Adaptive Artificial Bee Colony Research Articles

Prediction of monkeypox infection from clinical symptoms with adaptive artificial bee colony-based artificial neural network

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%.

Machine learning-based decision support system for orthognathic diagnosis and treatment planning

BackgroundDento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations.MethodsPatients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes.ResultsThe binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups.ConclusionsMachine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.

Secure Steganography Model over Cloud Environment using Adaptive ABC and Optimum Pixel Adjustment Algorithm

An effective security model with low computational complexity, minimal quality-compromised, and improved security robustness is essential due to the rapid expansion of multimedia data communication through different cloud services. An image steganography model has been proposed for the security of multimedia data in an unreliable cloud environment. This study's main goal is to provide efficient steganography with barely hidden information. A secure data communication model has been developed over a cloud environment using the Adaptive Artificial Bee Colony Algorithm and the Optimum Pixel Adjustment Algorithm Based Image Steganography Method. An adaptive ABC (artificial bee colony) technique is applied to select the optimal pixel positions in the cover image because the goal of this investigation is to increase PSNR. The Optimal Pixel Adjustment approach is used to minimise embedding errors while maintaining the stego image's appearance identical to the cover image after embedding. The MATLAB platform is used to implement the proposed method. The results show that the proposed AABC-based OPA is more efficient across all measures investigated during the embedding and extraction processes.

Adaptive Artificial Bee Colony Algorithm-Based Enhancement of Data Security in Cloud Computing

Adaptive Artificial Bee Colony Algorithm-Based Enhancement of Data Security in Cloud Computing

3D deployment of UAV-mounted base stations for heterogeneous access requirements

Recently, unmanned aerial vehicles (UAVs) have been reported a lot as aerial base stations (BSs) to assist wireless communication in Internet of Things (IoT). However, most results for UAV deployment require uniform access requirements and obstacle-free environment. This paper considers multi-UAV-mounted BSs deployment problem in obstacle-laden environment to achieve on-demand coverage for ground user equipments (UEs), with the goal of minimizing the number of required UAVs for full coverage of all UEs by optimizing the three-dimensional (3D) positions of UAVs as well as UE clustering. On one hand, the considered problem is challenging as the constraints on the non-uniform Quality of Service (QoS) requirements, the service ability of each UAV, the beamwidth of the directional antenna equipped by each UAV and obstacle avoidance are jointly taken into account. On the other hand, these practical considerations contribute to broadening the scope of applications. We formulate the problem as a mixed-integer programming problem and develop a three-step placement algorithm to achieve the goal. First, the maximum allowed service radius of each UE is derived based on the Karush-Kuhn-Tucker (KKT) condition to satisfy required signal-to-interference-plus-ratio (SINR) threshold. Second, a greed-prior chaotic search enabled adaptive artificial bee colony (GP-CA2BC) algorithm is proposed to group the UEs such that the minimum number of required UAVs can be obtained. Third, the 3D position of each UAV is optimized to further enhance the QoS. Finally, some simulation results are presented to demonstrate the superiority of the proposed scheme in decreasing the number of required UAVs as well as improving the QoS compared with the existing works.

An adaptive artificial bee colony for hybrid flow shop scheduling with batch processing machines in casting process

Hybrid flow shop scheduling problem (HFSP) with real-life constraints has been extensively considered; however, HFSP with batch processing machines (BPM) at a middle stage is seldom investigated. In this study, HFSP with BPM at a middle stage in hot & cold casting process is considered and an adaptive artificial bee colony (AABC) is proposed to minimise makespan. To produce high quality solutions, an adaptive search process with employed bee phase and adaptive search step is implemented. Adaptive search step, which may be onlooker bee phase or cooperation or empty, is decided by evolution quality and an adaptive threshold. Cooperation is performed between the improved solutions of one employed bee swarm and the unimproved solutions of another swarm. Six search operators are constructed and search operator is adaptively adjusted. A new scout phase is also given. A lower bound is provided and proved. Extensive experiments are conducted. The computational results validate that new strategies such as cooperation are effective and efficient and AABC can obtain better results than methods from existing literature on the considered problem.

A Reinforcing-Learning-Driven Artificial Bee Colony Algorithm for Scheduling Jobs and Flexible Maintenance under Learning and Deteriorating Effects

In the last decades, the availability constraint as well as learning and deteriorating effects were introduced into the production scheduling theory to simulate real-world case studies and to overcome the limitation of the classical models. To the best of our knowledge, this paper is the first in the literature to address the permutation flowshop scheduling problem (PFSP) with flexible maintenance under learning and deterioration effects to minimize the makespan. Firstly, we address the PFSP with flexible maintenance and learning effects. Then, the deteriorating effect is also considered. Adaptive artificial bee colony algorithms (ABC) enhanced with Q-learning are proposed, in which the Nawaz–Enscore–Ham (NEH) heuristic and modified NEH heuristics are hybridized with a maintenance insertion heuristic to construct potential integrated initial solutions. Furthermore, a Q-learning (QL)-based neighborhood selection is applied in the employed bees phase to improve the quality of the search space solutions. Computational experiments performed on Taillard’s well-known benchmarks, augmented with both prognostic and health management (PHM) and maintenance data, demonstrate the effectiveness of the proposed QL-driven ABC algorithms.

Multi-objective structural profile optimization of ships based on improved Artificial Bee Colony Algorithm and structural component library

This paper addresses the multi-objective, design variables discrete problem of river boat profile structure optimization. The study considers not only the economic performance but also the stability of the boat to a certain extent when selecting the optimization objectives. First, the standard component library of the vessels is established and the discrete structural model is converted into a continuous optimization model based on the component library index. Second, to balance the global search and local search capability of the optimization algorithm, an adaptive Artificial Bee Colony Algorithm (I-ABC) is proposed. I-ABC uses a modified chaotic mapping to improve the diversity of nectar sources, and adaptive search and following mechanisms are adopted for the leading and following bee stages, respectively. Then, a Dominating Artificial Bee Colony Algorithm (D-ABC) is proposed for multi-objective optimization by introducing Pareto domination and considering the degree of individual crowding in terms of hypersphere neighborhood. Then, numerical simulations and comparative analysis of the proposed algorithm are performed using different test functions, and the results show that I-ABC and D-ABC have excellent performance in both single-objective and multi-objective optimization. Finally, using the component library established in this paper and the proposed improved Bee Colony Algorithm to optimize the profile structure of a chemical-liquid tanker, the optimization results show that the maximum reduction of the ship profile area is 5.08% and the maximum reduction of the vertical center height is 7.427%. The proposed optimization algorithm is also applicable to other river boats.

Application of Adaptive Artificial Bee Colony Algorithm in Reservoir Information Optimal Operation

Application of Adaptive Artificial Bee Colony Algorithm in Reservoir Information Optimal Operation

Differentiation of NaCl, NaOH, and β-Phenylethylamine Using Ultraviolet Spectroscopy and Improved Adaptive Artificial Bee Colony Combined with BP-ANN Algorithm.

The aim of this study is to enhance the classification performance of the back-propagation-artificial neural network (BP-ANN) algorithm for NaCl, NaOH, β-phenylethylamine (PEA), and their mixture, as well as to avoid the defects of the artificial bee colony (ABC) algorithm such as prematurity and local optimization. In this paper, a method that combined an improved adaptive artificial bee colony (IAABC) algorithm and BP-ANN algorithm was proposed. This method improved the ABC algorithm by adding an adaptive local search factor and mutation factor; meanwhile, it can enhance the abilities of the global optimization and local search of the ABC algorithm and avoid prematurity. The extracted score vectors of the principal component of the ultraviolet (UV) spectrum were used as the input variable of the BP-ANN algorithm. The IAABC algorithm was used to optimize the weight and threshold of the BP-ANN algorithm, and the iterative algorithm was repeated until the output accuracy was reached. The output variable was the classification results of NaCl, NaOH, PEA, and the mixture. Meanwhile, the proposed IAABC-BP-ANN algorithm was compared with discriminant analysis (DA), sigmaid-support vector machine (SVM), radial basis function-SVM (RBF-SVM), BP-ANN, and ABC-BP-ANN. Then, the above algorithms were used to classify NaCl, NaOH, PEA, and the mixture, respectively. In the experiment, four indicators, accuracy, recall, precision, and F-score, were used as the evaluation criteria. In addition, the regression equation parameters of the mixture for the testing set were obtained by BP-ANN, ABC-BP-ANN, and IAABC-BP-ANN models. All of the results showed that IAABC-BP-ANN exhibits better performance than other algorithms. Therefore, IAABC-BP-ANN combined with UV spectroscopy is a potential identification tool for the detection of NaCl, NaOH, PEA, and the mixture.

Optimal Power Flows Control for Home Energy Management With Renewable Energy and Energy Storage Systems

This article presents a home energy management system (HEMS) based on the proposed adaptive artificial bee colony (AABC) optimization method, of managing a residential microgrid connected to the main grid. The studied microgrid consists of a photovoltaic system (PVS), a battery storage system (BSS), and electricity loads. The proposed AABC-based HEMS optimizes power flow ratio among PVS, BSS, and the main grid with respect to the load demand and electricity rate in order to minimize electricity consumption cost. Three dc-dc converters are actively controlled with an active dual-loop control architecture to dominate the power distribution among multi-power sources. The performances of the proposed optimal power flows control (PFC) under different electricity load levels and PVS generation conditions are examined. In the PFC experiments, the designed HEMS is implemented on a digital signal processor. The experimental results demonstrate the effectiveness and superiority of the proposed AABC-based HEMS compared with the rule-based control strategy and the equivalent consumption minimization strategy in terms of electricity consumption cost.

Substantial adaptive artificial bee colony algorithm implementation for glioblastoma detection

<p><span lang="EN-US">Glioblastoma multiforme (GBM) is a high-grade brain tumor that is extremely dangerous and aggressive. Due to its rapid development rate, high-grade cancers require early detection and treatment, and early detection may possibly increase the chances of survival. The current practice of GBM detection is performed by a radiologist; due to the enormous number of cases, it is nevertheless tedious, intrusive, and error-prone. Thus, this study attempted a substantial adaptive artificial bee colony (a-ABC) algorithm implementation in providing a non-invasive approach for GBM detection. The basic statistical intensity-based analysis of minimum (minGL), maximum (maxGL), and mean (meanGL) of grey level data was employed to investigate the GBM's feature properties. The a-ABC's performance for adaptive GBM detection identification was evaluated using T1-weighted (T1), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and T1-contrast (T1C) which are four different magnetic resonance imaging (MRI) imaging sequences. Hundred and twenty MRI of GBM images were assessed in total, with 30 images per imaging sequence. The overall mean of GBM detection accuracy percentage was 93.67%, implying that the proposed a-ABC algorithm is capable of detecting GBM brain tumors. Other feature extraction strategies, on the other hand, may be added in the future to enhancee the performance of feature extraction. </span></p>

Solution of fiber Raman amplifier model using binary search equation-based adaptive artificial bee colony algorithm

Due to the need for high bandwidth, there is growing interest in distributed fiber Raman amplifiers (FRA). In addition to having wide bandwidth, FRAs have the advantages of a low noise factor and simplicity of use. However, the optimization of the wavelength and power values of the pump lasers to be used in FRAs for a broadband and flat gain region is an important problem to be solved. In this study, a distributed FRA system was set using 100 signals with a 50-km propagation distance and 8 pump signals in the opposite direction, which were gain-flattened. The attenuation coefficient of SMF-28 type optical fiber was used in the system. First, optimum pump wavelengths and power levels were found by the binary search equation based adaptive artificial bee colony algorithm. Then, the FRA boundary value problem (BVP) was solved with the MATLAB BVP solver. Finally, the analytical Jacobian matrix required for the solution of the equation was included in the system, and faster and more effective results were obtained. The results show that the gain ripple of 100 optical signals was 0.5 dB at the maximum.

An adaptive artificial bee colony algorithm enhanced by Deep Q-Learning for milk-run vehicle scheduling problem based on supply hub

To meet market challenges and reduce costs, supply hubs are broadly applied to achieve just-in-time production. In this paper, a milk-run vehicle scheduling problem considering the ratio of parts is proposed based on the supply hub mode. A mathematical model is therefore established, aiming to minimize the energy consumption of electric vehicles and the time penalty of early or late distribution simultaneously in the scheduling horizon. To solve the proposed problem which is inherently NP-hard, an adaptive artificial bee colony algorithm enhanced by deep Q-learning network (AABC-DQN) is constructed, where the deep Q-learning network is used to reasonably select the neighborhood search operators to systematically expand the search range. Experimental results have demonstrated the superiority of the proposed AABC-DQN, which outperforms the benchmark algorithms in both solution quality and convergence ability.

Adaptive Artificial Bee Colony Algorithm for Nature-Inspired Cyber Defense

With the significant growth of the cyber environment over recent years, defensive mechanisms against adversaries have become an important step in maintaining online safety. The adaptive defense mechanism is an evolving approach that, when combined with nature-inspired algorithms, allows users to effectively run a series of artificial intelligence-driven tests on their customized networks to detect normal and under attack behavior of the nodes or machines attached to the network. This includes a detailed analysis of the difference in the throughput, end-to-end delay, and packet delivery ratio of the nodes before and after an attack. In this paper, we compare the behavior and fitness of the nodes when nodes under a simulated attack are altered, aiding several nature-inspired cyber security-based adaptive defense mechanism approaches and achieving clear experimental results. The simulation results show the effectiveness of the fitness of the nodes and their differences through a specially crafted metric value defined using the network performance statistics and the actual throughput difference of the attacked node before and after the attack.

Research on Fault Location in DC Distribution Network Based on Adaptive Artificial Bee Colony Slime Mould Algorithm

Research on Fault Location in DC Distribution Network Based on Adaptive Artificial Bee Colony Slime Mould Algorithm

An improved orthogonal matching pursuit method for denoising high-frequency ultrasonic detection signals of flip chips

Noise suppression of an echo signal plays an important role in high-frequency ultrasonic testing of flip chips. This paper proposes an orthogonal matching pursuit (OMP) method optimized by an improved artificial bee colony (ABC) algorithm for denoising high-frequency ultrasonic testing signals of flip chips. We add adaptive learning factors to change the way the ABC randomly selects the search direction, which speeds up the convergence speed of the algorithm. The improved ABC, named adaptive artificial bee colony (AABC), replaces the greedy search process of OMP. Instead of searching for the atom that best matches the echo signal, the improved OMP algorithm searches for the optimal parameter and replaces the atom with a set of parameters with practical physical significance. The introduction of the AABC changes the search space of OMP from discrete dictionary space to continuous parameter space, which minimizes the error between the atom and echo signal and leads to an accurate approximation. Additionally, the AABC reduces the search times of OMP and improves the convergence speed. Then, the wavelet transform thresholding technology is combined with the attenuation characteristic of high-frequency ultrasound to eliminate the influence of error decomposition caused by noise and inaccurate sparsity setting. We present the noise suppression experimental results of high-frequency ultrasonic simulation and real testing signals of flip chips, including Gaussian white noise and correlated noise, and compare the proposed method with other sparse representation methods, including matching pursuit (MP) and OMP. The results demonstrate the superior performance of the proposed method.

Modeling and Estimation of CO2 Emissions in China Based on Artificial Intelligence.

Since China's reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO2) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO2 emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO2 emissions, this study establishes a hybrid intelligent algorithm model suitable for CO2 emissions prediction based on China's CO2 emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5% in the advance prediction of CO2 emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues.

An adaptive artificial bee colony for unrelated parallel machine scheduling with additional resource and maintenance

Unrelated parallel machine scheduling problem with additional resource (UPMSPR) has been extensively considered; however, UPMSPR with preventive maintenance (PM) is seldom studied; on the other hand, artificial bee colony (ABC) has potential advantages on solving UPMSPR with PM because of the extensive applications of ABC to unrelated parallel machine scheduling. In this paper, an adaptive artificial bee colony (AABC) algorithm is proposed to solve UPMSPR with PM and makespan minimization. A new solution representation is presented Evolution quality of population is evaluated and an adaptive onlooker bee phase is implemented, in which multiple search operators including reduced variable neighborhood search (RVNS) are constructed, the number of the used onlooker bees and search operator are dynamically determined. Historical optimization data and a new scout phase are also used. A number of experiments are conducted on 300 instances from the literature. The computational results demonstrate that the new strategies are effective and AABC can provide better results than the algorithms from the literature.

An adaptive search equation-based artificial bee colony algorithm for transportation energy demand forecasting

This study aimed to develop a new adaptive artificial bee colony (A-ABC) algorithm that can adaptively select an appropriate search equation to more accurately estimate transport energy demand (TED). Also, A-ABC and canonical artificial bee colony (C-ABC) algorithms were compared in terms of efficiency and performance. The input parameters used in the proposed TED model were the official economic indicators of Turkey, including gross domestic product (GDP), population, and total vehicle kilometer per year (TKM). Three mathematical models, linear (A-ABCL), exponential (A-ABCE), and quadratic (A-ABCQ) were developed and tested. Also, economic variables were generated using the "curve fitting" technique to see TED's projections for the year 2034, under two different scenarios. In the first scenario, the results of linear, exponential, and quadratic models according to 2034 TED estimates were 40.1, 31.6, and 70.5 million tons of oil equivalent (Mtoe), respectively. In the second scenario, the results of linear, exponential, and quadratic models according to the TED estimates for 2034 were found as 40.0, 31.5, and 66.5 Mtoe, respectively. The presented models, A-ABCL, A-ABCE, A-ABCQ for the solution of the TED problem, produced successful results compared to the studies in the literature. Besides that, according to global error metrics, developed models generated lower error values than C-ABC. Furthermore, consumption estimation values of A-ABCL and A-ABCE were lower than A-ABCQ. According to A-ABCQ model estimations for both scenarios, the TED value would increase approximately three times from 2013 to 2034.