSR approaches are classified into five main categories, including evolutionary‐based, swarm intelligence‐based, human‐based, physics‐based, and hybrid, grounded on the type of employed search algorithm. Moreover, the current work reports the parameter settings according to their category, the type of considered operator(s), and the probabilistic rate that significantly impacts on the quality of the obtained solution. Furthermore, this study describes the comparison baseline techniques that support the empirical comparison regarding the cost‐effectiveness of a search‐based TSR approach. Finally, it isconcluded that search‐based TSR has great potential to optimally solve the TSR problem. In this regard, several potential research directions are outlined as useful for future researchers interested in conducting research in the TSR domain. [ABSTRACT FROM AUTHOR]

With the rapid development of swarm intelligence, consensus problem for multi‐agent systems (MASs) has attracted substantial attention. To deal with the leader‐following consensus problems in stochastic dynamical MASs with fixed and switching topologies, this article designed proportional‐integral (PI) control protocols. On the basis of algebraic graph theory and stochastic analysis techniques, by selecting appropriate Lyapunov functions, it is theoretically shown that leader‐following consensus of MASs with stochastic dynamics underlying fixed and switching topologies can be achieved in mean square, respectively. Sufficient criteria are derived for selecting the PI control gains. Finally, the theoretical results are illustrated through several numerical simulations. [ABSTRACT FROM AUTHOR]

A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self‐learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d‐axis in a time‐sharing manner to solve the problem of equation under‐ranking, and the mathematical model for full‐rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self‐learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness. [ABSTRACT FROM AUTHOR]

This study presents a cohesive configuration controller for distributed space coverage by a swarm of robots. The goal is to build a dense, convex network that is robust against disconnection while robots are flocking with only incomplete knowledge about the network. The controller is an integrated framework of two different algorithms. First, we present a boundary force algorithm: physics‐based swarm intelligence that borrows the concept of surface tension force between liquid molecules. The combination of such a force with conventional flocking produces a convex and dense configuration without knowledge of the complete geometry of a robot network. Second, robots distributively determine when a configuration is on the verge of disconnection by identifying a local articulation point—a region where the removal of a single robot will change the local topology. When such a point is detected, robots switch their behavior to clustering, which aggregates them around the vulnerable region to remove every articulation point and retain a connected configuration. Finally, we introduced an index that objectively represents the level of risk of a robot configuration against the massive fragmentation, called vulnerability index. We provide theoretical performance analyses of each algorithm and validate the results with simulations and experiments using a set of low‐cost robots. [ABSTRACT FROM AUTHOR]

Optimizing current‐limiting devices (CLDs) in multi‐terminal DC grids is essential to limit the fault current while reducing the cost of devices. However, current studies mainly focus on optimizing CLDs based on continuous parameters in simple DC grids, which cannot meet the optimal efficiency demand of CLDs in complex multi‐terminal DC grids and results in each CLD needing to be customized separately, increasing the production cost of devices. Therefore, to improve the optimal efficiency under complex multi‐terminal DC grids, the principle of fault area division based on the coupled degree of converter and DC grid and the corresponding hierarchical optimal implementation schemes of CLDs are proposed first. Then, the effects of CLD parameters on the fault current are analysed, and their sensitive ranges of current‐limiting are revealed. In combination with the error range of inductance, the principle of discrete parameter design of current‐limiting inductance parameters is put forward. Next, the discrete parameters of CLDs are optimized by an intelligence algorithm. Finally, the hierarchical optimal method of CLDs based on discrete parameters is validated on a six‐terminal DC grid in PSCAD/EMTDC. The proposed method can satisfy the demand of current‐limiting, which is more in line with the standardization requirements in practice. [ABSTRACT FROM AUTHOR]

The determination of photovoltaic (PV) parameters is of great importance for the reliability of solar system operation, continuity of the load power consumption, and control management of the energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching and learning‐based optimization (TLBO), named TLBOBSA, to accurately simulate the PV model. During the evaluation of the proposed algorithm, the concept of teaching from TLBO is introduced into the BSA to guide optimal individuals, thus improving the convergence rate of the algorithm. The learning behavior among individuals in the student phase of TLBO facilitates interindividual learning and provides beneficial information for its evolution, which is introduced into the BSA to ensure the diversity of the population. The comprehensive test results of different PV module models in different environmental conditions show that the proposed algorithm is more advantageous for parameter extraction than other existing algorithms. This can be seen in the simulation experiments of two commercial PV models, where the simulated current is consistent with the measured current at each measured voltage. This demonstrates that the proposed TLBOBSA is an accurate and reliable tool for evaluating unknown parameters of PV models. [ABSTRACT FROM AUTHOR]

In this article, the paradigm of machine culture as an extension to machine intelligence is introduced. This new concept is modelled based on animal intelligence and culture. The example of orca intelligence and culture is considered as orcas possess in addition to skills allowing them to reach preys, the ability to transmit their culture from generation to generation. The orca intelligence is studied and then simulated to design an algorithm called Orca Algorithm (OA). OA consists in modelling the orca lifestyle and in particular the orcas social organization, echolocation behaviour and hunting techniques. In order to integrate the cultural dimension, OA was hybridized with the Cultural Algorithm (CA) to get an algorithm called Orca Cultural Algorithm (OCA). OCA was tested on 22 benchmark problems of the literature to evaluate its performance. Extensive experiments were first performed to set the algorithm parameters prior to measure its effectiveness and efficiency. In a second stage, OCA was adapted to discrete problems and applied to the maze game with four level of complexity. Additional experiments were held to compare the designed algorithm with recent state‐of‐the‐art evolutionary algorithms. The overall obtained results are very promising. [ABSTRACT FROM AUTHOR]