Colony Algorithm Research Articles

Construction and application of a core competitiveness evaluation index system for public health personnel training based on the CIPP model

BACKGROUND: With the change of human disease spectrum and the increase of public emergencies, the public’s demand for health services and health is gradually increasing and higher expectations are placed on public health personnel. OBJECTIVE: Research needs to establish a comprehensive system of evaluation indices for accurate assessment of the core competencies of public health personnel, to enhance their core competitiveness, and introduce novel approaches to evaluate talent development in the field of public health. METHODS: The study is based on the CIPP (Context Input Process Product) model and uses literature analysis, semi-structured interviews, and Delphi methods to construct an evaluation index system for the core competitiveness of public health talent cultivation. The entropy method is used to determine the weight of the model evaluation index. Finally, the improved Artistic Be Colony algorithm (ABC) is used to optimize the BP network, and apply it to evaluate the core competitiveness of public health talent cultivation. RESULTS: The improved BP network achieved the target accuracy within 11 iterations, with the optimal value observed after 16 iterations, producing an MSE (Mean Square Error) value of 10–13. The evaluation of the index system reported a 97% accuracy, and upon application to the university’s public health training programme, nearly 50% of students and teachers achieved core competitiveness quality scores above 90. CONCLUSIONS: The aforementioned method suggests that it can proficiently assess the fundamental competitiveness of training for public health personnel and offer guidance for future development in the domain of public health.

Lévy arithmetic optimization for energy Management of Solar Wind Microgrid with multiple diesel generators for off-grid communities

This paper presents an improved optimization algorithm for the energy management of a renewable energy solar/wind microgrid with multiple diesel generators applied to off-grid remote communities. The main objective aims to solve the economic emission dispatch problem with a price penalty factor to minimize the energy cost and the emission level. An enhanced metaheuristic optimization algorithm, Lévy arithmetic algorithm, is applied to improve the searchability for optimal solution compared to the conventional arithmetic algorithm. The Lévy arithmetic method is used for the management of the microgrid and compared to other metaheuristic optimization algorithms for the same application. Comparative analysis demonstrates good cost savings using the Lévy arithmetic algorithm, compared to other optimization algorithms such as the arithmetic algorithm, crow search algorithm, hybrid modified grey wolf algorithm, interior search algorithm, cuckoo search algorithm, particle swarm algorithm, colony algorithm, and genetic algorithm.

Complex Environment Based on Improved A* Algorithm Research on Path Planning of Inspection Robots

The proposed research aims to accomplish an improved A* algorithm for mobile robots in complex environments. In this novel algorithm, the guidance of environment information is added to the evaluation function to enhance the adaptability of the algorithm in complex environments. Additionally, to solve the problem of path smoothness, the optimal selection rules for child nodes and the bidirectional optimization strategy for path smoothing are introduced to reduce redundant nodes, which effectively makes the search space smaller and the path smoother. The simulation experiments show that, compared with the colony algorithm and Dijkstra algorithms, the proposed algorithm has significantly improved performance. Compared with the A* algorithm, the average planning time is reduced by 17.2%, the average path length is reduced by 2.05%, the average turning point is reduced by 49.4%, and the average turning Angle is reduced by 75.5%. The improved A* algorithm reduces the search space by 61.5% on average. The simulation results show that the effectiveness and adaptability of the improved A* algorithm in complex environments are verified by multi-scale mapping and multi-obstacle environment simulation experiments.

Distribution Network Reactive Power Optimization Method Based on Filter Coordinated Quantum State Transfer Algorithm

To solve the problem of mixed integer nonlinear programming in dynamic reactive power optimization of distribution networks, a two-stage dynamic reactive power optimization model is used to reduce the size of the reactive power optimization calculation.The first stage is nonlinear planning, with the objective of minimizing the combined system network loss and node voltage offset. The second stage is a mixed-integer planning, with the objective of minimizing the incremental power system network loss on the basis of satisfying the constraints such as the number of equipment regulation throughout the day. In view of the fact that the conventional penalty function method to deal with the constraints will result in improper selection of penalty factors, an improved quantum state transfer algorithm with mixed filter technique is proposed. The algorithm adopts a two-population parallel search approach, and the individual adaptation degree and constraint violation degree are constituted as filter pairs as evaluation metrics to replace the penalty functions. Finally, the proposed algorithm, the traditional state transfer algorithm and Colony Algorithm are solved separately for the model using the IEEE 14 system as examples, and the correctness of the proposed model and algorithm is verified by comparing and analyzing the simulation results.

ABCAttack: A Gradient-Free Optimization Black-Box Attack for Fooling Deep Image Classifiers.

The vulnerability of deep neural network (DNN)-based systems makes them susceptible to adversarial perturbation and may cause classification task failure. In this work, we propose an adversarial attack model using the Artificial Bee Colony (ABC) algorithm to generate adversarial samples without the need for a further gradient evaluation and training of the substitute model, which can further improve the chance of task failure caused by adversarial perturbation. In untargeted attacks, the proposed method obtained 100%, 98.6%, and 90.00% success rates on the MNIST, CIFAR-10 and ImageNet datasets, respectively. The experimental results show that the proposed ABCAttack can not only obtain a high attack success rate with fewer queries in the black-box setting, but also break some existing defenses to a large extent, and is not limited by model structure or size, which provides further research directions for deep learning evasion attacks and defenses.

A Two-Stage Hybrid Adaptive Particle Swarm Optimization (PSO) Based Artificial Bee Colony (ABC) Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

A Two-Stage Hybrid Adaptive Particle Swarm Optimization (PSO) Based Artificial Bee Colony (ABC) Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Ayurvedic Plant Identification using Image Processing and Artificial Intelligence

Wireless networks provide small sensing, machine and wireless networking nodes. Different designs and implementation techniques were built based on the device requirements for wireless network sensors (WSN). Sensor networks are used in various applications, such as environmental monitoring, home automation, military applications, etc. In this study introduce an architectural survey and deployment of nodes in the Wi-Fi Sensor network in this article. The environmental features that can be added to the sensor networks are given. The program relies on the node installed in the WSN and is deterministic or random. But the biggest issue in both cases is the coverage of the region involved. Researcher also describe WSN routing protocols. In this paper, a new technique to deployment problem is proposed based on the artificial bee colony (ABC) algorithm which is enhanced for the deployment of sensor networks to gain better performance by trying to increase the coverage area of the network and energy consumption. The good performance of the proposed EABC algorithm shows that it can be utilized in the deployment of WSN.

Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework

There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.

Pricing and inventory control decisions in the stochastic hybrid production systems with multiple recovery options

Although pricing and inventory control are crucial decisions in each production system, these decisions are usually investigated separately. This paper considers pricing and inventory control decisions simultaneously in a hybrid production system. The hybrid production system has two recovery options, remanufacturing and refurbishing sites. The demand follows Poisson distribution, which depends on the sale price of each product. Returned products arrive according to a Poisson process. Each returned product can be remanufactured, refurbished, or disposed. The time to manufacturing, refurbishing, and remanufacturing a product also follows an exponential distribution. By modeling the system as a Markov chain, the long-run expected profit function is obtained in terms of the dispose-down-to level of returned products and the order-up-to level and the sale price of serviceable products. A three-dimensional state space of the Markov Chain dependent to the sale price is developed considering pricing and inventory control decisions simultaneously with remanufacturing and refurbishing returned products. Since the model is a mixed integer nonlinear programming and known as complex models, the Artificial Bee Colony (ABC) algorithm, simulation and complete search method are used to solve the problem. The results show that by increasing the purchase price of the returned products, the amount of returned products will increase. If the refurbishing cost of the returned products is high or the disposal cost is low, less inventory should be kept in the system with a high price of serviceable products. If the lost sale cost is high, the more inventory should be maintained. Moreover, by decreasing the price elasticity of demand, the customer’s demand increases, and then more inventory should be maintained in the system.

Torsional Stiffness Correction of the Split-Type Triple-Box Steel Box Girder Based on Refined Simulation

Split-type steel box girders are widely used in long-span bridges because of their good wind-resistance performance. In the design stage, a simple finite element model is usually established based on the beam element for wind-resistance design. However, since the irregular cross-beams and diaphragms in the split steel box girder cannot be virtually established, the stiffness of the girder will be underestimated. To improve the accuracy in simulating stiffness of the split-type triple-box steel box girder (STSBG) with the beam element model (BEM), a correction is made to the initial beam element model (IBEM) based on the result of a more refined finite element model. ANSYS is adopted to make a refined model (RM) of a bridge with STSBG as its girder and to calculate its aerostatic responses and dynamic characteristics in 3 typical construction states and 1 finished state. With the reference value, an objective function of the overall residual sum of squares is constructed for the torsion angle of the girder and the frequency of the bridge. Then, the beam element is used for conventional modelling of the bridge, and artificial bee colony (ABC) algorithm is adopted for the optimization and correction of structure parameters of the BEM of the girder. Finally, static and dynamic characteristics of the IBEM and the corrected beam element model (CBEM) are compared with values of the corresponding RM to evaluate the validity of the correction of the model. The results show that the aerostatic responses and dynamic characteristics of the CBEM are close to calculated values of the RM. In more detail, the relative error between the torsion angle of the girder in the middle span of the BEM and the corresponding reference value in the finished state is decreased from +61.71% to +4.94%, and the relative error of torsional fundamental frequency is decreased from −17.43% to +3.66%. According to the calculated value of the RM, ABC algorithm would satisfactorily improve the accuracy in simulating torsional stiffness of the STSBG with the IBEM. This research is expected to provide reference for beam element modelling, which is conducive to accurately simulating torsional stiffness of the STSBG.

Weighted Least Square Design Technique for Hilbert Transformer using Fractional Derivative

In this paper, a new design method for realization of Hilbert transformer (HT) by an all-pass infinite impulse response (IIR) filter is proposed. The design problem is devised as minimization of the weighted least squared errors of the phase response in the frequency domain, where the phase response of denominator polynomial of HT acts as a weighting function. The least squared design techniques usually suffer from large errors at the band edge frequencies. To resolve this issue, our aim is to have the phase response of the all-pass IIR filter closely matched with the desired phase response of HT. The approximation with desired phase response of HT has been achieved by using fractional derivatives constraints (FDCs). In our experiments, it is observed that the design problem using single fractional derivative (FD) has a multimodal behavior in nature. Moreover, highly precise value of order of FD is required, whose exploration task is computationally expensive. Therefore, recently developed heuristic search techniques, also known as swarm-based optimization techniques (SOTs), such as particle swarm optimization (PSO), and its variants, cuckoo search (CS) algorithm, and artificial bee colony (ABC) algorithm, are used for finding the required values. However, these methods are capable of solving non-differentiable and multimodal problems due to their multi-dimensional randomly guided search mechanism. The proposed methodology has gained 59% improvement in phase approximation error as compared to conventional reported techniques.

Forecasting diversion type hydropower plant generations using an artificial bee colony based extreme learning machine method

ABSTRACT In this study, a hybrid method based on extreme learning machine (ELM) method and artificial bee colony (ABC) algorithm was proposed to forecast small hydropower plant generations. The input weights and biases of ELM were optimized by ABC algorithm to achieve more accurate forecasting results. The forecasting performance of the proposed method was compared with benchmark methods, namely backpropagation-based artificial neural network (ANN), radial basis function-based ANN, and long short-term memory. The experimental results verified that the proposed method significantly outperformed the benchmark methods. Specially, when the proposed method was compared with ELM, the improvement percentages in correlation coefficient, root mean square error, and mean absolute error values were calculated as being 6.20%-29.08%-26.29% for 14 days ahead and 5.47%-24.42%-20.33% for 21 days ahead, respectively.

Fault-Tolerant Control Design for Multirotor UAVs Formation Flight

This paper proposes the use of the Gbest-Guided Artificial Bee Colony (GABC) algorithm to solve an online optimization problem for the leaderless distributed formation control of hexarotor Unmanned Aerial Vehicles (UAVs). The GABC is employed to optimize a cost function for each agent while ensuring the convergence of the fleet to the target position and averting both obstacles and collisions with other UAVs. The GABC algorithm has been shown to be competitive with some other conventional biological-inspired algorithms such as the Particle Swarm Optimization (PSO). Fault-Tolerant Control (FTC) methods are presented and tested on several scenarios, particularly we considered the cases of loss of agents and actuator faults in the fleet. Results show the success of the proposed FTC methods to minimize the faults effect on the formation final goal.

4E analysis and multi-objective optimisation for a solar hybrid steam power plant using ABC algorithm: a case study in Iran

The objectives of this study are 4E analysis and multi-objective optimisation for solar hybrid fossil fuel power generation system and steam power plant without solar field (WSF) of Karaj during a year. The analysis is performed using MATLAB programing, and validated with a power plant unit data. The results show that system with oil-water heat exchanger (solar hybrid system), has less CO2 emission and fuel consumption than WSF power plant but 2.8% more exergy efficiency. The annual average contribution of steam on the production of electricity during sunny hours and during the day, are 15% and 7.3%, respectively. By using artificial bee colony (ABC) algorithm, system is optimised for maximum exergy efficiency and minimum cost of produced electricity. Furthermore, sensitivity analysis is performed to study the solar tracking modes, changing the number of collector loops, capacity of the power plant, interest rate and plant economic life on unit cost.

A Survey of Bio Inspired Algorithms for Web Information Extraction and Optimization for Big Data Analytics

Information extraction is systematic process of extracting structured information from documents which has both unstructured and semi structured data set. Data available over the web is unstructured which is processed and delivered that may be challenging due to massive data over web. Bigdata analytics approach is used in the computation field where massive data is managed and processed as information. Data from various sources like industries, institutes are processed using algorithms in efficient means employing web of things or Internet of things used to mine such a large data. Bio inspired algorithms have evolved from application of heuristic approaches to meta-heuristic and hyper-heuristic methodologies. Bio inspired techniques are categorized into human inspired algorithms, Swarm Intelligence algorithms, evolutionary algorithms and ecology based algorithms. Genetic algorithms are purely heuristic in nature and are employed for computation and extracting information and from big data. This improves the computation speed effectively for extracting web related information as evolutionary algorithm resolves information extraction problems. The Ant colony and Particle Swarm Intelligence algorithms are of meta-heuristic in nature. The Cuckoo search, Artificial Bee Colony, Firefly algorithm and Bat algorithms are of hyper heuristic in nature i.e., they employ a combination of methods. Web information extraction using bio inspired concepts and genetic operators increases efficiency, capability to search particular information in massive data in web. Some of the tools that are available for data extraction and mining are DataMelt, Apache Mahout, Weka, Orange and Rapid Miner for enhancing web data extraction efficiency. This survey on bio inspired methodologies can be extended to parameter tuning and controlling is another big strategy that can be implemented, in addition to convergence speed up.

Improved Hargreaves Model Based on Multiple Intelligent Optimization Algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

Reference crop evapotranspiration (ET0) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET0 estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961–2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET0 value was calculated by FAO-56 Penman–Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley–Taylor, Imark–Allen and Jensen–Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R2 range 0.74–0.98) are more accurate than 4 empirical models (R2 range 0.55–0.84), and ET0-PSO-HG has the best accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with average R2 of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively; on a monthly scale, ET0-PSO-HG also has the highest accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with median R2 of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET0 estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET0 in humid regions in southwest China regions.

Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.

Overview of the LTE network and removal of unwanted Handover in the network and how to implement it with the ns3 simulator

Today, cellular networks, notably LTE, are used in substructure communications because of ease of launch, tranquil maintenance, the movable equipment, the communal accessed, and other services. LTE has superior improvement in efficiency, higher data rates, lower time lag, cost savings, capacity, and coverage. How to categorize BTS antennas and handover at the border points are main consequence in these networks. there is possibility to set unessential handover indoors. Also ability to eliminate unnecessary handovers and optimized the networks structure. As a response, several scenarios, such as using the SOM neural network, the X-means clustering algorithm, ICA colonial competition algorithm , and other innovative and meta-innovative algorithms are introduced. Setting up, testing, and evaluating need to a simulated environment. In this research, we tried to examine this type of the LTE network and the its Handover, by analyze the tools’ feature and network simulation languages such as (NS-2, NS-3, OMNET ++), we can present the right choice. Then, we will examine the possibility of implementing LTE and Handover scenarios in NS-3.

ABC Algorithm-Based Subblocks Set Design for OFDM With All Index Modulation

In this letter, we propose a low complexity artificial bee colony (ABC) algorithm based subblocks set design to maximize the diversity gain of orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), which is inspired by OFDM with index modulation (OFDM-IM). Unlike the conventional OFDM-IM, where constellation points of the M-ary signals are fixed, ABC aided OFDM-AIM (ABC-OFDM-AIM) scheme has flexible subcarrier constellation points to enhance the diversity gain, which ensures better BER performance. Also, a parallel ABC (PABC) algorithm, which has a more powerful search strategy than the ABC algorithm, is proposed (PABC-OFDM-AIM) to further enhance the diversity gain of the OFDM-AIM. Performances of the proposed schemes have been tested according to the average bit error probability (ABEP), search complexity, and BER performance criteria. Simulation results show that the proposed schemes outperform other OFDM-IM based schemes, especially in the improvement of BER.

Efficient Semi-Supervised Learning and Sparse Structural Learning for Feature Selection of Leukemia Dataset

The amount of data produced in health informatics growing large and as a result analysis of this huge amount of data requires a great knowledge which is to be gained. The basic aim of health informatics is to take in real world medical data from all levels of human existence to help improve our understanding of medicine and medical practices. Huge amount of unlabeled data are obtainable in lots of real-life data-mining tasks, e.g., uncategorized messages in an automatic email categorization system, unknown genes functions for doing gene function calculation, and so on. Labelled data is frequently restricted and expensive to produce, while labelling classically needs human proficiency. Consequently, semi-supervised learning has become a topic of significant recent interest. This research work proposed a new semi-supervised grouping, where the performance of unsupervised clustering algorithms is enhanced with restricted numbers of supervision in labels form on constraints or data. The previous system designed a Clustering Guided Hybrid support vector machine based Sparse Structural Learning (CGHSSL) for feature selection. However, it does not produce a satisfactory accuracy results. In this research, proposed clustering-guided with Convolution Neural Network (CNN) based sparse structural learning clustering algorithm. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is progressed for learning cluster labels of input samples having more accuracy guiding features election at same time. Concurrently, prediction of cluster labels is as well performed by CNN by means of using hidden structure which is shared by various characteristics. The parameters of CNN are then optimized maximizing Multi-objective Bee Colony (MBO) algorithm that can unravel feature correlations to render outcomes with additional consistency. Row-wise sparse designs are then balanced to yield design depicted to suit for feature selection. This semi supervised algorithm is utilized to choose important characteristics from Leukemia1 dataset additional resourcefully. Therefore dataset size is decreased significantly utilizing semi supervised algorithm prominently. As well proposed Semi Supervised Clustering-Guided Sparse Structural Learning (SSCGSSL) technique is utilized to increase the clustering performance in higher. The experimental results show that the proposed system achieves better performance compared with the existing system in terms of Accuracy, Entropy, Purity, Normalized Mutual Information (NMI) and F-measure.