Bee Colony Algorithm Research Articles

Model Development for Optimized Land Usage in Urban Agglomeration Zone Using Bee Colony Algorithms

This research work was developed to increase the output of urban zone regions and also to bring out the best use of the space of the urban zone regions. The problems associated with the current research work is basically poor accuracy, slow convergence speed and low rationality of evaluation index. We have proposed to use Bee Colony Algorithm which optimizes the usage areas of land in urban zone regions. The work that we developed a model using the bee colony algorithm for best optimization. The various evaluation units are divided by grid method. By this algorithm principle the weighted summation method of indexes is used to calculate the evaluation units. The the evaluation model is developed by using the above calculated evaluation value of land usage spatial adaptability in the required regions. By the evaluation function of urban land suitability, the cost function and spatial coordination function of urban land usage and the spatial optimization model of land use in urban agglomeration development zone is constructed. The model is developed by the bee colony algorithm and best fitting solution of land usage in urban agglomeration development zone is obtained. The research results prove that the evaluation index of the proposed method is reasonable, the accuracy of the evaluation results is high, and the convergence speed is fast.

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.

Sampling from social networks’s graph based on topological properties and bee colony algorithm

Sampling from social networks’s graph based on topological properties and bee colony algorithm

Cloud Service Optimization Method Based on Dynamic Artificial Ant-Bee Colony Algorithm in Agricultural Equipment Manufacturing

In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a cloud service optimization method is proposed in this paper. For the optimization model, the dynamic coefficient strategy and the reliability feedback update strategy are added to the mathematical model to strengthen the applicability of farming season. As optimization algorithm, a dynamic artificial ant-bee colony algorithm (DAABA) based on artificial ant colony algorithm and bee colony algorithm is presented. The optimal fusion evaluation strategy is used to save optimization time by reducing the useless iteration, and the iterative adjustment threshold strategy is adopted to improve the accuracy of cloud service by increasing the size of bee colony. Finally, the performance of DAABA is verified to be more superior by comparing with other algorithms.

Structures and properties of VB5−/0 clusters from density functional theory calculations

Density functional theory with the BPW91 functional and def2-TZVP basis sets was used to investigate the geometric structures of VB5−/0 clusters. By using the bee colony algorithm, 300 initial structures are created for the studied cluster. The geometry optimizations at the BPW91/def2-SVP level result in 18 low-lying isomers in quartet states for the anionic cluster. The results at the BPW91/def2-TZVP level show relative energies and vibrational frequencies for different spin states of 7 isomers of the anionic clusters and 6 isomers of the neutral cluster. It is found that the most stable isomers are A-VB5−/0 with non-planar pentagonal structure. The adiabatic detachment energy of the anionic cluster and the ionization energy of the neutral cluster are 1.93 and 7.36 eV.

An Energy Optimizing Routing in Wireless Senor Networks using Bee Colony Algorithm

An Energy Optimizing Routing in Wireless Senor Networks using Bee Colony Algorithm

Solvent effects on the structures of the neutral ammonia clusters

Understanding the solvation processes is subjected to the understanding of the clusters of the solvent molecule that constitute the solution. The description of proton transfer and ion diffusion processes in ammonia depends on the structures of the ammonia clusters. These structures have been well investigated in the gas phase. Although several processes take place in solution, solvent effects on the structures of molecular clusters have received less attention. In this work, we have investigated the solvent effects on the structures of neutral ammonia clusters from dimer to nonamer. Thus, we generated initial geometries using the bee colony algorithm as implemented in the ABCluster code. The generated geometries have been fully optimized at the MN15/6-31++G(d,p) and ωB97XD/6-31++G(d,p) levels of theory in the dielectric continuum medium. For the description of the dielectric continuum media we used the integral equation formalism polarized continuum model (IEF-PCM).

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.

Design for Energy Storage Springs of Universal Circuit Breakers Using Artificial Bee Colony Algorithm

To solve the imperfect springs structure parameters in the design of energy storage springs of the universal circuit breakers, and problems such as large volume of circuit breakers and low design efficiency, an approach to optimize the parameters of the energy storage springs of the circuit breakers is proposed based on the Artificial Bee Colony (ABC) algorithm. First, the mathematical optimization model of energy storage springs and the constraints of the spring parameters are derived in accordance with the working principle of energy storage springs. Then combined with cloud model and cross operation, the ABC algorithm is improved, which can adjust the cross factor, accelerate the convergence speed of ABC algorithm and improve the global search ability. And the classical test-function simulations verify that the improved ABC algorithm is superior to other evolutionary algorithms. Finally, the two different types of energy storage springs optimization models of universal circuit breakers are experimentally analyzed by use of the improved ABC algorithm, and the corresponding springs’ parameters are calculated. The experiment results show that the proposed approach is effective, which can reasonably design the parameters of energy storage springs of circuit breakers and improve the design efficiency.

Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot’s left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed.

Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

Abstract Software testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).

Artificial-Noise-Aided Energy-Efficient Secure Multibeam Wireless Communication Schemes Based on Frequency Diverse Array

In this paper, we research synthesis scheme for secure wireless communication in multibeam directional modulation (MBDM) system, which consists of multiple legitimate users (LUs) receiving their own individual confidential messages, respectively, and multiple eavesdroppers (Eves) intercepting confidential messages. We propose a new type of array antennas, termed frequency diverse arrays (FDA), to enhance security of confidential messages. Leveraging FDA technology and artificial noise (AN) technology, we aim to address the PHY security problem for MBDM by jointly optimizing the frequency offsets, the precoding matrix and the AN projection matrix. In the first stage, with known locations of Eves, precoding matrix is designed to minimize Eve’s receiving power of confidential message (Min-ERP), while satisfying power requirement of LUs. And then artificial-noise projection matrix (ANPM) is calculated to enhance AN impact on Eves without influencing LUs. Furthermore, we research a more practical scenario, where locations of Eves are unknown. Unlike the scenario of the known locations of Eves, precoding matrix is designed to maximize AN transmit power (Max-ATP), while satisfying each LU’s requirement received power of confidential message. In the second stage, we analyze and further optimize secrecy capacity. The problem is solved by optimizing frequency offsets through modified artificial bee colony (M-ABC) algorithm. Numerical results show that the proposed scheme can achieve a secure transmission in MBDM system.

Minimizing Cost of Highway Maintenance Considering the Impact of Vehicle Emissions using an Artificial Bee Colony Approach

The increasing traffic demand has reduced the efficiency of road networks and intensified the maintenance need for mobility and safety, increasing vehicle emissions, reducing air quality, and affecting climate change. To mitigate the negative impacts of work zone activities, a reliable method that can optimize spatio-temporal work zone activities is desirable. Previous studies have aimed to minimize the total cost, including maintenance, user delay, and accident costs, yet the associated environmental impact has been neglected. This study aims to optimize work zone activities using the artificial bee colony (ABC) algorithm, considering the cost of vehicle emissions in addition to the aforementioned costs for an environmentally sustainable optimization. MOtor Vehicle Emission Simulator (MOVES) is applied to calculate emission rates. The results show that the ABC algorithm is very efficient to search for the optimal solution that yields the minimum cost taking into account the well-being of the environment.

BETONARME KISA KONSOLLARIN TS 500’E GÖRE OPTİMUM TASARIMINDA ABC, TLBO VE TLABC ALGORİTMALARININ BAŞARIMI

Optimal design processes, which are an alternative to the design of reinforced concrete elements compared to traditional methods, attract the attention of researchers in parallel with the development of artificial intelligence techniques. The need for optimization in engineering problems is increasing rapidly, especially as resources are becoming scarce. In this study, the design of reinforced concrete corbels with minimum cost according to TS 500 was realized with Artificial Bee Colony (ABC), Teaching-Learning Based Optimization (TLBO) and Teaching-Learning Based Artificial Bee Colony (TLABC) algorithms. The performance of the algorithms was compared with various statistical methods that the performance of the TLABC algorithm is better than other algorithms. The parametric studies carried out have examined how the cantilever span, vertical and horizontal load changes affect the costs.

Design of a BR-ABC Algorithm-Based Fuzzy Model for Glucose Detection

This paper presents a modeling approach for defining a measured data set obtained from an optical sensing circuit based on the use of a fuzzy reasoning system. A simple but effective optical sensor is designed for in vitro determination of glucose concentrations in an aqueous solution. The measured data used in this study include analog voltages that reflect the absorbance values of three wavelengths measured in different concentrations of glucose from an RGB light-emitting diode (LED). The parameters of the fuzzy models are optimized using the bounded-range artificial bee colony (BR-ABC) algorithm to achieve the desired model performance. The results indicate that the optimized fuzzy model demonstrates high performance quality. The minimum mean square error (MSE) obtained from the singleton fuzzy model with the BR-ABC algorithm is 0.00014, which is better than the reported MSE value achieved with the Takagi–Sugeno fuzzy model.

A Hybrid Method Based on Clustering to Improve the Reliability of the Wireless Sensor Networks

The wireless sensor networks, because of its low cost and easy communications are used in many supervisory activities of various environments. As such networks have a short lifetime, in order to more usage and increasing the lifetime, researchers are looking for methods by which they can reduce energy consumption. Clustering methods and optimization algorithms such as genetic and bee colony algorithm are techniques that can increase the network lifetime. In this paper, the genetic algorithm is used to improve the clustering process of nodes in wireless sensor networks and to find an optimum route as well as improving the route of transition through nodes; the bee colony algorithm is applied. To propose the suggested algorithm, the wireless sensor network is divided into cells with variable size. Cells area is investigated in normal mode and compression and energy consumption is being evaluated in various sizes of the cell. After the simulation, it is observed that the results of the suggested method have a significant improvement in terms of energy consumption compared to other methods.

Security Enhanced Model for Cloud Data Based on Dynamic Data Fragmentation and Replication (DDFR)

Nowadays cloud computing is utilized in several IT capabilities like smart industry with IoTs, Mobile computing, etc., It is happen through outsourcing data to a third-party administrative control which is great application of cloud. But this leads to data leakage through attacks. A high level of data security is required on the data stored in cloud nodes. This paper enhances the security and data availability by dynamic fragmentation and replication process. The fragmentation is performed in runtime to create the fragments according to the available virtual machine. The replication aims to enhance the better load balance with less number of replicas. Bee colony algorithm is used for finding the best node in replication. The AES encryption approach is used for encrypting the fragments. This approach does not provides the original data if any attacks happened successfully.

An Improved Data Leak Detector for Situation Awareness Using Ant Bee Colony (ABC) Algorithm

An Improved Data Leak Detector for Situation Awareness Using Ant Bee Colony (ABC) Algorithm

OPTIMIZATION OF MICRO GAS TURBINE BY ECONOMIC, EXERGY AND ENVIRONMENT ANALYSIS USING GENETIC, BEE COLONY AND SEARCHING ALGORITHMS

In this paper it was dealt with optimization of micro gas turbine with natural gas fuel in four configurations of simple, with regenerator, combined heat and power (CHP) and CHP with regenerator. Target function included energy and exergy efficiencies and electricity cost by considering effects of environmental pollution. Optimization procedures were genetic, bee colony and searching algorithms. Results showed that optimum air fuel ratio calculated by searching method for micro gas turbine with above-mentioned cycles were 1.7, 1.3, 1.6 and 2.3 respectively. By applying Genetic algorithm, optimum air fuel ratios were 1.40, 1.21, 1.42 and 1.82 respectively. At these points, energy efficiency obtained as 29, 34.4, 39.4 and 38.2 %, second law efficiency obtained as 61.4, 74.9, 85.4 and 57.2 %, electricity cost obtained as 0.102, 0.086, 0.075 and 0.029 US$/kWh respectively. By using bee colony algorithm, optimum air fuel ratios were 1.36, 1.13, 1.32 and 1.61 respectively. At these points, energy efficiency are 28.2, 34.3, 40.5 and 35.6 %, second law efficiency are 60.9, 75.67, 81.80 and 56.65 %, electricity costs are 0.105, 0.087, 0.073 and 0.03 US$/kWh respectively. Among these methods, Genetic algorithm was selected as best method because of best answer in optimization.