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Particle swarm optimizer (PSO) and mobile robot swarm are two typical swarm techniques. Many applications emerge separately along both of them while the similarity between them is rarely considered. When a solution space is a certain region in reality, a robot swarm can replace a particle swarm to explore the optimal solution by performing PSO. In this way, a mobile robot swarm should be able to efficiently explore an area just like the particle swarm and uninterruptedly work even under the shortage of robots or in the case of unexpected failure of robots. Furthermore, the moving distances of robots are highly constrained because energy and time can be costly. Inspired by such requirements, this article proposes a moving-distance-minimized PSO (MPSO) for a mobile robot swarm to minimize the total moving distance of its robots while performing optimization. The distances between the current robot positions and the particle ones in the next generation are utilized to derive paths for robots such that the total distance that robots move is minimized, hence minimizing the energy and time for a robot swarm to locate the optima. Experiments on 28 CEC2013 benchmark functions show the advantage of the proposed method over the standard PSO. By adopting the given algorithm, the moving distance can be reduced by more than 66% and the makespan can be reduced by nearly 70% while offering the same optimization effects.

In this article, an adaptive swarm control within saturated input based on the nonlinear coupling degree is proposed. Swarm control is a forward study in kinematics and dynamics of the swarm system. However, the coupling degree in the previous work can only manifest whether the agents in the swarm being connected or not, ignoring the connection strength. As a result, the nonlinear coupling degree is proposed, which is more suitable for practical engineering than the previous coupling degree. Based on the nonlinear coupling degree, we put forward novel swarm kinematics and dynamics. Besides, the effects of input saturation and nonlinear dynamics should be considered for this novel swarm control based on the nonlinear coupling degree. Therefore, we introduce the backstepping method to design an adaptive swarm controller. With this controller, the input saturation auxiliary system is designed to reduce the effects of input saturation, and a radial basis function neural network (RBF-NN) is introduced to approximate the nonlinear dynamics. To overcome the differential explosion in the backstepping method, a command filter is put forward to reduce the amount of calculation and reduces the difficulty of the controller design. It is proved that the proposed controller ensures stability based on the Lyapunov stability theory. Finally, the simulation results of a multiagent system composed of six omnidirectional mobile robots illustrate the validity of the proposed controller.

The independent metering system decouples the meter-in and meter-out flows of the chamber, giving extra control flexibility and can reducing energy consumption while keeping motion control accuracy. The control performance of the independent metering system is significantly determined by the valve characteristics. Valve nonlinearities, such as deadband, nonlinear flow gain, and leakage, may restrict the control accuracy. To address the issue and improve system control precision, the nonlinearities of the valve need to be further modeled. In this study, a nonlinear valve flow model is proposed to accurately depict the relationships between the flow rate, pressure drop, and input voltage. The valve deadband is compensated by the static inverse function, while a certain polynomial projection function is used to mimic the nonlinear flow curve outside the deadband. To deal with uncertainties and nonlinearities in the independent metering system, an adaptive robust control approach is synthesized. The least squares estimation law is adopted to online adapt the valve parameter and other system parameters. Comparative experiments were carried out to illustrate the effectiveness of the proposed approach. The results show that the valve flow nonlinearities are further captured by the proposed model, and the independent metering system performed a higher-level control precision.

In this paper we are presents the design and implementation of a smart energy meter using an PIC16F886 microcontroller and a liquid crystal display (LCD). It is used in residential consumer. It is an electric device having energy meter chip for measuring the electric energy consumed. It is also allowing the meter owner to have a meter utilized and the corresponding amount will be displayed on the LCD continuously. Also they can monitor the meter reading regularly without the person visiting each house. Microcontroller PIC16F886 to prevent and control the energy meter from power theft. In the voltage system whenever occurring of under voltage and over voltage condition then meter should be given a constant voltage. Keywords: Smart meter, PIC16F886 microcontroller, Power consumption, Voltage stabilizer and LCD display

An in-vitro nano-swarm has the potential to revolutionize the medical field, from the diagnosis of ailments to targeted delivery of drugs. A nanobot swarm uses swarm intelligence to allow simple devices to communicate in a decentralized, distributed manner, within any given environment to achieve a common goal or arrive at an optimal solution quickly. This review discusses the different factors to consider and components necessary to deploy an in-vitro nano-swarm for targeted drug delivery to eliminate cancerous cells in the brain. It also discusses some swarming techniques that can be implemented for the decentralized control and acoustic communication between individuals in the nanobot swarm. Finally, there is discussion about the different challenges faced while deploying a nanobot swarm in the brain. This review hopes to give direction to other researchers through a comprehensive compilation about different practices and advancements in the field.

Although enterprise architecture (EA) is a topic of significant interest among scholars and practitioners, limited empirical research exists examining EA in the context of routinization, use of standards, information technology (IT) flexibility, and enterprise agility. Specifically, the study’s purpose was to explore the routinization of EA and develop a better understanding of how the use of EA standards impact IT flexibility and enterprise agility. The current study used a theoretical lens that combined competence-based theory and diffusion of innovation theory to study the topic of EA. The overarching research question was To what extent do organizational factors such as EA routinization and use of enterprise architecture standards, enabled through the adoption of EA, lead to increases in IT flexibility and enterprise agility?.. The analysis from this study supported the findings that the EA routinization and the use of the EA standards constructs influence IT efficiency, and in turn, IT efficiency influences enterprise agility.

Complex computational problems are occurrences in our daily lives that needs to be analysed effectively in order to make meaningful and informed decision. This study performs empirical analysis into the performance of six optimisation algorithms based on swarm intelligence on nine well known stochastic and global optimisation problems, with the aim of identifying a technique that returns an optimum output on some selected benchmark techniques. Extensive experiments show that, Multi-Swarm and Pigeon inspired optimisation algorithm outperformed Particle Swarm, Firefly and Evolutionary optimizations in both convergence speed and global solution. The algorithms adopted in this paper gives an indication of which algorithmic solution presents optimal results for a problem in terms of quality of performance, precision and efficiency.

We propose a model of consumption inefficiency in collective households. Inefficiency depends on a ‘cooperation factor’, which can also affect both the allocation of resources within a household and the utility of household members. Households are conditionally efficient, conditioning on the value of the cooperation factor. This lets us exploit convenient modelling features of efficient households (like not needing to specify the bargaining process), while still accounting for, and measuring the dollar cost of, inefficient levels of cooperation.

In order to improve the effect of digital protection of cultural heritage, this paper analyzes the spatial and temporal characteristics of cultural heritage with the support of ArcGIS technology, conducts research on ArcGIS’ geographic information technology algorithm, and improves the algorithm with the support of digital technology. Moreover, this paper combines the watermark algorithm to digitally mark cultural heritage to improve the copyright effect of cultural heritage. In addition, this paper proposes a topological integrity verification method based on weak watermark vector geographic data to improve the reliability of digital processing of cultural heritage, and build an intelligent cultural heritage digital protection system model. The research results show that digital protection technology of cultural heritage based on the ArcGIS geographic information technology algorithm in this paper can play an important role in the digital protection of cultural heritage.

The whole process management of construction project is to carry out all-round supervision and management of the whole stage of construction project implementation, which can realize the effective allocation of construction project (CP) funds and improve the construction quality at the same time. On this basis, this article analyzes the impact of the whole-process management (WPM) model on the employment ability (EA) of college students (CS), uses computer-aided technology to calculate the information of graduates in a construction company, and analyzes according to the work situation of the graduates in the enterprise and the evaluation of the EA of the graduates by the enterprise cultivation path of university students’ EA. The experimental results of this paper show that the evaluation of the four structural elements of EA of CS by construction companies is higher than that of college students’ self-evaluation, indicating that there is a difference between the EA of students and the needs of enterprise EA, which can be used as an entry point to train students EA.

In this paper we describe application of the method of physical processes simulating to build a swarm of drones. We show that this method provides the construction of a swarm of drones in the form of a crystal lattice with specified parameters. We found those additional conditions that make it possible to assemble a swarm of disparate drones. We have proposed an algorithm that allows assembling a swarm with minimal costs using a simple measuring complex. Key words UAV, self-organization, physical processes simulating, crystal lattice, swarm robotics, sensors for UAV.

Spacecraft swarms can achieve mission objectives otherwise impossible by monolithic satellites. To this end, swarms need to autonomously reconfigure their relative motion while complying with space...

The purpose of this paper is to examine equipping households with some smart devices and to discuss the advantages of having and using them in households, as well as the reasons why households do not purchase these devices. The paper was written on the basis of secondary and primary sources of information. Direct research was conducted using the technique of an online survey on a sample of 620 representatives of households living in Poland in 2021. The results obtained indicate that households most often have house electronic devices, i.e., smart TVs and multimedia players. As for household appliances, most respondents possessed a smart vacuum cleaner, followed by a washing machine and a refrigerator. The most popular smart home automation devices are lighting (light bulbs) and heating (thermostats). The main advantage of using smart devices is to facilitate everyday activities by controlling devices from anywhere, whereas the reason why households do not purchase such devices is their high price and no need to possess them.

Distribution systems continue to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to solve overcurrent relay coordination problems; however, the implementation of these methods is time-consuming. Therefore, recent studies have adopted the utilisation of particle swarm optimization and genetic algorithms to solve overcurrent relay coordination problems and maximise system selectivity and operational speed. Particle swarm optimization and genetic algorithms are evolutionary algorithms that at times suffer from premature convergence due to poor selection of control parameters. Consequently, this paper presents a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on particle swarm optimizer and genetic algorithms performance, alternatively on the behaviour of overcurrent relays. Optimization algorithms aim to minimise overcurrent relay time multiplier settings and accomplish optimal protection coordination. The findings indicate that particle swarm optimization is more sensitive to inertia weight and swarm size while the number of iterations has minimal effect. The results also depict that 30% crossover, 2% mutation, and smaller population size yield faster convergence rate and optimise fitness function, which improves genetic algorithms performance. Sensitivity analysis results are verified by comparing the performance of particle swarm optimization with the genetic algorithms which show the former parameter setting outperforms the latter. The relay operational speed is reduced by 15% for particle swarm optimization and system selectivity is maximised. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and efficiency.

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

Analog circuit design can be formulated as a non-linear constrained optimisation problem that can be solved using any suitable optimisation algorithms. Different optimisation techniques have been reported to reduce the design time of analog circuits. A hybrid particle swarm optimisation algorithm with linearly decreasing inertia weight for the optimisation of analog circuit design is proposed in this study. The proposed method is used to design a two-stage operational amplifier circuit with Miller compensation. The results show that the proposed optimisation method can substantially reduce the design time needed for analog circuits.

Reusing heritage sites is no longer only a practice of maintaining the historic, built values of a heritage object, but increasingly also a practice of linking heritage buildings to other aims such as involving local (heritage) communities or integrating heritage in its urban context. However, working closely with local heritage communities and incorporating multiple aims and stakeholders–each with their own interests and understanding of heritage–makes projects of heritage reuse highly complex. To address and understand this complexity, various scholars argue for a co-evolutionary perspective that sees heritage as a manifestation of interrelated and interdependent processes. This paper translates the concept of co-evolution into a conceptual model for analysing practices of heritage reuse. We apply this model in sixteen selected European projects of heritage reuse, to analyse how and why co-evolution manifests itself in projects of heritage reuse. This analysis demonstrates that the actions of initiators and other actors in the heritage reuse projects, as well as the social/institutional system in which they operate, are conducive to whether or not a co-evolutionary heritage approach is enacted.

A swarm of robots has several macroscopic properties that are extraneous to an individual robot. This is a direct consequence of the fact that a swarm is a coupled high-dimensional system that is characterized by interactions between individual robots. In a Smoothed-Particle-Hydrodynamics (SPH) based control of a swarm of robots, the equations of motion may be parameterized by key parameters that affect the macroscopic properties of the swarm such as separation, compressibility, cohesivity, and velocity matching. In this paper, we analyze this relation between the parameters in the equations of motion and the properties of the swarm. The resulting analysis enables the design of a versatile, scalable controller that may be modified and adopted to suit the desired objectives of individual applications. We also present a new interaction potential that enables controlling the robot-robot separation, beyond maintaining the robot density, and providing a swarm cohesive force that keeps the robots in the swarm together. The constructed interaction potential is conservative, and the overall swarm stability may still be established using passivity arguments in the presence of a dissipative

Swarm robots are a branch of robotics that draws inspiration from biological swarms to mimic their collective behavior. Automatic design methods are part of swarm engineering, depend on artificial intelligence algorithms to produce the collective behavior of robots. In general, they follow two-approach evolutionary algorithms like practical swarm optimization and reinforcement learning. This paper studies these approaches, illustrating the effect of modifications and enhancements of algorithms for both directions, showing important parameters considered for the best performance of the swarm, and explaining the methods and advantages of using deep learning to reinforcement learning.

Many optimal control tasks for engineering problems require the solution of nonconvex optimisation problems, which are rather hard to solve. This article presents a novel iterative optimisation pro...

The application of the C4.5 Algorithm based on Particle Swarm Optimization to classify the level of sales of drugs that are often sold at the Bunda Azka Pharmacy, is a strategic thing to reduce the problems experienced by the pharmacy. Classify the level of sales of drugs sold using the C4.5 method. based on particle swarm optimization, to find out whether the C4.5 method based on particle swarm optimization (PSO) can optimize drug sales in the future. This research method uses a descriptive method, namely by conducting case study research by studying activities in the field, observing and interviewing stakeholders. in the initial step of this research is the determination of the attributes that will be processed into data mining with the help of rapidminer tools, this study the author uses the KDD model as a standardization in the data mining process. at the pharmacy. The data will later be processed using the c4.5 algorithm based on Particle Swarm Optimization to find the accuracy results of the prediction of the data. The data sample used is the number of 65 drug transaction records at the Bunda Azka Pharmacy. In the test results, the accuracy of Particle Swarm Optimization was 78.10%, for class recall drug sales was 72.50% and after using Particle Swarm Optimization increased to 78.33%, while precision had an accuracy of 77.92% and after using Particle Swarm Optimization increased to 80.33%. From the results of testing with Particle Swarm Optimization, there is an increase in accuracy of 7.15% from the research application of the C4.5 Method Based on Particle Swarm Optimization to Predict Drug Sales at Bunda Azka Pharmacy.

This work is aimed at solving the morbidity problem of the smart meter fusion model and improve the measurement accuracy and reliability of the smart meter. Starting with the topology of the smart meter, the reason for the serious morbidity of the smart meter model is discussed. First, the basic process of power system state estimation of smart meters is introduced, and the concept of error analysis of smart meters is clarified. Then, the causes and mechanisms of the ill-conditioned problems of the smart meter model are analyzed, and methods to reduce the morbidity of the smart meter calculation model are analyzed. Finally, a data optimization algorithm based on a greedy strategy and an improved Tikhonov regularization method is proposed. The model data is processed and optimized to reduce the morbidity of the smart meter measurement model. The results show that the analysis algorithm for reducing the morbidity error of the smart meter proposed in this study can effectively interfere with the morbidity of the smart meter calculation model. The processing effect shows that it can reduce the measurement error of the smart meter to about 5%, which is an order of magnitude lower than the error before processing, and the processing effect of the least square method is improved by more than 70%. From the perspective of processing speed, when the user number is between 50 and 100, the running time of the algorithm ranges between 1.5 and 3.5 s, which can be fully adapted to the actual situation and has strong practicability. In short, this study is helpful in improving the accuracy and reliability of smart meter calculations and provides a certain reference for related research.

Community engagement is becoming a key part of heritage management processes. Community-heritage engagement, however, also means that heritage management processes become more dynamic and versatile, as participation and community engagement is often complex, multifaceted, open-ended and unpredictable. This paper introduces a third, more radical perspective on community-heritage engagement, which we coin ‘a co-evolutionary heritage approach’. We argue that a co-evolutionary heritage approach is alive to the adaptability, flexibility and complexity that comes with the diversity of heritage valuation by communities.

Building information modelling (BIM) has been adopted in the architectural heritage industry. The digital protection method with BIM Technology as the core can introduce the information management workflow into the protection of architectural heritage, which can provide possibility for the complete preservation of all kinds of information related to the architectural heritage, improve the efficiency of protection, and meet the management needs of the whole life cycle of the architectural heritage. This paper is based on digital technology and combined with the characteristics of architectural heritage to create a BIM model of architectural heritage. The Autodesk Revit software, which is commonly used in China, is used as the entry point, and the C# language is used to develop Revit to make up for the shortcomings of traditional architectural heritage protection. At the same time, all kinds of information related to the architectural heritage are completely preserved, improving the efficiency of protection work, and meeting the information management needs in the process of building heritage maintenance and management.

PurposeEnterprise architecture (EA) is usually adopted as an approach for managing enterprise complexities and aligning business and information technology (IT) capabilities. Although IT practitioners and researchers agree about the potential applicability and benefits of EA in smart cities, little is known about the factors that influence the acceptance and usefulness of EA in smart cities. Thus, EA acceptance and usage remains a central concern of urban research and practice. Similarly, there are fewer studies that explored EA adoption from the context of enterprises that provide digital services in cities grounded on empirical evidence. Therefore, this study assesses the acceptance and usefulness of EA in smart cities context by developing an EA adoption model drawing on the DeLone and McLean Information System (IS) success model.Design/methodology/approachBased on the identified factors survey questionnaire was designed and sent out to participants which includes IT professionals, senior managers and consultants from 18 organizations in Norway and Ireland involved in a smart city project (+CityxChange) (https://cityxchange.eu/). Statistical Package for Social Science (SPSS) and structural equation modelling using partial least square was applied for data analysis.FindingsThe results suggest that service quality and system quality positively impact user satisfaction of EA and user's intention to use EA. More importantly, information quality does not influence user satisfaction of EA, and the user satisfaction of EA and user's intention to use EA significantly influences the net benefit of EA.Originality/valueThis study provides a complete understanding for academicians and IT practitioners regarding the factors and impacts on EA acceptance and use in smart cities. Finally, this study discusses the implications of this research and provides recommendations for future research.

This work uses genetic programming to explore the space of continuous optimisers, with the goal of discovering novel ways of doing optimisation. In order to keep the search space broad, the optimisers are evolved from scratch using Push, a Turing-complete, general-purpose, language. The resulting optimisers are found to be diverse, and explore their optimisation landscapes using a variety of interesting, and sometimes unusual, strategies. Significantly, when applied to problems that were not seen during training, many of the evolved optimisers generalise well, and often outperform existing optimisers. This supports the idea that novel and effective forms of optimisation can be discovered in an automated manner. This paper also shows that pools of evolved optimisers can be hybridised to further increase their generality, leading to optimisers that perform robustly over a broad variety of problem types and sizes., Comment: arXiv admin note: text overlap with arXiv:1910.00945

This paper introduces the application of swarm intelligence in relaying to detect symmetrical faults during power swing. The swarm intelligence assistance reduces the computational burden and enhan...

In previous work, we developed a single Evolutionary Algorithm (EA) to solve random instances of the Anshel-Anshel-Goldfeld (AAG) key exchange protocol over polycyclic groups. The EA consisted of six simple heuristics which manipulated strings. The present work extends this by exploring the use of hyper-heuristics in group-theoretic cryptology for the first time. Hyper-heuristics are a way to generate new algorithms from existing algorithm components (in this case the simple heuristics), with the EAs being one example of the type of algorithm which can be generated by our hyper-heuristic framework. We take as a starting point the above EA and allow hyper-heuristics to build on it by making small tweaks to it. This adaptation is through a process of taking the EA and injecting chains of heuristics built from the simple heuristics. We demonstrate we can create novel heuristic chains, which when placed in the EA create algorithms which out-perform the existing EA. The new algorithms solve a markedly greater number of random AAG instances than the EA for harder instances. This suggests the approach could be applied to many of the same kinds of problems, providing a framework for the solution of cryptology problems over groups. The contribution of this paper is thus a framework to automatically build algorithms to attack cryptology problems., Comment: 16 pages

The work is devoted to the analysis of the household appliances market and the main trends in its development. The structure and share of imports of household appliances are analyzed. The main leaders in the household appliances market are considered and the shares of their companies in the Russian Federation today are presented. The article also examines the development trends of the household appliances market.

We estimate the effect of labor-saving household technologies on female labor force participation. To establish the causal link between home durable ownership and household time allocation,...

To shed light on the debate over the effect of infrastructure investment on household consumption, this paper uses rural and urban household consumption data from the Chinese Household Income Project (CHIP) to analyse how infrastructure development affects the marginal spending behaviour of households. We find that although infrastructure stock crowds in total consumption, the effects of infrastructure development on sub‐categories of consumption differ considerably between the rural sample and the urban sample. Specifically, infrastructure development promotes urban households' spending on infrastructure‐based goods, and generates more spillover effects on rural households as indicated by the marginal consumption pattern shifting from necessities to more luxurious goods. Heterogeneity analyses show that the effect of infrastructure development is stronger in households with higher income and with a younger head.

In this article, Particle Swarm Optimisation (PSO) algorithm is used to obtain the controller gain for Automatic Generation Control (AGC) of the representation of an Interconnected Power System (IP...

This paper explores conceptualisations of households in sustainability transitions. Existing literature in the field has only engaged with households in a limited and fragmented way, despite the role of households in resource use, shaping social innovation and as a target of environmental policies. The paper asks: how are households conceptualised in sustainability transitions and how could this be further developed in future research? The paper uses systematic review to explore how the literature currently engages with households. We find two overarching approaches to households: a closed-box approach and an open-box approach. The closed-box approach engages households as a fixed unit of analysis, without attention to internal dynamics and how these may interact with system-level changes, whereas the open-box approach unpacks the household as a dynamic social unit and material setting in the context of multiple scales and other social units. Both approaches are further discussed in terms of how they engage with households as users, sites and sources. Based on the analysis, the paper proposes a comprehensive definition of households in transition studies, and identifies four areas for future research in sustainability transitions: households' and householders’ agency; unpacking household dynamics; household innovation across scales and socio-technical systems; and households in policy and governance.

The particle swarm optimization (PSO) algorithm has certain disadvantages; for instance, the convergence viscosity of the algorithm is reduced in the post evolution phase, the optimization search efficiency is also reduced, the algorithm is easy to be inserted with a local extremum during the calculation of a complex problem of a high-dimensional multiple extremum, and the convergence thereof is low. To compensate for the PSO disadvantage, we propose a particle swarm optimization of the comprehensive improvement strategy, which is a simple particle swarm optimization with a dynamic adaptive hybridization of the extremum disturbance and the ecds-PSO algorithm. This new comprehensively improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second-order to a first-order difference equation. The evolutionary process is controlled by the particle position variables only. The hybridization operation of increasing the extremum disturbance and introducing a genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and the plurality of a comparative experiment provide the following information: the improved particle swarm optimization is a simple and effective optimization algorithm that can enhance the algorithm accuracy, the convergence viscosity and the ability of avoiding the local extremum, and effectively reduce the calculation complexity.

The paper employs the particle swarm optimization (PSO) method for the identification of damage to a stringer web. This method is inspired by a swarm of bees or flock of birds which look for a common solution. It is a heuristic method, that utilizes a particle as a problem-solving candidate. The location of this particle in the search space represents the configuration of all the variables to possible solutions of the problem. As the particle moves into the solution space, the values of the variables change. The particle is defined by a position vector and a velocity vector. The resulting direction of the particle is determined by three components i.e. (1) the moment or inertia, (2) autobiographical or cognitive information and (3) social interaction. The quality of the particle is evaluated by a comparison function. In this study we tried to locate damage by using this method. The damage is represented by a rusted area on a stringer web, which was simulated as a change in the Young’s modulus. We used the modal calculation of the eigenfrequencies as an evaluation function.

Face image generation based on generative adversarial networks (GAN) is a hot research topic in computer vision. Existing GAN-based algorithms are constrained by training instability and mode collapse. Considering that particle swarm optimization (PSO) algorithm has good global optimization ability, we propose a generation antagonism network based on PSO algorithm to improve the training stability. More specifically, the inertia weight of particle swarm is improved by using the parameters of particle representative generator network in particle swarm optimization, and the aggregation degree of particles is judged to ensure the optimization ability of particle swarm optimization and the diversity of population. In addition, we evaluate the performance of the generator by generating quality and diversity evaluation functions to better guide the iterative updating of particle swarm optimization. Our face image generation experiment is conducted on CelebA dataset and experimental result shows the effectiveness and robustness of our proposed method.

This paper presents an analysis of embodiment of predicative metaphor which is an important topic in cognitive linguistic study. Previous researches are mainly about the identification, classification and construal of predicative metaphor, while its cognitive motivation has not been discussed. Based on the conceptual metaphor theory and embodied philosophy, the cognitive motivation of the metaphorical usage of English body-action verbs is discussed. It is concluded that the metaphorical usage of English body-action verbs arises from the embodied experience. Concepts related to human body are preferred to be the source domain of the cross-domain mapping used to understand other concepts. The metaphorical usage of English body-action verbs is created through human body metaphor.

The recently proposed multiobjective particle swarm optimization algorithms (MOPSO) based on the pairwise competition mechanism can not effectively solve multiobjective optimization problems, which are characterized by relatively poor diversity and convergence. In this paper, an improved MOPSO algorithm (TC-MOPSO) based on a tripartite competition mechanism is proposed. For this tripartite competition mechanism, the traditional swarm leaders and the external archive adopted in most MOPSO algorithms are not used, the particles are updated based on the tripartite competitors conducted in the current swarm at each generation. In each competition, three particles are selected from the swarm randomly, and the particles with lower fitness are updated separately by learning from the specified better particle, which aims to allow particles to search more widely in the space while maintaining the diversity of the algorithm. In addition, a novel progressive particle updating strategy is applied to update position, each particle learns from the specified better particle that has been updated, which can further improve the search efficiency including convergence rate and accuracy. Experimental results on 26 benchmark test instances and an application to the feature selection verify the superiority of the proposed algorithm over five state-of-the-art MOPSO algorithms and five competitive multiobjective evolutionary algorithms.

Digital textile printing has emerged as a sustainable alternative to the conventional screen printing. Similar to other forms of coloration, digitally printed cotton fabric also needs finishing to ...

Tolerance-cost optimisation, i.e. using optimisation techniques for tolerance allocation, is frequently used to determine a cost-efficient tolerance design that can meet the stringent requirements ...

Source optimisation (SO) is an approved approach to improve the imaging quality in inverse lithography techniques. It is critical to apply an optimisation approach with high convergence efficiency and minimum errors in pixel-based SO. To improve the convergence efficiency of the pixel-based SO, a route of particle swarm optimiser (PSO) combined with the adaptive nonlinear control strategy (ANCS) is proposed in this study. As a global optimisation algorithm, ANCS-PSO has the attributes of breaking away from the local optimum by adjusting the particle learning factor adaptively. In addition, the nonlinear control approach can broaden the search range and speed up the convergence of the iteration operation. The proposed approach also is compared with the linear decreasing inertia weight strategy and the simulated annealing strategy. The performance verification simulation displays the validity of PSO-ANCS and its potentials in SO with high convergence efficiency and optimisation capacity, by comparing the linear decreasing inertia weight strategy and the simulated annealing strategy.

Characterisation and quantification of nonlinearities in the engineering structures include selecting and fitting a good mathematical model to a set of experimental vibration data with significant nonlinear features. These tasks involve solving an optimisation problem where it is difficult to choose a priori the best optimisation technique. This paper presents a systematic comparison of ten optimisation methods used to select the best nonlinear model and estimate its parameters through nonlinear system identification. The model selection framework fits the structure’s equation of motions using time-domain dynamic response data and takes into account couplings due to the presence of the nonlinearities. Three benchmark problems are used to evaluate the performance of two families of optimisation methods: (i) deterministic local searches and (ii) global optimisation metaheuristics. Furthermore, hybrid local–global optimisation methods are examined. All benchmark problems include a free play nonlinearity commonly found in mechanical structures. Multiple performance criteria are considered based on computational efficiency and robustness, that is, finding the best nonlinear model. Results show that hybrid methods, that is, the multi-start strategy with local gradient-based Levenberg–Marquardt method and the particle swarm with Levenberg–Marquardt method, lead to a successful selection of nonlinear models and an accurate estimation of their parameters within acceptable computational times.

This paper aims at developing a system which helps in monitoring the readings from an energy meter and controlling the switching of energy meter. This system also has tamper switch, which helps in illegal removing of energy meter cabinet and alerts the authorities in the form of text message.

A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions

This article explores issues relating to interdisciplinary work between heritage and creative practice. In particular, issues in conceptualising music heritage are raised through an example of rese...

In order to improve the effect of agricultural economic structure optimisation, this paper builds an agricultural economic structure optimisation analysis system based on factor analysis and intell...

World Heritage is a profound expression of civilization. It connects the past with the present and the future. But the complexities and nuances for World Heritage in the new digital paradigm are no...

This research is aimed at finding how the students of English Department of State University of Gorontalo perceive the thing as revealed in the metaphor they used in their English Poem and, as well, interpreting the metaphor. This research was designed in qualitative research methodology. The subject of research was the poems written by the students in 2010. It is found that the students used metaphor to express their feeling. The metaphor reflects the students’ perception of the reality categorized in semantic fields. In terms of the intended meaning of the metaphor, the researcher inferred the meaning by finding the metaphor predications based on the researcher knowledge of the predication.

Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

The goal of this experiment is to examine the potential use of gratitude to encourage cooperation during a resource dilemma task