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Accusations have been made against high-profile papers documenting honeybee navigation, specifically regarding the existence of an internal "odometer" that relies on visual cues. Two scientists have raised concerns about possible miscalculations, image reuse, and data manipulation in 10 key papers. The author of these papers, Mandyam Veerambudi "Srini" Srinivasan, denies the allegations and states that his conclusions remain firm and have been independently replicated. While some researchers support Srinivasan, others believe that the claims need to be revisited. The journal Science is evaluating the concerns, and further investigation may be necessary. [Extracted from the article]

Cyber Defense is becoming a major issue for every organization to keep business continuity intact. The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm (ABC) as an Nature Inspired Cyber Security mechanism to achieve adaptive defense. It experiments on the Denial- Of-Service attack scenarios which involves limiting the traffic flow for each node. Businesses today have adapted their service distribution models to include the use of the Internet, allowing them to effectively manage and interact with their customer data. This shift has created an increased reliance on online services to store vast amounts of confidential customer data, meaning any disruption or outage of these services could be disastrous for the business, leaving them without the knowledge to serve their customers. Adversaries can exploit such an event to gain unauthorized access to the confidential data of the customers. The proposed algorithm utilizes an Adaptive Defense approach to continuously select nodes that could present characteristics of a probable malicious entity. For any changes in network parameters, the cluster of nodes is selected in the prepared solution set as a probable malicious node and the traffic rate with the ratio of packet delivery is managed with respect to the properties of normal nodes to deliver a disaster recovery plan for potential businesses. [ABSTRACT FROM AUTHOR]

The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability. OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm. METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments. .ASA-RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms. CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods. [ABSTRACT FROM AUTHOR]

Distribution generation (DG) technology based on a variety of renewable energy technologies has developed rapidly. A large number of multi-type DG are connected to the distribution network (DN), resulting in a decline in the stability of DN operation. It is urgent to find a method that can effectively connect multi-energy DG to DN. photovoltaic (PV), wind power generation (WPG), fuel cell (FC), and micro gas turbine (MGT) are considered in this paper. A multi-objective optimization model was established based on the life cycle cost (LCC) of DG, voltage quality, voltage fluctuation, system network loss, power deviation of the tie-line, DG pollution emission index, and meteorological index weight of DN. Multi-objective artificial bee colony algorithm (MOABC) was used to determine the optimal location and capacity of the four kinds of DG access DN, and compared with the other three heuristic algorithms. Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node, the total voltage deviation, voltage fluctuation, and system network loss of DN decreased by 49.67%, 7.47% and 48.12%, respectively, compared with that without DG configuration. In the IEEE 69 test node, the total voltage deviation, voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%, 35.93% and 75.17%, respectively, compared with that without DG configuration, indicating that MOABC can reasonably plan the capacity and location of DG. Achieve the maximum trade-off between DG economy and DN operation stability. [ABSTRACT FROM AUTHOR]

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information. [ABSTRACT FROM AUTHOR]

Mobile object detection and tracking is an important application of wireless sensor network. However, longevity of such application is a challenge as sensor nodes in wireless sensor network operates on battery power. Therefore, there is the requirement for increasing the lifetime of this application by optimal usage of energy while tracking. Of course, while optimizing the energy usage, tracking accuracy should not be compromised. A number of research works are carried out in the past to enhance the wireless sensor network lifetime but no work resolved the problem completely. This paper studies the issue of data routing to sink in energy constrained wireless sensor network based application where tracking information collected from different sensor nodes are processed to determine the object's presence in the monitoring area. This paper proposes an energy efficient hybrid artificial bee colony-modified Dijkstra (HABC-MD) algorithm for cluster based network for optimum usage of energy during routing of packet. The objective of this algorithm is to save the energy of the network by optimizing the number of node-to-node transmission while tracking. It uses metaheuristic approach based artificial bee colony algorithm for selection of cluster head in each round and heuristic approach-based Dijkstra algorithm for selection of optimum route between source node and sink for packet transmission. Modified Dijkstra algorithm considers both distance between source and destination and energy of the source node for determination of optimum route to sink. The performance of HABC-MD is compared with existing hybrid LEACH-Dijkstra algorithm and is found that performance of our algorithm is better in comparison to later algorithm. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively. Design/methodology/approach: Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used. Findings: Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance. Originality/value: In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function. [ABSTRACT FROM AUTHOR]

One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population's diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search. [ABSTRACT FROM AUTHOR]

This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques. [ABSTRACT FROM AUTHOR]

The Artificial Bee Colony algorithm (ABC) is a swarm intelligence algorithm inspired by honey bee harvesting behavior. It boasts the benefits of minimal parameters and strong exploration capabilities. However, the ABC algorithm is still susceptible to local optima entrapment and lacks consideration of selection probability in the onlooker bee phase, leading to reduced convergence accuracy in later search stages. To address these issues, this paper introduces an enhanced ABC algorithm called Hierarchical Learning-based Artificial Bee Colony (HLABC). Initially, a hierarchical learning approach is devised, dividing the entire population into distinct layers based on solution quality. In this hierarchical approach, bees at lower layers can access much better advantageous information from higher layers. Secondly, the exploitation ability of onlooker bees is enhanced through novel strategies designed based on hierarchical learning. Thirdly, the exploration ability of scout bees is strengthened by implementing an opposition-based learning method. To evaluate the performance of the proposed algorithm, 69 benchmark functions from four benchmark suites (CEC2005, CEC2010, CEC2013 and CEC2022) are used to test the performance of HLABC, along with five variants of the ABC algorithm, The experimental statistical results show that the HLABC algorithm outperforms the ABC algorithm on all test problems with an average winning rate of 89%. Furthermore, to validate the performance of the HLABC algorithm in real-world optimization problems, this paper applies the HLABC algorithm to two practical applications: the deployment of wireless sensor networks (WSNs), the power scheduling problem in a smart home (PSPSH) and the multi-thresholding image segmentation (MIS). The experimental and statistical results demonstrate that HLABC is an efficient and stable optimizer. It shows better or comparable performance compared to other ABC variants when considering the quality of solutions for a suite of benchmark problems and real-world optimization problems. These findings affirm the effectiveness and versatility of the HLABC algorithm in addressing both theoretical and practical optimization challenges. [ABSTRACT FROM AUTHOR]

Artificial physical optimizer (APO), as a new heuristic stochastic algorithm, is difficult to balance convergence and diversity when dealing with complex multi-objective problems. This paper introduces the advantages of R2 indicator and target space decomposition strategy, and constructs the candidate solution of external archive pruning technology selection based on APO algorithm. A hybrid strategy guided multi-objective artificial physical optimizer algorithm (HSGMOAPO) is proposed. Firstly, R2 indicator is used to select the candidate solutions that have great influence on the convergence of the whole algorithm. Secondly, the target space decomposition strategy is used to select the remaining solutions to improve the diversity of the algorithm. Finally, the restriction processing method is used to improve the ability to avoid local optimization. In order to verify the comprehensive ability of HSGMOAPO algorithm in solving multi-objective problems, five comparison algorithms were evaluated experimentally on standard test problems and practical problems. The results show that HSGMOAPO algorithm has good convergence and diversity in solving multi-objective problems, and has the potential to solve practical problems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

Software cost estimation (SCE), estimating the cost and time required for software development, plays a highly significant role in managing software projects. A somewhat accurate SCE is necessary for a software project to be successful. It allows effective control of construction time and cost. In the past few decades, various models have been presented to evaluate software projects, including mathematical models and machine learning algorithms. In this paper, a new model based on the hybrid of the artificial fish swarm algorithm (AFSA) and the artificial bee colony (ABC) algorithm is presented for SCE. The initial population of AFSA, which includes the values of the effort factors, is generated using the ABC algorithm. ABC algorithm is used to solve the problems of the AFSA algorithm such as population diversity and getting stuck in a local optimum. ABC algorithm achieves the best solutions using observer and scout bees. The evaluation of the combined method has been implemented on eight different data sets and evaluated based on eight different criteria such as mean magnitude of relative error and PRED (0.25). The proposed method is more error-free than current SCE methods, according to the results. The error value of the proposed method is lower on NASA60, NASA63, and NASA93 datasets. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarm intelligence technology has shown unique advantages in agricultural and forestry disaster detection, early warning, and prevention with its efficient and precise cooperative operation capability. In this paper, a systematic application strategy of UAV swarms in forest fire detection is proposed, including fire point detection, fire assessment, and control measures, based on the fusion of particle swarm optimization (PSO) and the artificial bee colony (ABC) algorithm. The UAV swarm application strategy provides optimized paths to quickly locate multiple mountain forest fire points in 3D forest modeling environments and control measures based on the analysis of the fire situation. This work lays a research foundation for studying the precise application of UAV swarm technology in real-world forest fire detection and prevention. [ABSTRACT FROM AUTHOR]

Over the last decade, dual active bridge (DAB) converters have become critical components in high-frequency power conversion systems. Recently, intensive efforts have been directed at optimizing DAB converter design and control. In particular, several strategies have been proposed to improve the performance of DAB control systems. For example, fractional-order (FO) control methods have proven potential in several applications since they offer improved controllability, flexibility, and robustness. However, the FO controller design process is critical for industrializing their use. Conventional FO control design methods use frequency domain-based design schemes, which result in complex and impractical designs. In addition, several nonlinear equations need to be solved to determine the optimum parameters. Currently, metaheuristic algorithms are used to design FO controllers due to their effectiveness in improving system performance and their ability to simultaneously tune possible design parameters. Moreover, metaheuristic algorithms do not require precise and detailed knowledge of the controlled system model. In this paper, a hybrid algorithm based on the chaotic artificial ecosystem-based optimization (AEO) and manta-ray foraging optimization (MRFO) algorithms is proposed with the aim of combining the best features of each. Unlike the conventional MRFO method, the newly proposed hybrid AEO-CMRFO algorithm enables the use of chaotic maps and weighting factors. Moreover, the AEO and CMRFO hybridization process enables better convergence performance and the avoidance of local optima. Therefore, superior FO controller performance was achieved compared to traditional control design methods and other studied metaheuristic algorithms. An exhaustive study is provided, and the proposed control method was compared with traditional control methods to verify its advantages and superiority. [ABSTRACT FROM AUTHOR]

Avionics systems are a crucial part of aircraft, and the heterogeneity of resources also leads to load differentials within these systems. Inefficient load balancing technology faces the dual challenge of over-utilization and under-utilization of resources, which results in the decline of service performance (in the case of over-utilization) or the waste of resources (in the case of under-utilization). However, it is necessary to control avionics systems via an efficient load balancing method under time constraints and resource states. Therefore, this paper proposes a load balancing method for avionics systems using both artificial bee colony and simulated annealing algorithms. First, the load balancing model of avionics systems is established; this model can reflect the demands of the tasks for the resources in detail. Then, the load balancing of avionics systems is realized by artificial bee colony and simulated annealing algorithms, the hybrid algorithm not only retains the advantages of simple and easy implementation of ABC algorithm, but also utilizes the probability jump of SA algorithm to jump out of the local extreme and achieve the effect of global optimization. Finally, compared with the existing algorithms, the experimental results show that the algorithm proposed in this paper produces a good and stable load balance performance. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes a novel dual four-rod horizontal large-amplitude quasi-zero stiffness (QZS) vibration isolator based on the singular configuration of a planar four-rod mechanism combined with gravity compensation. Methods: First, the mechanism design and three-dimensional model of dual four-rod horizontal vibration isolator are established. Second, kinematic characteristics are analyzed, and trajectory planning is carried out to obtain the kinematic performance. Then, based on the artificial fish swarm algorithm, the appropriate size and mass of the component are solved, and the QZS characteristics of the isolator are optimized. Third, the statics and dynamics theoretical models of isolators are established, and the transmissibility of isolators under different damping ratios and excitation amplitudes is solved by simulation experiments. Finally, the experimental prototype is established, and the experiment of resilience and acceleration transmissibility is carried out to verify the effectiveness of low-frequency vibration isolation. Results: The static and dynamic simulation analysis of isolator well verifies the theoretical solution of isolator. According to the theoretical model, different vibration isolation performance can be obtained with different input parameters. The experimental results of the prototype show that the isolator has lower initial vibration isolation frequency and wider vibration isolation bandwidth. Conclusion: The dual four-rod horizontal large-amplitude QZS isolator designed in this paper shows good performance in isolating the external excitation of the low-frequency large vibration amplitude. Since the medical precision instrument will inevitably produce low-frequency vibration during the transportation process and reduce the accuracy and stability of the equipment, in order to reduce the possibility of the instrument being damaged during the transportation process, the vibration isolator has great application potential in the isolation of medical precision instrument. [ABSTRACT FROM AUTHOR]

Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process. [ABSTRACT FROM AUTHOR]

In the context of multi-vehicle formation, obstacle avoidance in unknown environments presents a number of challenges, including obstacles near the target, susceptibility to local minima, and dynamic obstacle avoidance. To address these issues in multi-vehicle formation control and obstacle avoidance within unknown environments, this paper uses PID control to optimize the potential field function of the artificial potential field method and conducts simulation experiments. The results demonstrate that the proposed algorithm achieves reductions of 39.7%, 41.9%, 24.8% and 32.0% in four efficiency functions (total iteration times, formation efficiency function value, energy consumption and standard deviation of iteration times) compared to other algorithms. The improved algorithm more effectively addresses the challenge of slow obstacle avoidance when vehicles approach the target and can handle unexpected situations such as local minima and dynamic obstacles. It achieves energy-efficient optimization for multi-vehicle obstacle avoidance in complex environments. [ABSTRACT FROM AUTHOR]

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection. [ABSTRACT FROM AUTHOR]

Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm's challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm's issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios. [ABSTRACT FROM AUTHOR]

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman's rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration. [ABSTRACT FROM AUTHOR]

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems. [ABSTRACT FROM AUTHOR]

The paper proposes a calculation procedure for square cascades based on cross-sections of partial fluxes. The bees algorithm is used to achieve a constant cross-section for the stage feed flux. The optimization goal is determined as a residual sum of squares of the given stage feed flows calculated through cross-sections of partial fluxes. The paper describes the numerical experiment with germanium tetrafluoride used for multicomponent gas separation of germanium isotopes. It is shown that the proposed calculation procedure can be used to obtain the high concentration of 70Ge and 76Ge isotopes in the square cascade of the stage feed flux. [ABSTRACT FROM AUTHOR]

In urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two‐objective train operation optimization based on eco‐driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware‐in‐the‐loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow. [ABSTRACT FROM AUTHOR]

Recently, the Distributed Generation (DG) structure has become an important aspect of daily planning and has achieved a variety of functions while maximizing the functionality of utilities. It should balance the energy equilibrium of the system components and the production limit. Uncertainties arise during the operation of the DG, represented by generator power, load requirements and fluctuations in electricity prices. The reactive power problem is the major concern associated with the DG system. This paper analyzes both DG and an energy storage system along with their optimal performance using a specific algorithm. For optimal analysis of DG and ESS, an Improved Artificial Bee Colony Algorithm (IABC) is proposed. The IABC evaluates the performance of distribution network. It takes DG and ESS into account for analyzing optimal design problems, the main goal of which is to minimize their reactive power dispersion functions. In addition, there are barriers to reducing the DG's operating costs. The proposed method is implemented on the MATLAB platform and tested using the IEEE 33-bus system. To confirm the effectiveness of the proposed method, it will be compared with the existing methods such as artificial bee colony (ABC) and levy-based method, respectively. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

To meet the production demand of workshop, this paper proposes an efficient discrete artificial bee colony (DABC) algorithm to solve a new automatic guided vehicle (AGV) scheduling problem with delivery and pickup in a matrix manufacturing workshop. The goal is to produce a AGV transportation solution that minimizes the total cost, including travel cost, time cost, and AGV cost. Therefore, a mixed integer linear programming model is established. To improve the transportation efficiency, a dynamic calculation method is developed. In the DABC algorithm, a heuristic algorithm and a median based probability selection method are used. For improving the quality of the solutions, four effective neighborhood operators are introduced. In the local search, a rule is given to save the operation time and a problem-based search operator is proposed to improve the quality of the best individual. Finally, a series of comparison experiments were implemented with the iterative greedy algorithm, artificial bee colony algorithm, hybrid fruit fly optimization algorithm, discrete artificial bee colony algorithm, improved harmony search, and hybrid genetic-sweep algorithm. The results show that the proposed DABC algorithm has high performance on solving the delivery and pickup problem. [ABSTRACT FROM AUTHOR]

In rough set theory, a construct is an attribute subset with the same ability to discern objects belonging to different classes as the whole set of attributes, while maintaining the similarity between objects belonging to the same class. Although algorithms for reducts computation can be adapted to compute constructs, practical problems exist where these algorithms cannot compute all constructs within a reasonable time frame. Therefore, this paper introduces an algorithm for computing all constructs of a decision system. The results of experiments with various decision systems (both artificial and real-world) suggest that our algorithm is, in most cases, faster than the state-of-the-art algorithms when the simplified binary discernibility–similarity matrix has a density of less than 0.29. [ABSTRACT FROM AUTHOR]

Proposed is a Satellite network cache placement strategy (PNCCP) based on popularity and node cooperation to address the issue of significant delays in end-to-end connectivity due to instability among satellites. Initially, the strategy employs spectral clustering algorithm to partition the satellite network's topology, limiting the retrieval scope of content and reducing unnecessary propagation delays. Within each partition, a cache collaboration open mechanism among satellites is devised to share cache resources, utilizing the proximity of neighboring nodes to share popular content and cache space. Furthermore, the data naming network (NDN) cache model is enhanced and integrated with the open mechanism, with an update mechanism designed to address the invalidation caused by the dynamic nature of satellite networks. Finally, aiming to minimize users' average retrieval delay, the artificial bee colony algorithm is employed to solve the optimal cache placement problem. Simulation results demonstrate that compared to three contrasting cache strategies, the proposed strategy reduces user content retrieval delays, improves cache hit rates, and holds an advantage in reducing request hop counts. [ABSTRACT FROM AUTHOR]

In 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%. [ABSTRACT FROM AUTHOR]

Peak load forecasting plays an important role in electric utilities. However, the daily peak load forecasting problem, especially for holidays, is fuzzy and highly nonlinear. In order to address the nonlinearity and fuzziness of the holiday load behaviors, a grammatical evolution-based fuzzy regression approach is proposed in this paper. The proposed hybrid approach is based on the theorem that fuzzy polynomial regression can model all fuzzy functions. It employs the rules of the grammatical evolution to generate fuzzy nonlinear structures in polynomial form. Then, a two-stage fuzzy regression approach is used to determine the coefficients and calculate the fitness of the fuzzy functions. An artificial bee colony algorithm is used as the evolution system to update the elements of the grammatical evolution system. The process is repeated until a fuzzy model that best fits the load data is found. After that, the developed fuzzy nonlinear model is applied to forecast holiday peak load. Considering that different holidays possess different load patterns, a separate forecaster model is built for each holiday. Test results on real load data show that an averaged absolute percent error less than 2% can be achieved, which significantly outperforms existing methods involved in the comparison. [ABSTRACT FROM AUTHOR]

The optimal foraging algorithm (OFA) is a swarm-based algorithm motivated by animal behavioral ecology theory. When solving complex optimization problems characterized by multiple peaks, OFA is easy to get trapped in local minima and encounters slow convergence. Therefore, this paper presents an improved optimal foraging algorithm with social behavior based on quasi-opposition (QOS-OFA) to address these problems. First, quasi-opposition-based learning (QOBL) is introduced to improve the overall quality of the population in the initialization phase. Second, an efficient cosine-based scale factor is designed to accelerate the exploration of the search space. Third, a new search strategy with social behavior is designed to enhance local exploitation. The cosine-based scale factor is used as a regulator to achieve a balance between global exploration and local exploitation. The proposed QOS-OFA is compared with seven meta-heuristic algorithms on a CEC benchmark test suite and three real-world optimization problems. The experimental results show that QOS-OFA is better than other competitors on most of the test problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime mold optimization (EMSMO) for addressing continuous optimization problems. The architecture of a typical hyper-heuristic algorithm comprises two main components: the high-level component and the low-level component. The low-level component contains a set of low-level heuristics (LLHs) and intrinsic problem attributes, while the high-level component manipulates the LLHs to construct the sequence of heuristics. Inspired by the foraging behaviors of slime mold, we designed four easy-implemented search strategies including the search for food, approach food, wrap food, and re-initialization as the LLHs for the low-level component. In the high-level component, we adopt an improvement-based probabilistic selection function that contains two metrics: (1) the probability of improvement and (2) the normalized improvement. The selection function cooperates with the roulette wheel strategy to construct the optimization sequence. To evaluate the performance of our proposal, we implement comprehensive numerical experiments on CEC2013 benchmark functions and three engineering optimization problems. Six classic or advanced evolutionary algorithms and three hyper-heuristic algorithms are applied as competitor algorithms to evaluate the competitiveness of EMSMO. Experimental and statistical results show that EMSMO has broad prospects for solving continuous optimization problems. [ABSTRACT FROM AUTHOR]

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This paper promotes a new metaheuristic called the half mirror algorithm (HMA). As its name suggests, HMA offers a new kind of mirroring search. HMA is developed by hybridizing swarm intelligence and the evolution system. Swarm intelligence is adopted by constructing several autonomous agents called swarms. On the other hand, the evolution system is adopted using arithmetic crossover based on a particular reference called a mirror. Four mirrors are used in HMA: the best swarm member, a randomly selected swarm member, the central point of the space, and the corresponding swarm member. During the confrontative assessment, HMA is confronted with average and subtraction-based optimization (ASBO), total interaction algorithm (TIA), walrus optimization algorithm (WaOA), coati optimization algorithm (COA), and clouded leopard optimization (CLO). The result shows that HMA is superior to ASBO, TIA, WaOA, COA, and CLO in 20, 19, 19, 20, and 20 out of 23 functions, respectively. Moreover, HMA has found the global optimal of eight functions. It means the superiority of HMA occurs in almost entire functions. In the future, the mirroring search can be combined with the guided and neighborhood search to construct a more powerful metaheuristic. [ABSTRACT FROM AUTHOR]

In the article, we propose a new method to generate representative volume element (RVE) of random spatial distributions with cylindrical fibers and spherical particles based on the Improved Artificial Bee Colony (IABC) algorithm. In this employed bee phase of IABC, the proposed algorithm adopts a new search strategy, which improves the computational efficiency. To analyze the differences between the fiber distribution generated by this algorithm and the actual fiber distribution, we conducted statistical analysis on the distribution of the generated RVE. The statistical analysis results of the nearest neighbor distance (NND), Second-order intensity function, and pair correlation function were in good agreement with the experiment and complete spatial randomness (CSR) patterns. In addition, the predicted elastic properties were compared with experimental measurements and theoretical prediction methods. The results demonstrate the accuracy and efficiency of the proposed algorithm. This algorithm can quickly generate the RVE of cylindrical fibers and spherical particles with volume fractions of 80% and 62%, and the calculation time for generating the RVE of cylindrical fibers is about 17s. [ABSTRACT FROM AUTHOR]

Space debris poses a potentially catastrophic risk to large-scale LEO constellations due to its substantial cascading effects, thereby rendering efficient and timely debris removal a critical research area in contemporary aerospace engineering. This paper delves into the application of the three-dimensional traveling salesman problem with orbital transfer constraints for space debris removal task planning. To overcome the challenges of sparse optimal solutions and vulnerability to local optima in addressing large-scale space debris removal task planning problems, we introduce a new Hierarchical Exploration Artificial Bee Colony (HEABC) optimization algorithm. Initially, we present an innovative two-step search strategy that employs a short-and-long term hybrid approach to optimize search time utilization and enhance solution quality, thereby addressing the sparsity of optimal solutions in the HEABC algorithm. Subsequently, to mitigate the issue of converging to local optima in high-dimensional encoding-based search problems, we devise a dual population update strategy aimed at preserving the innate evolutionary driving force of the search population. This strategy ensures the continuous updating of the population, even in the absence of intrinsic driving forces. Ultimately, experimental results substantiate that our proposed HEABC algorithm attains superior task planning sequences in a reduced time span and exhibits heightened adaptability in comparison to various traditional search algorithms. This is corroborated by numerical experiments conducted on one publicly available and one STK-generated space debris datasets. [ABSTRACT FROM AUTHOR]

In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user's real topics and corresponding topics can be grasped by capturing the candidate users' real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet technology along with the emergence of intelligent applications has put forward higher requirements for task offloading. In Cloud-Edge-End (CEE) environments, offloading computing tasks of terminal devices to edge and cloud servers can effectively reduce system delay and alleviate network congestion. Designing a reliable task offloading strategy in CEE environments to meet users' requirements is a challenging issue. To design an effective offloading strategy, a Service Reliability Analysis and Elite-Artificial Bee Colony Offloading model (SRA-E-ABCO) is presented for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. An Elite Artificial Bee Colony Offloading (E-ABCO) method is also proposed, which optimizes the offloading strategy by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Simulation results on real datasets validate the efficient performance of the proposed scheme that not only reduces task offloading delay but also optimize system overhead in comparison to baseline schemes. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of optimizing energy efficiency in production shop scheduling problem, focusing specifically on the constraint of sequence-dependent setup times (FSSP SDST). This study is conducted in the context of promoting green industrial practices. The main objective is to minimize total energy consumption (TEC) in the manufacturing process. We introduce a mathematical model of this problem. To solve this problem, we use the Artificial Bee Colony algorithm (ABC). In addition, the Migrating Birds Optimization (MBO) algorithm will be presented. The effectiveness of the proposed algorithms will be subjected to rigorous evaluation through detailed tests on different instances. This methodical approach will enable us to carry out an in-depth and detailed comparative analysis, highlighting the relative strengths and weaknesses of the algorithms presented in this study. [ABSTRACT FROM AUTHOR]

This paper presents an improved metaheuristic technique inspired by the foundational concepts of the artificial bee colony (ABC) algorithm adapted to deal with multi-objective optimization challenges. Our approach combines the main ideas of ABC with a non-dominated sorting strategy including aspects of Pareto dominance, crowding distance, and greedy selection method. Furthermore, the chosen non-dominated solutions are archived in a repository with a static size. The presented approach, multiobjective artificial bee colony (MOABC), is compared to other state-of-theart algorithms including the non-dominated sorting genetic algorithm II (NSGA II) and the multi-objective particle swarm optimization (MOPSO). MOABC and selected algorithms from the literature are applied to five zitzler-deb-thiele (ZDT) Multi-objective benchmark functions. Then three key metrics are employed for performance evaluations: generational distance (GD), spread (SP), and hypervolume (HV). The simulation results suggest that the proposed method is competitive and presents an effective choice for tackling multi-objective optimization problems. [ABSTRACT FROM AUTHOR]