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The duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA‐BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA‐BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA‐BP neural network model, this research utilized an artificial bee colony (ABC)‐improved K‐means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC‐K‐means‐GA‐BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC‐K‐means‐GA‐BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency. [ABSTRACT FROM AUTHOR]

In the typical vibration signal of a reciprocating air compressor, multi-source nonlinear characteristics are exhibited and are often drowned out in background noise, which leads to a lack of robustness in traditional feature analysis methods and difficulty in effective extraction. To address this issue, an algorithm based on ABC-SGMD is proposed in this paper. The Symplectic Geometry Mode Decomposition (SGMD), which is optimized with the Artificial Bee Colony algorithm (ABC), is utilized to decompose the signal, and a multi-feature fusion model is constructed for fault feature extraction. The extracted features are then input into the Self-Adaptive Evolutionary Extreme Learning Machine (SaDE-ELM), and a fault diagnosis model based on ABC-SGMD and SaDE-ELM is established. Ultimately, the signals of reciprocating air compressors and experimental data are used to demonstrate the applicability of the method. The results manifest that this framework has superiority in handling nonlinear and non-stationary signals. [ABSTRACT FROM AUTHOR]

With the widespread application of smart contracts, there is a growing concern over the quality assurance of smart contracts. The data flow testing is an important technology to ensure the correctness of smart contracts. We propose an approach named IABC‐TCG (Improved Artificial Bee Colony‐Test Case Generation) to generate test cases for the data flow testing of smart contracts. With a dominance relations‐based fitness function, an improved artificial bee colony algorithm is used to generate test cases, in which the bee colony search coefficient is adaptively adjusted to improve the effectiveness and efficiency of the search. In addition, an improved test case selection and updation strategy is used to avoid unnecessary test cases. The experimental results show that IABC‐TCG achieves 100% coverage for all the test requirements on a dataset of 30 smart contracts and outperforms the baseline approaches in terms of the number of test cases and the execution time. Performing tests with the generated test cases, IABC‐TCG can find more errors with less test cost. [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]

In a device-to-device content delivery network (D2D-CDN), user equipment (UE) covered by a base station can forward content received from the base station to other uncovered UEs in a multi-hop manner to increase network coverage. However, infrastructures, such as local base stations, are destroyed and D2D-CDN cannot operate normally due to the lack of base stations as a content source in disaster scenarios. Unmanned aerial vehicle (UAV) can temporarily serve as a content source with its flexibility to deliver content to terrestrial UEs, which we call it UAV base station (UAV-BS). However, the placement of UAV-BS will be affected by many factors, such as UE distribution and communication interference. Therefore, how to place UAV-BS in a reasonable way to maximize the network throughput and the number of covered UEs while ensuring the communication quality of service (QoS) is still a challenging problem. To solve this problem, first, we propose a UAV-BS placement optimization model (U-POM) and formulate an optimization problem. Secondly, we prove that our maximization problem is NP-hard and propose an improved artificial bee colony algorithm (ABC), called the global optimal artificial bee colony (GOABC) algorithm. Finally, we construct an analysis and testing platform to analyze the accuracy and the stability of the GOABC algorithm. The results highlight the effectiveness of the proposed GOABC algorithm in maximizing network throughput and the number of covered UEs while ensuring the communication QoS. [ABSTRACT FROM AUTHOR]

This paper studies the minimum edge-dilation K-center (MEDKC) problem for edge-weighted, undirected and connected graphs. This problem which is N P -hard holds significant relevance in designing efficient routing schemes for computer networks. To the best of our knowledge, there exists only two variants of genetic algorithm that have been developed for this problem. In this paper, we propose an artificial bee colony algorithm (ABC_MEDKC) for this problem. The proposed ABC_MEDKC incorporates two adaptable neighborhood operators specifically tailored for this problem in which the first neighborhood operator utilizes solution components of another solution, and the second neighborhood operator follows swapping of center vertices with non-center vertices in a mixed strategies of greedy and random approach. On available benchmark instances, computational results of ABC_MEDKC indicate that ABC_MEDKC overall outperforms the existing two variants of genetic algorithm in both solution quality and computational time. ABC_MEDKC also outperforms an existing polynomial-time approximation algorithm developed for this problem in terms of solution quality. ABC_MEDKC finds new values for 17 instances out of 91 instances. In addition, the convergence behavior of ABC_MEDKC and the statistical analysis are also studied. [ABSTRACT FROM AUTHOR]

Computational offloading is an essential technology in Mobile Edge Computing (MEC). To address the shortage of computational offloading strategies in multi-user and multi-MEC server scenarios,this paper proposes a hybrid artificial bee colony approach (Artificial Reverse Sine-Cosine, ARSC). First, the opposition-based learning strategy is used to initialize the population and optimize the initial solution of the population. Then the global optimal bootstrap information of the sine-cosine algorithm is exploited to improve the local search capability in the employed bee stage. Finally, to balance the global and local search capability of the approach, the step size factor is adapted by introducing dynamic perception. Simulation results show that the proposed ARSC approach outperforms offloading strategies based on particle swarm algorithm and artificial bee colony algorithm in convergence, latency, and energy consumption. [ABSTRACT FROM AUTHOR]

In this study operating room scheduling (ORS) problem is addressed in multiresource manner. In the addressed problem, besides operating rooms (ORs) and surgeons, the anesthesia team is also considered as an additional resource. The surgeon(s) who will perform the operation have already been assigned to the patients and is a dedicated resource. The assignment of the anesthesia team has been considered as a decision problem and a flexible resource. In this study, cooperative operations are also considered. A mixed integer linear programming (MILP) model is proposed for the problem. Since the problem is NP-hard, an artificial bee colony (ABC) algorithm is proposed for the problem. The solutions of the ABC are compared with the MILP model and random search. [ABSTRACT FROM AUTHOR]

A disassembly line is an effective disassembly system to recover end-of-life products. In real life, as end-of-life products are subject to varying degrees of wear and tear, task failure may occur in the disassembly process. In this paper, the task failure risks are considered, and an expected profit-based stochastic disassembly line balancing problem is studied. First, a mathematical model is presented to maximise the expected recovering profit with task failures. Then, a hybrid metaheuristic approach is developed to efficiently solve the proposed model, which is integrated with a variable neighbourhood descent method and an artificial bee colony algorithm. Finally, the effectiveness and robustness of the proposed algorithm are verified by three cases, and experiment results show that the solution performance of the proposed approach is superior to the other three existing methods. [ABSTRACT FROM AUTHOR]

Abstract In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN.

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]

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. 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.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

With the advent of 5G communications, the demand for high data rates and improved spectral efficiency has necessitated the exploration of advanced modulation techniques. Orthogonal Frequency Division Multiplexing (OFDM) and Universal Filtered Multi-Carrier (UFMC) are two prominent modulation schemes for 5G systems. However, both OFDM and UFMC suffer from a high Peak-to-Average Power Ratio (PAPR), leading to signal distortion and inefficient power amplification. The Partial Transmit Sequence (PTS) approach is a promising strategy to minimize PAPR in OFDM and is applied to UFMC to minimize PAPR in UFMC and is known as scrambled UFMC (S-UFMC). PTS, however, involves an extensive search to identify the best phase factors that increase computational complexity. The artificial bee colony algorithm is proposed to reduce the computational complexity S-UFMC and OFDM. A novel modulation scheme, amplitude phase shift keying (APSK), is used instead of quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) in S-UFMC and OFDM, which has better performance than conventional modulation schemes. In this research, OFDM, PTS OFDM, UFMC and S-UFMC are compared and it is found that the proposed scrambled UFMC with APSK modulation using ABC algorithm performs better than OFDM and UFMC in terms of PAPR, spectral efficiency and computational complexity. [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]

In the evolutionary algorithm, an intuitive phenomenon is that the eliminated bad particles are also beneficial to convergence in evolutionary algorithms by preventing the generated particles from being close to those eliminated bad particles. Most existing algorithms do not take full advantage of the historical information of these particles or use surrogate models without guaranteeing approximation accuracy. In this study, we propose a multi-granularity general framework to divide the feasible region into different granularities by utilizing completely random trees and computing the spatial distribution of individuals. Secondly, through the sampling and migration strategy, make full use of the sparsity of the calculated individual space distribution and the locality of the best individual in history to replace the poor individual in the current population to speed up the local convergence speed of the algorithm. The time complexity of the algorithm using this framework is equal to the maximum between the time complexity of the evolutionary algorithm using this framework and O(tMlogM), where M denotes the number of points and historical particles generated in an iteration and t denotes the number of iterations. Therefore, the additional computational cost incurred by this framework is very low. Experiments on 12 classical functions, including high-dimensional functions, show that the proposed framework can improve four respective evolutionary algorithms and achieve significantly better results in terms of convergence performance and optimization accuracy. [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]

Resource allocation plays a pivotal role in Cloud Computing (CC), significantly impacting its overall performance. In order to fairly spread workload among servers, network ports, and hard drives, task scheduling is a critical module of CC. Cloud computing experiences request overloading as a result of dynamic computing over the internet. To address this challenge, a novel Cloud based Load balancing using Quantum artificial bee colony Optimization Algorithm has been proposed for task scheduling that can significantly enhance the effectiveness of cloud computing operations. The technique focuses on two important aspects of cloud computing: resource allocation and task scheduling. By achieving load balancing between servers, the proposed CLUQOA (Cloud based Load balancing Using Quantum artificial bee colony Optimization Algorithm) improves reliability and lowers expenses, latency, and response times. To show how effective it is, key performance parameters including makespan, resource usage, task migration, task execution time, and response time are assessed. The results of comparative comparisons with other approaches, such as HUNTER (Holistic resoUrce maNagemenT technique for Energy-efficient cloud computing using aRtificial intelligence), FPNSO (Flower Pollination based Non-dominated Sorting Optimization), and ProHPA (Proactive Hybrid Pod Autoscaling), demonstrate the superiority of CLUQOA in terms of improving resource utilization (35.6%, 26.4%, and 13.9%, respectively) and reducing makespan (11.02%, 9.6%, and 10.4%, respectively). [ABSTRACT FROM AUTHOR]

In our research, we provide an enhanced and effective Krawtchouk moments parameter p optimization approach using the Artificial Bee Colony (ABC) algorithm optimization technique for the purpose of completing tasks including the reconstruction and classification of 3D and 2D images with high quality. The proposed method, which uses the ABC algorithm to generate a suitable parameter based on Krawtchouck moments, aims to accurately characterize the image as a vector known as the descriptor vector of moments, which is then used to reconstruct the image for each order. The simulation and analysis of the findings demonstrate the high quality and effectiveness of the suggested KM-ABC approach for image reconstruction, as well as its excellent accuracy in images with and without noise. [ABSTRACT FROM AUTHOR]

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of "free leasing". In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment's consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer's premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer's planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor's average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented. [ABSTRACT FROM AUTHOR]

As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively. [ABSTRACT FROM AUTHOR]

Disassembly lines are widely utilized to disassemble end-of-life products. Most of the research focuses on the complete disassembly of obsolete products. However, there is a lack of studies on profit and on carbon emission saved. Hence, this study considers the multi-objective partial disassembly line balancing problem with AND/OR precedence relations to optimize profit, saved carbon emission and line balance simultaneously. Firstly, a multi-objective mixed-integer programming model is formulated, which could optimally solve the small number of instances with a single objective. Meanwhile, an improved multi-objective artificial bee colony algorithm is developed to generate a set of high-quality Pareto solutions. This algorithm utilizes two-layer encoding of the task permutation vector and the number of selected parts, and develops two-phase decoding to handle the precedence relation constraint and cycle time constraint. In addition, the modified employed bee phase utilizes the neighborhood operation, and the onlooker phase utilizes the crossover operator to achieve a diverse population. The modified scout phase selects a solution from the Pareto front to replace the abandoned individual to obtain a new high-quality solution. To test the performance of the proposed algorithm, the algorithm is compared with the multi-objective simulated annealing algorithm, the original multi-objective artificial bee colony algorithm and the well-known fast non-dominated genetic algorithm. The comparative study demonstrates that the proposed improvements enhance the performance of the method presented, and the proposed methodology outperforms all the compared algorithms. [ABSTRACT FROM AUTHOR]

In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN. [ABSTRACT FROM AUTHOR]

In order to improve the quality of urban logistics distribution services and meet customer needs to the greatest extent, improve the utilization rate of service resources and reduce the cost of logistics and distribution services, the author proposes an urban logistics distribution scheduling algorithm based on artificial bee colony. Uncertain dynamic customer demands often occur during the execution of logistics distribution services, resulting in the inability to achieve optimal cost by executing the distribution service according to the original plan, in response to such problems with dynamic customer needs, the author conducted a problem analysis, the corresponding mathematical model is established and transformed into a static problem for rescheduling. The result shows: Using the scheduling algorithm to solve the example 100 times, when the vehicle 1 and vehicle 3 can meet their own load, they can serve customers with new needs, which can make the delivery cost the lowest. In the case of a small number of customers, the algorithm can achieve high accuracy, and when the number of customers reaches a certain scale, the stability of the algorithm will decrease slightly. Conclusion: The algorithm achieves the purpose of promptly responding to changes in customer demand and quickly adjusting distribution services.

In this study operating room scheduling (ORS) problem is addressed in multi-resource manner. In the addressed problem, besides operating rooms (ORs) and surgeons, the anesthesia team is also considered as an additional resource. The surgeon(s) who will perform the operation have already been assigned to the patients and is a dedicated resource. The assignment of the anesthesia team has been considered as a decision problem and a flexible resource. In this study, cooperative operations are also considered. A mixed integer linear programming (MILP) model is proposed for the problem. Since the problem is NP-hard, an artificial bee colony (ABC) algorithm is proposed for the problem. The solutions of the ABC are compared with the MILP model and random search.

Abstract 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.

In the face of increasingly complex optimization problems, the Artificial Bee Colony algorithm still encounters some unresolved issues. For instance, the randomness involved in generating the initial population provides diversity to optimization solutions but also leads to convergence among individuals due to the current pseudo-randomness in this process. To address this challenge, this study introduces an improved ABC algorithm based on a two-dimensional queue structure, referred to as KLABC. The KLABC algorithm introduces several key advantages over traditional ABC algorithms. By employing the K-Means algorithm, it enhances the assessment of initial population individuals, ensuring a higher-quality population that aligns with specified requirements. The integration of a two-dimensional queue-based search strategy, along with the application of the first-in, first-out (FIFO) mechanism, not only preserves population diversity but also minimizes the risk of discarding potential optimal solutions during iterative searches. Augmenting this framework, the concepts of the global best solution (GBest) and queue best solution (QBest) are incorporated to fortify the exploration formula, expediting the algorithm's convergence rate. Experimental results on clustering optimization problems across seven UCI datasets validate the effectiveness and robustness of the proposed algorithm. Experimental results indicate that the KLABC algorithm achieves faster convergence compared to the traditional ABC algorithm, with an average performance improvement of 55.63%. Additionally, when comparing the runtime of different algorithms in handling large-scale data, the KLABC algorithm effectively maintains its performance even in scenarios with larger data sizes.

ABSTRACTFor real-time patient heart rate management, most widely used biomedical implantable devices in the cardiovascular system is the cardiac pacemaker (CP). A key factor in keeping the patient alive is the development of novel heart pacing techniques which can reduce the risk of cardiac arrhythmia. The present work is inspired to achieve this goal. To achieve an accurate, controlled, and regulated heart rate, a pacemaker with an intelligent proportional integral derivative (PID) controller is considered. The proposed PID controller is an integration of the traditional PID controller with appropriate tuning, that uses a swarm intelligence-based artificial bee colony (ABC) algorithm for handling the bio-electrical signals. To ensure the efficacy of the proposed controller experiments are conducted. MATLAB/Simulink software is used to test and simulate the suggested model and to adjust the controller gains. The simulation is performed in the time and frequency domain. The resulting pulse rate from the ABC-PID controller has a rise time (0.0985 s), settling time (0.3293 s), maximum overshoot (0.111367%), and MSE (0.0040565). External disturbances of various duty cycles are also introduced in the proposed CP control system. The proposed ABC-PID controller for implanted pacemakers reduces the risk of heart rate over-run.

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction 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.)

Considering the traffic flow of the inner and outer intersection approach of urban arterial roads, an optimization control model of traffic arterial road signal parameters with signal cycle, green light time, and phase offset as the optimization variables is proposed. In order to solve the optimization model of timing parameters, the swarm evolution mechanism of artificial bee colony algorithm is improved and applied. The case study shows that the optimization model of timing parameters can collaboratively optimize the signal cycle, green light time and phase offset of the arterial coordinated control system, and the timing results of the optimization model by comprehensive consideration of inner and outer approach traffic flow are more beneficial to the improvement of traffic efficiency than the decoupling optimization algorithm. The proposed optimization model and solution algorithm can improve the effect of arterial coordinated control. [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]

A multiobjective environmental economic dispatching model of power system is established with minimum economic cost and pollution emission as the optimization objectives to meet the challenges of power system dispatching caused by the global energy crisis and climate warming and promote the realization of the double carbon goal. A multiobjective artificial bee colony algorithm (MOABC) based on nondominant sorting and improved greedy criterion is designed according to the characteristics of the model. In the design of the algorithm, the Taguchi method is used to optimize the parameters, the heuristic method is used to process the constraints dynamically, and a variety of comprehensive evaluation indexes are used to evaluate the algorithm's performance. In the simulation analysis, a six-machine power system and a ten-machine power system are simulated and compared with different algorithms to verify the proposed model's rationality and effectiveness. Finally, the technique for order preference by similarity to the ideal solution (TOPSIS) method is used to determine the optimal compromise solution to provide a reference for the scientific decision-making of dispatchers. The simulation results show that the MOABC algorithm achieved the lowest economic cost (161.159 × 103 yuan for the 6-machine system and 2.771 × 104 $ for the 10-machine system) and pollution emission (194.202 kg for the 6-machine system and 3.639 × 103 kg for the 10-machine system) compared to the multiobjective wind driven optimization, multiobjective particle swarm optimization, and NSGA-II algorithms. [ABSTRACT FROM AUTHOR]

This article discusses the Enhanced Artificial Bee Colony (EABC) algorithm and its application in solving optimization problems. The algorithm is compared to other variants of the Artificial Bee Colony (ABC) algorithm on various test functions and engineering optimization problems. The results show that the EABC algorithm performs well in terms of convergence accuracy and stability, and it is able to find better solutions compared to other ABC variants. The document also presents the formulation and performance comparison of the EABC algorithm on two specific optimization problems: the cantilever beam design problem and the welded beam design problem. The experimental results demonstrate that the EABC algorithm outperforms other variants of the artificial bee colony algorithm in terms of convergence speed and accuracy. Overall, the EABC algorithm is praised for its fast convergence speed, high accuracy, and strong robustness. [Extracted from the article]

Electric energy is the most commonly used energy in people's daily life. In the process of use, how to improve the utilization rate of resources, so as to achieve the purpose of energy saving and environmental protection of power resources is a very significant work. The power grid is crucial in the transmission of electricity, and the energy consumption of substations is a major factor affecting the operation cost. Therefore, it is a key condition to ensure the stable operation of the power grid to scientifically calculate its energy consumption and formulate appropriate energy-saving measures. After consulting a large number of relevant documents and data, it has been found that many researchers have provided new ideas for the calculation of energy consumption and the study of energy-saving measures in substations. This article was adding its own understanding and taking it as the direction and basis. In the introduction, the research significance of substation energy consumption calculation was introduced, and then the academic research and analysis were carried out on the two key sentences of substation energy consumption calculation and analysis and the research of multi-objective artificial bee colony (ABC) algorithm in substation. In the method part, a constructive algorithm theory was proposed, and the application of the algorithm in substation energy consumption was proposed to provide a theoretical basis for the energy consumption calculation and energy conservation measures research of substations based on multi-objective ABC algorithm. At the end of the article, a simulation comparison experiment was carried out and the experiment was summarized and discussed. Firstly, the performance of the two algorithms was analyzed. Then, through the first selection of power plant T and Y, it was concluded that the resistivity of the cable in the distribution room in power plant Y was much higher than the average value based on the experimental results. Therefore, it was taken as the sample subject of the following experiments and analyzed again. For the second time, the analysis of district power plant Y showed that through simple calculation, the average energy efficiency difference of the distribution room before and after use was 3.1. Finally, it was analyzed that the ABC algorithm could not only improve the energy efficiency of the distribution room in the power plant, but also improve the effective efficiency of its power station. It shows that this kind of algorithm is effective and worthy of further study. [ABSTRACT FROM AUTHOR]

In the past decades, the artificial bee colony (ABC) algorithm has gained significant concern and has become an important metaheuristic algorithm. Despite the extensive research on various ABC variant algorithms, achieving a balance between exploratory and exploitative abilities, as well as escaping from local optimal positions, remains a major challenge. In this article, an empirical balanced artificial bee colony (EBABC) algorithm is proposed. Firstly, we utilize the information of randomly selected individual and the global optimal individual at present in a new searching equation to guide individuals towards favorable positions. Additionally, we introduce the diversity of the colony into another searching equation, allowing for the dynamic adjustment of the algorithm's exploratory and exploitative abilities. Furthermore, we design a mechanism for dynamically selecting equations to balance the exploratory and exploitative abilities of the algorithm. Lastly, we introduce a dynamic disturbance strategy for the optimal individual at present to prevent it from getting trapped in local optima. We conduct numerical experiments on 27 benchmark functions and 5 Non-negative Linear Least Squares Problems (NLLS). The results demonstrate the feasibility, effectiveness, and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time. [ABSTRACT FROM AUTHOR]

In the face of increasingly complex optimization problems, the Artificial Bee Colony algorithm still encounters some unresolved issues. For instance, the randomness involved in generating the initial population provides diversity to optimization solutions but also leads to convergence among individuals due to the current pseudo-randomness in this process. To address this challenge, this study introduces an improved ABC algorithm based on a two-dimensional queue structure, referred to as KLABC. The KLABC algorithm introduces several key advantages over traditional ABC algorithms. By employing the K-Means algorithm, it enhances the assessment of initial population individuals, ensuring a higher-quality population that aligns with specified requirements. The integration of a two-dimensional queue-based search strategy, along with the application of the first-in, first-out (FIFO) mechanism, not only preserves population diversity but also minimizes the risk of discarding potential optimal solutions during iterative searches. Augmenting this framework, the concepts of the global best solution (GBest) and queue best solution (QBest) are incorporated to fortify the exploration formula, expediting the algorithm's convergence rate. Experimental results on clustering optimization problems across seven UCI datasets validate the effectiveness and robustness of the proposed algorithm. Experimental results indicate that the KLABC algorithm achieves faster convergence compared to the traditional ABC algorithm, with an average performance improvement of 55.63%. Additionally, when comparing the runtime of different algorithms in handling large-scale data, the KLABC algorithm effectively maintains its performance even in scenarios with larger data sizes. [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]

With the rapid development of artificial intelligence technology, the application of image algorithm in multimedia vision technology has made remarkable progress. In the field of sports, human health is an important concern, so it is of great significance to apply artificial intelligence image algorithm to the research of human health in sports. This paper uses computer vision and image processing technology, combined with artificial intelligence algorithm, to analyze and process the multimedia image of the movement. Through the acquisition and preprocessing of moving images, the key information and features of the movement are extracted. Then, deep learning algorithms and pattern recognition technology are used to analyze and evaluate the posture, movement and body state of the exercisers. Finally, according to the analysis results, personalized health advice and guidance are provided. The results show that the method can accurately identify and analyze the posture, movement and body state of the athletes, provide personalized health advice and guidance, help the athletes to improve the training effect, avoid sports injuries, and improve the level and quality of sports.

Over the past few years, sentiment analysis has become most predominant aspect to be extracted in the modern digital era. Due to digitization all over the world, the number of existing digital documents increasing exponentially day by day. Analyzing such documents to extract the exact meaning itself becomes a tedious task. And analyzing those documents in sentimental point of view has increased the modality of difficultness to another level. In this research work, an effective bio-inspired filter-based feature selection algorithm is proposed to effectively handle the documents from sentiment point of view. A popular bio inspired algorithm called Artificial Bee Colony algorithm is modified using Boolean operators to effectively handle feature selection problem in classifying the documents from sentimental aspect. A total of 9 different documents instances are taken for evaluating the performance of proposed model. A total of 5 different filter-based measures are used to quantify the working model of the algorithm. The outputs are analyzed to state-of-the-art algorithms, and the comparative results demonstrate a substantial improvement over the existing models.