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The problems in equipment fault detection include data dimension explosion, computational complexity, low detection accuracy, etc. To solve these problems, a device anomaly detection algorithm based on enhanced long short-term memory (LSTM) is proposed. The algorithm first reduces the dimensionality of the device sensor data by principal component analysis (PCA), extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss, and then uses the enhanced stacked LSTM to predict the extracted temporal data, thus improving the accuracy of anomaly detection. To improve the efficiency of the anomaly detection, a genetic algorithm (GA) is used to adjust the magnitude of the enhancements made by the LSTM model. The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection, with the recall rate of 97. 07%, which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment. [ABSTRACT FROM AUTHOR]

With the aim of addressing the complexity of decision environments, uncertainty of decision information and weight determination of mutual influence between decision makers, a (p,q)-rung interval-valued orthopair fuzzy multi-attribute group decision making algorithm based on weight optimization is proposed. First, in order to improve the ability of decision makers to capture their judgment in a wider space, the concept of a (p,q)-rung interval-valued orthopair fuzzy set is proposed, and its related definition and properties are studied. Second, considering the mutual influence between decision makers and the relationship between attributes, the analytic network process (ANP) and entropy method are employed to determine the subjective and objective weights, respectively. Considering the influence of subjective and objective weights on the combination weights, the deviation degree and dispersion degree of the subjective and objective weights are taken as objective functions, and the optimal solution of the combination weights is iteratively solved by genetic algorithm. Then, based on the (p,q)-rung interval-valued orthopair fuzzy set and weight optimization model, an improved (p,q)-rung interval-valued orthopair fuzzy ELECTRE method is proposed. Finally, in order to verify the accuracy and robustness of the algorithm, the algorithm is applied to the example analysis of investment enterprise evaluation, and the results demonstrate that the algorithm has definite theoretical and application value. [ABSTRACT FROM AUTHOR]

Feature selection algorithms based on evolutionary computation have continued to emerge, and most of them have achieved outstanding results. However, there are two drawbacks when facing high-dimensional datasets: firstly, it is difficult to reduce features effectively, and secondly, the "curse of dimensionality". To alleviate those problems, we take the initial population generation as an entry point and propose a variant initial population generator, which can improve diversity and initialize populations randomly throughout the solution space. However, during the experimental process, it was found that the improved diversity would cause the algorithm to converge too fast and thus lead to premature. Therefore, we introduced multi-population techniques to balance diversity and convergence speed, and finally formed the MPF-FS framework. To prove the effectiveness of this framework, two feature selection algorithms, multi-population multi-objective artificial bee colony algorithm and multi-population non-dominated sorting genetic algorithm II, are implemented based on this framework. Nine well-known public datasets were used in this study, and the results reveal that the two proposed multi-population methods on high-dimensional datasets can reduce more features without reducing (or even improving) classification accuracy, which outperforms the corresponding single-population algorithms. Further compared to the state-of-the-art methods, our method still shows promising results. [ABSTRACT FROM AUTHOR]

This research aims to employ and qualify the bio-inspired algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution Algorithm (DE) in the extraction of the parameters of the circuit equivalent to a photovoltaic module in the models of a diode and five parameters (1D5P) and two diodes and seven parameters (2D7P) in order to simulate the I-V characteristics curves for any irradiation and temperature scenarios. The peculiarity of this study stands in the exclusive use of information present in the module's datasheet to carry out the full extraction and simulation process without depending on external sources of data or experimental data. To validate the methods, a comparison was made between the data obtained by the simulations with data from the module manufacturer in different scenarios of irradiation and temperature. The algorithm bound to the model with the highest accuracy was DE 1D5P, with a maximum relative error of 0.4% in conditions close to the reference and 3.61% for scenarios far from the reference. On the other hand, the algorithm that obtained the worst result in extracting parameters was the GA in the 2D7P model, which presented a maximum relative error of 9.59% in conditions far from the reference. [ABSTRACT FROM AUTHOR]

The combination of antenna arrays with optimization algorithms aims to minimize SLL, Linear antenna arrays are an extensively used electromagnetic system in modern wireless communication. The improvement algorithms are the genetic algorithm GA, the flower pollination algorithm FPA, and the grey wolf optimization GWO. This has been implemented to reduce SLL and communicate the signal to the right place and the highest efficiency with the greatest amount of energy and by reaching the best solution. antenna arrays engineering was arranged in linearity and implemented in different numbers of elements, i.e.8,16,32,64,128, and 256 elements, Each algorithm has criteria that affect the reduction of SLL, In GA when considering the influential parameters represented by iteration, population size, and max stall iteration, the best effect is iteration where SLL is reduced to -32.9523dB and at 16-element at iteration 50. FPA has many influential parameters representing iteration, population size, probability, and flower attraction rate. The best of these effects is iteration. SLL reduced to -35.0696dB at iteration 300 and at 64-element. In GWO the influential parameters are iteration and population size the best effect, it was concluded, is iteration as well, which has reduced SLL to -32.8479dB at 8-element in iteration 140. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the recovery sequencing and scheduling of the carrier aircraft fleet landing, the carrier deck environment and the return recovery approach mode of the carrier aircraft are analyzed firstly, and the evaluation index model of the recovery sequencing and scheduling based on the weighted waiting time is established. According to the various constraints in the recovery sequencing and scheduling problem of the carrier aircraft, the model of the recovery sequencing and scheduling problem of the carrier aircraft considering the air refueling condition is established. Then, according to the established recycling sequencing and scheduling model and the idea of huper-heuristic algorithm, a genetic programming with mandatory landing rules (MGP) algorithm is designed to solve the ship necovery scheduling problem. Further, the effectiveness of the proposed recovery sequencing and scheduling model and MGP algorithm is verified by simulation examples, and the advantages of MGP algorithm are verified by comparison with genetic algorithm, pure heuristic algorithm and common genetic programming algorithm. Finally, based on the simulation results of an example, the influence of escape and wave off on the landing fleet recovery scheme is analyzed. [ABSTRACT FROM AUTHOR]

Over the past several years, blockchain technology has been applied in numerous fields. Blockchain (BC) is based on the idea of decentralized applications (DApps) and facilitates their use. This increases the transparency of the apps, distributed, and flexible. Within this context, this paper presents a procedure and algorithm to build a DApp on Ethereum blockchain based platforms. simulation of nature reserve (SONR) is a smart contract code that was built to simulate the work of a nature reserve to take advantages of the characteristics of the genetic algorithm, and then deploy this contract on one of the Ethereum test networks, the Goerli test network, to customize a new block with a unique address for this DApps on that network, the contract creation time is within 12 seconds, and the average transaction time is 8.1 seconds with a 2.5 gwei gas cost. [ABSTRACT FROM AUTHOR]

Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms; genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A Hybrid Quantum Genetic Algorithm with an Adaptive Rotation Angle (HQGAAA) for the 0-1 knapsack problem is presented. This novel proposal uses the Deutsch-Jozsa quantum circuit to generate quantum populations, which synergistically works as haploid recombination and mutation operators taking advantage of quantum entanglement providing exploitative and explorative features to produce new individuals. Furthermore, the created individuals are updated using an adaptive rotation angle operator that helps refine new individuals to converge to the optimal solution. We performed comparative tests with other quantum evolutionary algorithms and the classical genetic algorithm to demonstrate that this proposal performed better with the tested problem. Results showed that quantum algorithms performed similar but better than the classic genetic algorithm regarding accuracy. Moreover, statistic tests demonstrated that our proposal is faster than the other quantum algorithms tested. [ABSTRACT FROM AUTHOR]

Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel-inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space. [ABSTRACT FROM AUTHOR]

The relevance of the study on intelligent methods in multi-agent systems right now is due to the growth of their wide application in the modern world and the need to improve the effectiveness of cooperation between agents to achieve the desired results. The purpose of the present article is to consider intellectual methods, as well as to describe and analyse various ways and methods of their application. Among the methods used, it is possible to distinguish the analytical method, the classification method, the functional method, the statistical method, the synthesis method. The application of intelligent methods in multi-agent systems leads to improved performance and better results through the use of artificial intelligence, machine learning and decision-making. These methods involve modelling a system using agents, which interact and cooperate with their environment. Agents can learn to use different algorithms, such as neural networks and genetic algorithms, allowing them to adapt and improve their behaviour. The study of intelligent methods in multi-agent systems brings new approaches and innovations to science, which help to more effectively model and manage the interaction of agents to achieve optimal results. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications. [ABSTRACT FROM AUTHOR]

This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem. [ABSTRACT FROM AUTHOR]

Different methods have been created to make optimization processes more efficient and effective, which is a big step forward in the area of evolutionary optimization. This abstract talks about four well-known methods: Neuroevolution of Augmenting Topologies (NEAT), Genetic Algorithms (GAs), Genetic Programming (GP), and Advanced Neuroevolutionary Genetic Algorithm (ANGA). It focuses on important performance indicators like Fitness Metrics, Generalization, Efficiency and Speed, and Overall Performance. With scores of 90% in Fitness Metrics and 88% in Generalization, NEAT, a neuroevolutionary program, shows strong success in competitive tasks. With an 80% score, it does poorly in Efficiency and Speed, though. GAs are known for using a population-based method. They do very well in Efficiency and Speed (90%), but they do a little worse in Fitness Metrics and Generalization (89% and 85%, respectively). With a focus on updated computer programs, GP gets marks that are equal in Fitness Metrics, Generalization, and Efficiency and Speed (88%, 85%, and 85%, respectively). The new ANGA algorithm stands out as a top worker, doing exceptionally well in all tests. ANGA gets great marks of 93% in Fitness Metrics, 94% in Generalization, and 93% in Efficiency and Speed. This shows how well it can optimize everything. Overall Performance score of 97.78% shows how well it works as a whole, making ANGA a potential method for genetic optimization. [ABSTRACT FROM AUTHOR]

In view of the problems that current load balancing algorithm affects system efficiency under low load and poor distribution efficiency under high load, based on Nginx server, a dynamic and static load balancing algorithm based on improved genetic algorithm is proposed in this study. The algorithm chooses to use server performance parameters based on CPU performance, memory performance, disk I/O and network bandwidth as server node performance evaluation indexes and static weighted polling algorithm weights under low load, and designs a dynamic load balancing algorithm under high load based on the change of node performance utilization rate as a percentage of the cluster average load utilization rate by introducing operation conversion thresholds and dynamic. By introducing the improved genetic algorithm of operation transition threshold and dynamic triangular function operation probability as the threshold calculation method, the transformation of static algorithm dominant area into dynamic algorithm dominant area is calculated. This study designs comparison experiments to verify that the proposed algorithm has better load balancing effect when compared with weighted polling algorithm, probabilistic meritocracy algorithm and dnfs_conn algorithm in the experimental environment, and has about 15% improvement in the values of average response time and actual concurrent connections when compared with dnfs_conn algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the rescheduling problems of hybrid flow shop with rush order insertion, this paper proposed a hyper-heuristic genetic algorithm based on the two-layer coding. Aiming at the double-decision characteristics of order sort and machine selection in the hybrid flow shop, this algorithm designed a two-layer coding scheme in the low level. Each individual represented both order sort and machine selection information, and corresponded to a unique scheduling solution. Then the low level proposed 12 kinds of sorting and selection heuristics for iterative optimization of individuals. The high level of the algorithm used an adaptive genetic algorithm to determine the operation combination of order sort heuristic and machine selection heuristic and the sort of execution of each combination. And it designed an adaptive mutation operator to optimize the effectiveness of the algorithm. The results of large-scale data experiments show that the proposed algorithm has good solving quality and efficiency. [ABSTRACT FROM AUTHOR]

The optimization of cognitive radio (CR) system using an enhanced firefly algorithm (EFA) is presented in this work. The Firefly algorithm (FA) is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies. It has already proved its competence in various optimization problems, but it suffers from slow convergence issues. To improve the convergence performance of FA, a new variant named EFA is proposed. The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions, and simulation results show its superior performance compared to biogeography-based optimization (BBO), bat algorithm, artificial bee colony, and FA. As an application of this algorithm to real-world problems, EFA is also applied to optimize the CR system. CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem. However, it requires optimization to meet specific performance objectives. The results obtained by EFA in CR system optimization are compared with results in the literature of BBO, simulated annealing, and genetic algorithm. Statistical results further prove that the proposed algorithm is highly efficient and provides superior results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Evolutionary algorithms (EAs) in the repository of the evolutionary computing (EC) paradigm are simplistic and stochastic in nature. The desirable characteristic of adaptive simplicity has sustained research community's rampant curiosity in EAs. Distributed evolutionary algorithmic (DEA) framework is an instinctive extension of EAs. This paper summarizes the design and implementation of a multi-threaded DEA (MTDEA) framework. Empirical analysis between a traditional DEA framework and a simulated MTDEA is presented. The MTDEA framework was outfitted with three plug-in algorithms to handle commonly occurring faults in distributed environments. The propriety of the MTDEA framework was validated in two customary applications - Image thresholding and Image reproduction. The thresholding and reproduction problems were addressed by incorporation of the differential evolution (DE) and genetic algorithm (GA) methods respectively, into the MTDEA framework. The performances of DE and GA routines were compared with their sequential counterpart frameworks. The MTDEA framework was evaluated for reliability and scalability on a high-performance computing (HPC) system. [ABSTRACT FROM AUTHOR]

The unit commitment problem (UCP), which includes the unit schedule and power dispatch, is a nonlinear high-dimensional and highly constrained mixed-integer combinatorial optimization problem. One challenge herein is to obtain high-quality solutions considering various constraints. Developing a competitive hybrid method is a mainstream study goal in this field, which has focused on the unit schedule optimization but less on power dispatch. Inspired by the advantage of genetic algorithms (GAs) in solving combinational optimization problems and the characteristic of grey prediction evolution algorithm (GPE) with strong exploration ability, this paper proposes a novel hybrid GA and GPE method, termed hGAGPE, to solve the UCP. In hGAGPE, GPE, as a novel real parameter stochastic search algorithm based on the grey prediction theory for data mining, is first employed to solve the power dispatch of the UCP. Meanwhile, the unit schedule is performed by the popular GA. Additionally, some heuristic repair mechanisms based on the priority list and an elite selection mechanism are incorporated to enhance the performance of hGAGPE. The proposed hGAGPE is evaluated on six test systems with generating units in the range of 10 to 100 during a 24-h scheduling period. The numerical results demonstrate the feasibility and effectiveness of hGAGPE in comparison with other existing approaches. [ABSTRACT FROM AUTHOR]

Intelligent logistics system in Industry 4.0 involves the emergence of fog computing in handling various IoT sensors attached for automating the logistics system. Instead of transmitting massive data from the sensors to the cloud center, it will be processed by the fog nodes which provide faster response thereby avoiding heavy traffic and delay. While determining the location of fog nodes in building the logistics system, energy consumption and load balancing became major challenges. This work proposes a novel method to determine the location of fog nodes, edge devices and gateways under the minimal energy consumption of the fog nodes and optimal load balancing. In this work, artificial bee colony is combined with genetic algorithm to find the best location for the placement of edge devices, fog nodes and gateways with the objective of minimal deployment cost. Finally, the results show that the proposed method causes 11.9 % reduction in the deployment cost compared to other existing methods, however also ensure minimal energy consumption and optimal load balancing among the fog computing nodes used in this system. [ABSTRACT FROM AUTHOR]

Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms can be applied, being less or more effective in the relevant scenarios. In this work, we develop a novel framework that attempts to compose the best features of link prediction algorithms when applied to a network, in order to have even more reliable predictions, especially in topologies emerging in Industrial Internet of Things (IIoT) environments. According to the proposed framework, we first apply appropriate link prediction algorithms that we have chosen for an analyzed network (basic algorithms). Each basic algorithm gives us a numerical estimate for each missing edge in the network. We store the results of each basic algorithm in appropriate structures. Then we provide them as input to a developed genetic algorithm. The genetic algorithm evaluates the results of the basic algorithms for each missing edge of the network. At each missing edge of the network and from generation to generation, it composes the estimates of the basic algorithms regarding each edge and produces a new optimized estimate. This optimization results in a vector of weights where each weight corresponds to the effectiveness of the prediction for each of the basic algorithms we have employed. With these weights, we build a new enhanced predictor tool, which can obtain new optimized estimates for each missing edge in the network. The enhanced predictor tool applies to each missing edge the basic algorithms, normalizes the basic algorithms' estimates, and, using the weights of the estimates derived from the genetic algorithm, returns a new estimate of whether or not an edge will be added in the future. According to the results of our experiments on several types of networks with five well-known link prediction algorithms, we show that the new enhanced predictor tool yields in every case better predictions than each individual algorithm, therefore providing an accuracy-targeting alternative in the existing state of the art. [ABSTRACT FROM AUTHOR]

Conventional intelligent layout algorithm of ceramic design drawing elements mainly uses density distribution method to obtain ceramic design drawing element units, which is vulnerable to the influence of pattern density, resulting in a low distribution balance of design drawing elements. Therefore, a new intelligent layout algorithm for ceramic design drawing elements needs to be designed based on a genetic algorithm. That is, combining the characteristics of ceramic design drawing elements, the intelligent parting structure of ceramic design drawing elements is constructed, and then the brilliant layout model of ceramic design drawing elements is designed using genetic algorithm. The experimental results show that the developed intelligent layout algorithm of ceramic design elements has a high distribution balance, which proves that the designed intelligent layout algorithm of ceramic design elements has good performance, reliability, and certain application value, and has made specific contributions to improving the aesthetic quality of ceramic design drawings. [ABSTRACT FROM AUTHOR]

The current overall layout planning model matrix of landscape ceramic sculpture is generally unidirectional, and the planning efficiency is low, resulting in a decline in the layout optimization ratio of the model. Therefore, the design and verification analysis of landscape ceramic sculpture's overall layout planning model based on the Nondominated Sorting Genetic Algorithm (NSGA - II) algorithm is proposed. According to the actual planning needs and standards, first set the basic layout points, establish a cross-planning matrix in a multi-level manner, and improve the efficiency of the overall layout planning of the sculpture. The NSGA - II calculation landscape ceramic sculpture layout planning structure is constructed on this basis, and the model design is realized by level conversion. This novel NSGA-II with level conversion performs better layout planning when compared with other conventional models. The final test results show that through three stages of layout optimization processing, compared with the initial planning layout, the optimal layout optimization ratio for the setting of the plaza sculpture can reach more than 60%, indicating that with the help of this method, the layout planning of sculpture has been further improved, the space has been expanded, and has practical application value. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicle (UAV) equipped with visual sensors are extensively used in area coverage applications. As a UAV would only cover a fraction of the region of interest, the entire region needs to be covered by several UAVs where each UAV accomplishes its own tasks. For the covering of the target region, a working method consisting of three levels has been developed. The initial step employs the Voronoi partition technique to create a number of convex sub-polygonal areas inside the target area. In the second level, each sub-polygonal area is partitioned to provide a near-optimal collection of waypoints. At the third and final level, we find a path that visits each of the waypoints without colliding with anything and is as short as feasible. Collision due to both static and dynamic obstacles is also considered for avoidance. The first and second-level partitioning processes are carried out offline, whereas path planning is handled in real-time. Traditional methods like Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), and Cuckoo Optimisation Algorithm (COA) are used to evaluate the proposed work. The evaluated result of the proposed work is compared with the existing work on area coverage in terms of the percentage of inside and outside area coverage. Also, the performance of the proposed dynamic path planning method is compared with TSP and the Improved Follow the Gap Method (FGM-I). The outcome demonstrates that the proposed work is far more effective than the existing result and is suitable for the application in issue, which involves area coverage with the presence of obstacles. [ABSTRACT FROM AUTHOR]

Integration testing is an essential activity in software testing, especially in object-oriented software development. Determining the sequence of classes to be integrated, i.e., the class integration test order (CITO) problem, is of great importance but computationally challenging. Previous research has shown that meta heuristic algorithms can devise class integration test orders with lower test stubbing complexity, resulting in software testing cost reduction. This study focuses on the comparable performance evaluation of ten commonly used meta heuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search algorithm (CS), firefly algorithm (FA), bat algorithm (BA), grey wolf algorithm (GWO), moth flame optimization (MFO), sine cosine algorithm (SCA), salp swarm algorithm (SSA) and Harris hawk optimization (HHO). The objective of this study is to identify the most suited algorithms, narrowing down potential avenues for future researches in the field of search-based class integration test order generation. The standard implementations of these algorithms are employed to generate integration test orders. Additionally, these test orders are evaluated and compared in terms of stubbing complexity, convergence speed, average runtime, and memory consumption. The experimental results suggest that MFO, SSA, GWO and CS are the most suited algorithms. MFO, SSA and GWO exhibit excellent optimization performance in systems where fitness values are heavily impacted by attribute coupling. Meanwhile, MFO, GWO and CS are recommended for systems where the fitness values are strongly influenced by method coupling. BA and FA emerge as the slowest algorithms, while the remaining algorithms exhibit intermediate performance. The performance analysis may be used to select and improve appropriate algorithms for the CITO problem, providing a cornerstone for future scientific research and practical applications. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

Scheduling complex applications as task graphs on finite computational resources assuring task interdependencies is a well-known NP-complete optimization problem. This problem is well-addressed for microprocessor systems but for Dynamically Reconfigurable Hardware (DRHW) systems in which, in addition to tasks, the reconfiguration time and complexity also have to be scheduled; this problem is more complicated. DRHW reconfiguration overhead is considerable and can be crucial for real-world applications. To deal with this overhead, in this paper, a meta-heuristic method named Graph-Oriented Simulated Annealing (GOSA) is proposed. By introducing an innovative graph and solution structures called schedule graphs, and also some controlling functions which are inherited from the nature of the problem, the proposed method adapts itself to the characteristics of the problem. This helps the algorithm to adjust its exploration and exploitation speed and accuracy according to the requirements of the given problem and consequently find high-quality solutions quickly. To demonstrate the performance of the proposed method, it was tested on several synthetic and real-world benchmark task graphs, and the results were compared with a selection of classic and state-of-the-art algorithms. The method is comprehensively evaluated by performing numerous experiments in terms of execution time, makespan, scalability, and reliability. The results of the experiments on benchmarks show that in terms of the quality of the solutions, GOSA outperforms BGA, HPSO-GA, and FATS by 17%, 13%, and 5%, respectively, and its execution time is considerably less than all competing algorithms. Moreover, according to the experiments done on synthetic graphs, the makespan of the solutions generated by GOSA, Genetic Algorithm (GA), and the Gxhaustive Search over the List Scheduler are improved on average by 7.2%, 8.1%, and 19.1%, respectively. The most significant achievement of the proposed method is its execution time which is 31 times faster than GA. Finally, the results confirm that the proposed method is scalable for large task graphs, and its reliability is superior. [ABSTRACT FROM AUTHOR]

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

There are many large-scale sparse multi-objective optimization problems in real life, but it is difficult to solve such problems due to the high dimension of decision variables and the excessive sparseness of Pareto optimal solutions. To address these issues, this paper proposes a quadratic association vector and dynamic guided operator search algorithm for large-scale sparse multi-objective optimization problems. Firstly, according to the individual distribution in the decision space, the reference vector is used to quadratically associate the individual, aiming to find better solutions during the initialization process. Secondly, the dynamic guided operator is introduced into the crossover mutation. The population is guided to evolve toward the sparse optimal solution by flipping the real and binary variables according to the dynamic guided operator. Thirdly, individuals are selected into the mating pool for crossover mutation based on the decision variables of the population individuals, with the aim of further enhancing the convergence of the algorithm. The experimental results on eight benchmark problems show that the algorithm obtains the best comprehensive performance on 66.7% of the test problems. The proposed algorithm is superior to the existing algorithm in terms of effectiveness. [ABSTRACT FROM AUTHOR]

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction. [ABSTRACT FROM AUTHOR]

This paper presents a novel routing planning method based on multi-objective optimization to tackle the routing problem in computing power networks. The proposed method aims to improve the performance and efficiency of routing by considering multiple objectives. In this study, we first model the computing power network and formulate the routing problem as a multi-objective optimization problem. To address this problem, we introduce a non-dominated sorting genetic algorithm incorporating a ratio parameter adjustment strategy based on reinforcement learning. Extensive simulations are conducted to evaluate the performance of the proposed routing algorithm. The results demonstrate significant client latency and cost reductions, highlighting the algorithm's effectiveness. By providing a comprehensive solution to the routing problem in computing power networks, this work contributes to the field by offering improved performance and efficiency. The proposed method's ability to optimize multiple objectives sets it apart from existing approaches, making it a valuable contribution to the research community. [ABSTRACT FROM AUTHOR]

Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H&S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain. [ABSTRACT FROM AUTHOR]

In photolithographic processes, nanometer-level-precision wavefront-aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such way, a near-optimal image is exposed on the wafer surface. Optimizing the parameters of these models, however, involves several time costly sensor measurements which reduce the throughput performance of the machine in terms of exposed wafers per hour. In that case, photolithography machines come across the trade-off between throughput and quality. Therefore, one of the most common optimal experimental design (OED) problems in photolithography machines (and not only) is how to choose the minimum amount of sensor measurements that will provide the maximum amount of information. Additionally, each sensor measurement corresponds to a point on the wafer surface and therefore we must measure uniformly around the wafer surface as well. In order to solve this problem, we propose a sensor mark selection algorithm which exploits genetic algorithms. The proposed solution first selects a pool of points that qualify as candidates to be selected in order to meet the uniformity constraint. Then, the point that provides the maximum amount of information, quantified by the Fisher-based criteria of G-, D-, and A-optimality, is selected and added to the measurement scheme. This process, however, is considered "greedy", and for this reason, genetic algorithms (GA) are exploited to further improve the solution. By repeating in parallel the "greedy" part several times, we obtain an initial population that will be the input to our GA. This meta-heuristic approach outperforms the "greedy" approach significantly. The proposed solution is applied in a real life semiconductors industry use case and achieves interesting industry as well as academical results. [ABSTRACT FROM AUTHOR]

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, in production environments where the production system is parallel machines, the reliability of the machines is important and the uncertainty of scheduling parameters is common. The focus of this paper is on the unrelated parallel machine scheduling problem using a fuzzy approach with machines maintenance activities and process constraints is of concern. An important application of this problem is in the production of products that the due dates are defined as a time window and the best due date is close to the middle of the time window and the jobs processing times depend on other factors such as operator and their value is not specified and are announced as interval under uncertainty. To begin the study, a fuzzy mathematical model is introduced in which changing between a fuzzy approach and a deterministic model is described. Then, Due to the NP-hard nature of the problem, a fuzzy-based genetic algorithm has been developed to address large instances. In this algorithm, a greedy decoding approach according to fuzzy parameters is developed. Numerical experiments are used assess the efficacy of the developed algorithm. It is concluded that the proposed algorithm shows great performance in large instances and is superior to the proposed mathematical model in small instances too. [ABSTRACT FROM AUTHOR]

Featured Application: When multiple AUVs are combined in formation, they can improve operational efficiency while maintaining mutual communication. This article focuses on the obstacle avoidance problems en-countered by AUV formations during collaborative search and target capture, proposes an energy cost optimal formation obstacle avoidance strategy and an improved SOM path planning algorithm to achieve target capture by occupying adjacent positions of target in the underwater obstacle environment. When performing cooperative search operations underwater, multi-autonomous underwater vehicles formations may encounter array-type obstacles such as gullies and bumps. To safely traverse the obstacle domain, this paper balances convergence time, transformation distance and sensor network power consumption, and proposes a Formation Comprehensive Cost (FCC) model to achieve collision avoidance of the formations. The FCC model is used instead of the fitness function of the genetic algorithm to solve the assignment of capture positions and the improved neural self-organizing map (INSOM) algorithm is proposed to achieve efficient path-planning during the capture process. The simulation experiments in 3D space verify that the proposed scheme can improve the efficiency of robot deployment while ensuring safety. [ABSTRACT FROM AUTHOR]

This study addresses the importance of focal nodes in understanding the structural composition of networks. To identify these crucial nodes, a novel technique based on parallel Fuzzy Cognitive Maps (FCMs) is proposed. By utilising the focal nodes produced by the parallel FCMs, the algorithm efficiently creates initial clusters within the population. The community discovery process is accelerated through a distributed genetic algorithm that leverages the focal nodes obtained from the parallel FCM. This approach mitigates the randomness of the algorithm, addressing the limitations of the random population selection commonly found in genetic algorithms. The proposed algorithm improves the performance of the genetic algorithm by enabling informed decision making and forming a better initial population. This enhancement leads to improved convergence and overall algorithm performance. Furthermore, as graph sizes grow, traditional algorithms struggle to handle the increased complexity. To address this challenge, distributed algorithms are necessary for effectively managing larger data sizes and complexity. The proposed method is evaluated on diverse benchmark networks, encompassing both weighted and unweighted networks. The results demonstrate the superior scalability and performance of the proposed approach compared to the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

One of the most widely used human behavior detection methods is anomaly detection, which this article covers. Ensuring a person's safety is a crucial task in every community today due to the ever-increasing actions that can be dangerous, from planned crime to harm from an accident. Classic closedcircuit television is insufficient since a person must always be awake and available to monitor the cameras, which is costly. Also, someone's attention tends to decrease after a certain period of time. Due to these reasons, a surveillance system that is automated and able to detect unusual activities in real-time and give sufferers prompt aid is necessary. It should be noted that the identification process must be completed swiftly and correctly. In this paper, we employ a model based on mixes the machine learning (ML) model, namely genetic algorithms with deep learning (DL). In this study's experimentation, the UCF-Crime dataset was employed. The detection accuracy on the testing sample dataset was equal to 89.90%, while the area under the curve (AUC) was equal to 94.58%. The developed models have demonstrated reliability and the ability to achieve the greatest accuracy when compared to models that have already been designed. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of directional overcurrent relays (DOCRs) plays an important role in protecting power systems and ensuring their safe, reliable, and efficient operation. However, coordinating DOCRs involves solving a highly constrained and nonlinear optimization problem. The primary objective of optimization is to minimize the total operating time of DOCRs by determining the optimal values for decision variables such as the time multiplier setting (TMS) and plug setting (PS). This article presents an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to achieve improved solutions. First, this study modifies the firefly algorithm to obtain a global solution by updating the firefly's brightness and to prevent the distance between the individual fireflies from being too far. Additionally, the randomized movements are controlled to produce a high convergence rate. Second, the optimization problem is solved using the genetic algorithm. Finally, the solution obtained from the modified firefly algorithm is used as the initial population for the genetic algorithm. The proposed algorithms have been tested on the IEEE 3-bus, 8-bus, 9-bus and 15-bus networks. The results indicate the effectiveness and superiority of the proposed algorithms in minimizing the total operating time of DOCRs compared with other optimization methods presented in the literature. [ABSTRACT FROM AUTHOR]

The presence of sinkholes has been widely studied due to their potential risk to infrastructure and to the lives of inhabitants and rescuers in urban disaster areas, which is generally addressed in geotechnics and geophysics. In recent years, robotics has gained importance for the inspection and assessment of areas of potential risk for sinkhole formation, as well as for environmental exploration and post-disaster assistance. From the mobile robotics approach, this paper proposes RUDE-AL (Roped UGV DEployment ALgorithm), a methodology for deploying a Mobile Cable-Driven Parallel Robot (MCDPR) composed of four mobile robots and a cable-driven parallel robot (CDPR) for sinkhole exploration tasks and assistance to potential trapped victims. The deployment of the fleet is organized with node-edge formation during the mission's first stage, positioning itself around the area of interest and acting as anchors for the subsequent release of the cable robot. One of the relevant issues considered in this work is the selection of target points for mobile robots (anchors) considering the constraints of a roped fleet, avoiding the collision of the cables with positive obstacles through a fitting function that maximizes the area covered of the zone to explore and minimizes the cost of the route distance performed by the fleet using genetic algorithms, generating feasible target routes for each mobile robot with a configurable balance between the parameters of the fitness function. The main results show a robust method whose adjustment function is affected by the number of positive obstacles near the area of interest and the shape characteristics of the sinkhole. [ABSTRACT FROM AUTHOR]

Effective project planning is crucial for the successful execution of complex industrial projects, as it can significantly reduce costs and enhance overall efficiency. However, applying precise methods for obtaining optimal project plans is often limited to small activity graphs, rendering them impractical for real-world scenarios. To address this issue, heuristic and metaheuristic algorithms have been the focus of extensive research over the past few decades. This study explores an extended problem formulation known as the Multiple Contractor-Resource Project Scheduling Problem (MC-MRCPSP). This formulation extends beyond the basic problem structure, incorporating various constraints typically encountered in industrial project scheduling. To tackle this challenging optimization problem, we propose a novel hybrid algorithm that combines a fine-tuned genetic algorithm with an advanced tabu search. This approach strikes an optimal balance between exploration and exploitation of the solution space, leading to enhanced project plan optimization. Through comprehensive experiments and evaluations, we demonstrate the effectiveness of our proposed approach. Compared to a purely genetic algorithm, our hybrid solution achieves a 38% improvement in project plan optimization. These results underscore our study's significant contribution to project planning and lay the groundwork for future advancements in MC-MRCPSP solutions. Ultimately, our research brings us closer to improved resource utilization, cost reduction, and enhanced efficiency in managing complex industrial projects. [ABSTRACT FROM AUTHOR]