Artificial Bee Colony Optimization Algorithm Research Articles

Edge detection of aerial images using artificial bee colony algorithm

Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.

Application of 5G Combined Wireless Interconnection Technology in the Reform of English Education in Colleges and Universities

Fifth-generation (5G) network is a digital cellular network communication technology that allows for more data traffic and wireless connectivity. The global demand for wireless communication networks is increasing as the number of wireless users grows and new wireless services are presented. Fifth-generation (5G) wireless networks are expected to provide higher data speeds, expanded coverage, improved cost-effectiveness, resource utilization, security, flexibility, and scalability in the future. To attain higher-order teaching abilities, include cutting-edge technology into the curriculum. Teachers should use network resources, notably 5G network communication tools, to reform English teaching since the rapid advancement of network communication technology provides a new viewpoint for English education in colleges and universities. Researchers are concentrating their efforts on maximizing the benefits of the information technology revolution. It has become a hot subject among academics to use it as an opportunity to innovate in English education. As a result, this study proposes a paradigm for revamping English instruction using 5G integrated wireless connectivity technologies. To preprocess the input data, the Butterworth filter and the Gaussian Mixture Model (GMM) are employed. To extract data features, a Gray Level Co-Occurrence Matrix (GLCM) is employed. For classification, the decision tree approach is used. The File Transport Protocol (FTP) is used for data transfer, which may be enhanced with the Revamped Artificial Bee Colony Optimization Algorithm (RABCOA). Using the MATLAB simulation tool, the proposed system is simulated and its performance is assessed. The outcome of the study’s shows that the scholars are usually unsatisfied with old style English classroom education and are more interested in the instructional innovation of 5G integrated wireless networking technologies.

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

AbstractCloud computing (CC) is vulnerable for the attacks of current information technology, because it prolongs and uses the information technology infrastructure, traditional operating system, and applications. Several security issues in detecting irregular network performances are confronted by the CC environment. With the intention of solving these security issues, dual‐channel capsule generation adversarial network optimized with red fox optimization algorithm fostered intrusion detection framework (IDS‐CC‐DCCGAN‐RFOA) is proposed in this article, for securing the privacy attacks in CC environment. Initially, the data are taken from NSL‐KDD benchmark dataset. Then the data are given to the preprocessing segment. When using the developed random forest and local least squares in preprocessing segment, the redundancy, and missing values are purged. Then the output of preprocessing is given to the feature selection stage. In feature selection, the optimal features are extracted using univariate ensemble feature selection (UEFS) technique. Based on the optimal features, the data are classified into secured data and privacy attack data based on dual‐channel capsule generative adversarial network. Here, the weight of dual‐channel capsule generative adversarial network is optimally tuned using the red fox optimization algorithm for generating an effective and optimal solution to detect the intruders. The proposed IDS‐CC‐DCCGAN‐RFOA method is implemented in MATLAB. Here, the performance metrics, like accuracy, specificity, sensitivity, F‐score, precision, false positive rate, AUC are analyzed. Then, the proposed IDS‐CC‐CCGAN‐WSOA method provides 13.9367%, 11.12%, 13.268%, and 13.739% higher accuracy and 24.54%, 29.76%, 12.87%, and 34.21% higher sensitivity compared with the existing methods, like intrusion detection framework in cloud computing using deep belief network with salp swarm algorithm (IDS‐CC‐DBN‐CSSA), intrusion detection framework in cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS‐CC‐DNN‐IGASAA), intrusion detection for cloud computing using multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS‐CC‐MLPNN‐ABC), and intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS‐CC‐RCNN‐ALO).

An Adaptive Controller Design using Duelist Optimization Algorithm for an Interconnected Power System

A Controller is generally considered as a continuous and discrete mode of execution by a huge sample period that may result in degenerated dynamic performance or system instability. Nowadays, the maximum penetration in thermal, wind, hydropower systems has decreased the power system inertia that leads to rapid frequency response and higher frequency deviation followed by contingencies and requires rapid load frequency control. The goal of load frequency control (LFC) is to achieve zero steady-state errors in frequency deviations and minimize unscheduled tie-line power flows among the interconnected areas. The study of the literature reveals that a lot of research has been carried out in this area to achieve the desired objectives using different approaches. This manuscript proposes an optimization algorithm called Duelist Optimization Algorithm (DOA) in a three area interconnected power system consisting of thermal, wind, and hydro generating systems. The proposed system introduces an adaptive PID fuzzy controller whose parameters are optimized by the DOA algorithm. The Duelist Optimization algorithm is used to optimally tune the parameters of the controller in order to keep the system frequency deviation within the threshold limit, and maintain the power balance among the control areas during load variations. The proposed method is simulated in MATLAB / Simulink environment for the estimation of its performance and then compared with some of the existing techniques such as Artificial Bee Colony (ABC) optimization algorithm, Bacteria Foraging Optimization (BFO), and Particle Swarm Optimization (PSO) algorithm. The simulation result established the suprerioty of the proposed method over the other methods.

Road Traffic Prediction and Optimal Alternate Path Selection Using HBI-LSTM and HV-ABC

Objectives: The objective of this work is to monitor and manage the traffic flow, so an Intelligent Transportation System (ITS) is developed that comprises the fundamental information of the real-time traffic flow. Methods: For reducing road Traffic Congestion (TC), this paper proffers an efficient traffic prediction framework and the optimal alternate route selection. The conversion of videos (from surveillance camera) into frames is done, and then pre-processing occurs. Then, for recognizing the traffic on the roadways, the background elimination utilizing Gaussian Mixture Model (GMM) is performed. Next, for identifying the vehicle motion, Motion Estimation (ME) utilizing the Virtual loop-based Lucas-Kanade (VLK) technique is performed. Utilizing the You Only Look Once (YOLO) technique, the frames are segmented centered on the estimated motion for identifying the type of objects on the road. Then, for classifying the traffic centered on the number of objects in the segmented frames, the H-detach optimized Bidirectional Long Short Term Memory (HBI-LSTM) is utilized. The traffic is classified by the classifier as heavy traffic, medium traffic, and low traffic. Findings: Utilizing the Horizontal Vertical cross-search appended Artificial Bee Colony (HV-ABC) optimization algorithm, the optimal alternate paths are chosen from different other routes if the traffic is heavy or medium. Novelty: The experimental outcomes demonstrate that the other top-notch models are outperformed by the proposed framework. Keywords: Traffic Congestion, Surveillance Videos, Noise Removal, Motion Estimation, Path Selection, Artificial Bee Colony (ABC) Optimization

Multiclass cyber-attack classification approach based on the Krill Herd Optimized Deep Neural Network (KH-DNN) model for WSN

The Intrusion Detection System (IDS) is a useful infiltration detection tool. With IDS, it may mechanically categorize intrusions, attacks, or breaches of security stratagems across host-level within the network and within the organization. The Krill Herd Optimized Deep Neural Network (KH-DNN) is proposed in this document as a multiclass cyberattack methodology. This model is suggested by uniting the network-based intrusion detection system (NIDS) and the host-based intrusion detection system (HIDS) to advance flexible and actual IDS to categorize and detect unexpected and unpredictable cyber attacks. In this method, two types of IDS are classified, that is, NIDS and HIDS. Classification of the system call is exploited to discriminate behavior into normal and attack categories. In this manuscript accuracy, the parameter of the deep neural network classifier is optimized with the Krill Herd optimization algorithm. Finally, the DNN model is performed in the NIDS dataset and the HIDS dataset. For the HIDS dataset, the KDD Cup 99 dataset was used, which employed two datasets: ADFA-LD and ADFA-WD, and several types of attacks were detected and classified, including Adduser, Hydra-FTP, Java-Meterpreter, Hydra-SSH, and Web-Shell. The suggested IDS-KH-DNN algorithm attains higher accuracy 42.56% and 23.4%, high precision 84.74% and 52.43%, high F-score 53.5% 64.455, high sensitivity 33.4% and 45.23%, and high specificity 31.45% and 44.23% for the NIDS dataset. The proposed system is compared to two current processes: intrusion detection using Gray Wolf optimization-based support vector machine (IDS-GWO-SVM) and intrusion detection using artificial bee colony optimization algorithm based support vector machine (IDS-ABCO-SVM). Finally, the simulation results show that the suggested approach is capable of quickly and accurately locating optimal solutions.

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization.

In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

A Decomposition and Dominance-Based Multiobjective Artificial Bee Colony Algorithm for Multiple Sequence Alignment

The multiple sequence alignment (MSA) problem is essential in biological research for finding a specific relationship between the biologic sequences and their function. This study proposes a decomposition and dominance-based multiobjective artificial bee colony optimization algorithm for MSA (MOABC/D-MSA). MOABC/D-MSA uses three kinds of searching strategies to obtain a group of nondominated solutions with high quality and diversity of an MSA problem. A decomposition-based employed bee strategy is proposed to search for high-performance solutions of the MSA, while insuring their diversity. A nondominated sorting-based onlooker strategy searches for the solutions near the Pareto front (PF) to guide the subsequent searching. The scout bee strategy facilitates the algorithm to get out of the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a benchmark for MSA algorithms. Experimental results show that the proposed algorithm has competitive performance with state-of-the-art metaheuristic algorithms. Furthermore, nondominated solutions of MOABC/D-MSA have a more uniform distribution in the objective space.

Dynamic Self-Learning Artificial Bee Colony Optimization Algorithm for Flexible Job-Shop Scheduling Problem with Job Insertion

To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is proposed. When a new job is inserted, the new job and the operations that have not started processing will be rescheduled. Finally, through solving the Brandimarte instances, it is proved that the convergence accuracy of the SLABC algorithm is higher than that of other optimization algorithms, and the effectiveness of the DSLABC algorithm is demonstrated by solving a specific example with a new job inserted.

Multi-robot cooperation and path planning for stick transporting using improved Q-learning and democratic robotics PSO

The highlight of this paper is to propose an innovative method for solving the problem of path planning and synchronization of robot pairs in a known static and complex environment. The problem addresses the stick transportation by robot pairs from the predefined starting position to the pre-assumed goal position. This paper describes the method of computing collision-free paths for multiple numbers of paired robots called twins using a hybrid algorithm with balanced local exploitation and global exploration. The hybrid algorithm is designed by combining the individual benefits of the improved Q-Learning (IQ) and democratic robotics particle swarm optimization (DRPSO). The main intention of IQ is to produce the best position with collision avoidance by executing the best state-action pair. DRPSO algorithm computes the optimal trajectory for the twin robot using the distributed democratic approach and enhances the intensification capability. The twin robots are synchronized to the number of steps, turns, the direction of movement, and distance between both of them in the proposed approach. Computer simulation results reveal the efficacy of the proposed algorithm in both paths travelled and runtime. The experimental and VREP simulation results illustrate that the proposed algorithm offers better synchronization, cooperation, and performance in terms of several parameters such as path length, path deviation, run time, and path smoothness as a replica of the number of turns made. The comparative study with artificial bee colony optimization (ABCO) and imperialist competitive firefly algorithm (ICFA) witnesses the superiority of the hybrid IQ-DRPSO algorithm. Experiments that have been conducted through Arduino bots in a real environment prove the efficiency of the proposed approach. Results obtained from simulation and experiment reveal the supremacy of the proposed algorithm for synchronization, cooperation, and path traveled.

AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids

Electricity theft (ET) is an utmost problem for power utilities because it threatens public safety, disturbs the normal working of grid infrastructure and increases revenue losses. In the literature, many machine learning (ML), deep learning (DL) and statistical based models are introduced to detect ET. However, these models do not give optimal results due to the following reasons: curse of dimensionality, class imbalance problem, inappropriate hyper-parameter tuning of ML and DL models, etc. Keeping the aforementioned concerns in view, we introduce a hybrid DL model for the efficient detection of electricity thieves in smart grids. AlexNet is utilized to handle the curse of dimensionality issue while the final classification of energy thieves and normal consumers is performed through adaptive boosting (AdaBoost). Moreover, class imbalance problem is resolved using an undersampling technique, named as near miss. Furthermore, hyper-parameters of AdaBoost and AlexNet are tuned using artificial bee colony optimization algorithm. The real smart meters’ dataset is used to assess the efficacy of the hybrid model. The substantial amount of simulations proves that the hybrid model obtains the highest classification results as compared to its counterparts. Our proposed model obtains 88%, 86%, 84%, 85%, 78% and 91% accuracy, precision, recall, F1-score, Matthew correlation coefficient and area under the curve receiver operating characteristics, respectively.

Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system (IT2-FLS) is a challenging task in the presence of uncertainty and imprecision. Grasshopper optimization algorithm (GOA) is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature, which has good convergence ability towards optima. The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS. The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. Tuning of the consequent part parameters are accomplished using extreme learning machine. The optimized IT2-FLS (GOAIT2FELM) obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices. The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm. Analysis of the performance, on the same data-sets, reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

Predicting viscosity of CO2–N2 gaseous mixtures using advanced intelligent schemes

Acquiring accurate knowledge about the viscosity of carbon dioxide, nitrogen, and their mixtures as an extremely fundamental thermo-physical property for a broad range of temperatures and pressures is crucial not only for carbon capture and utilization (CCU) or carbon capture and storage (CCS) operations but also in chemical and petroleum industries and engineering design process. The proposed study aims at developing a model to predict the viscosity of carbon dioxide and nitrogen mixtures utilizing the Boosted Regression Tree (BRT) model optimized with the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms, the Cascade Feed-Forward Neural Networks (CFNN) and Multilayer Perception (MLP), General Regression Neural Network (GRNN), and the Genetic Programming (GP) techniques. To this end, an extensive dataset consisted of 3036 data points was gathered from the open-source literature in a broad range of pressures (0.001–453.2 MPa) and temperatures (66.5–973.15 K). The consistency of the employed paradigms was assessed based on graphical and statistical error analyses. The results indicated that the developed models provide a high degree of consistency with experimental values compared to the literature correlations. Among the established intelligent models, BRT-ABC model with a correlation coefficient (R 2 ) of 0.9993 and root mean square error (RMSE) of 1.80 μPa s achieved the most accurate and reliable predictions of the gaseous mixture viscosity. Meanwhile, the GP technique was used to develop two easy-to-use correlations with regard to gas composition, temperature, and pressure with R 2 values of 0.9883 and 0.9900 at temperatures lower and higher than 300 K, respectively. • Advanced machine learning models were evolved for estimating the viscosity of CO 2 –N 2 gaseous mixtures. • An extensive databank covering 3036 data points was considered for implementing the paradigms. • BRT-ABC model achieved the most accurate results.

Fast terminal sliding mode control design for position control of induction motors using adaptive quantum neural networks

In this work, an efficient robust control scheme is proposed for position control of induction motors based on field-oriented control (FOC). The proposed control scheme consists of three interior loops. In the outer loop, the central controller based on the nonsingular fast terminal sliding mode control (NFTSM) is designed. The objective of this loop is to provide the required stator current in the q-axis for regulating the electromagnetic torque. In the inner loops, two fractional-order PI (FOPI) controllers are designed to provide the stator voltages. Besides, since induction motors have a complex mathematical model, time-varying dynamics, and non-linear structure, to deal with these challenges in the controller design, the quantum neural network (QNN) is presented with a novel framework and employed to estimate the lumped uncertainties. This makes the main contribution of the paper. In addition, to further improve system performance, the parameters of the FOPI controllers are tuned by employing the artificial bee colony (ABC) optimization algorithm. The stability of the proposed control approach is proved by Lyapunov’s stability theory. According to the results of the comparative simulation, the superiority of this approach is confirmed.

Dispatch Optimization Model for Haulage Equipment between Stopes Based on Mine Short-Term Resource Planning

The working environment of underground mines is complicated, making it difficult to construct an underground mine production plan. In response to the requirements for the preparation of a short-term production plan for underground mines, an optimization model for short-term resource planning was constructed, with the goal of maximizing the total revenue during the planning period. The artificial bee colony optimization algorithm is used to solve the model using MATLAB. According to the basic requirements of underground mine ore haulage and ore hoisting, a haulage equipment inter-stopes dispatch plan model was constructed, with the primary goal of minimizing the haulage equipment wait time. A non-dominated sorting genetic algorithm is used to solve the optimization model. An underground mine is examined using the two models, and the optimization results are compared and verified with the scheme obtained by using traditional optimization algorithms. Results show that based on the improved optimization algorithm, the use of short-term production planning schemes to guide mine production operations can increase the haulage equipment utilization rate, thereby increasing mine production revenue.

An accurate foreground moving object detection based on segmentation techniques and optimal classifier

AbstractIn video surveillance schemes, the motion object detection plays a significant role. To subtract the object background, a segmentation technique based on feature extraction is utilized in which the change in the training rate makes an alteration in the background. Thereafter, the extracted features are trained by using the self‐organizing map (SOM) network in which the weight parameters in the network is optimized with the help of artificial bee colony (ABC) optimization algorithm, so, the proposed methodology is named as HSOM‐ABC technique. This methodology is carried out to perform the classification process in this research. Initially, the whole dataset is preprocessed with the help of grayscale conversion method which converts the original image into grayscale color. After this, fuzzy c‐means clustering is applied to perform the segmentation process and this method divides the foreground and background parts efficiently. Then, feature extraction is done with the help of local binary pattern method which extract the relevant features from the segmented image. Finally, HSOM‐ABC method is proposed to accurate classification process. Hence, the moving objects are identified by categorizing the background and foreground images. MatLab platform is chosen for the proposed work simulation and the performance is evaluated by means of different parameters and it is compared with new existing approaches. Experimental outcomes show that the proposed strategy achieves higher precision value than any other existing methods.

Psoriasis Lesion Detection Using Hybrid Seeker Optimization-based Image Clustering.

In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a prerequisite for quantifying the severity of this disease. However, segmentation of psoriatic lesions cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence. An alternative method for psoriatic lesion segmentation with objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique that possesses a higher probability of global convergence for psoriasis lesion segmentation. A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms, namely Artificial Bee Colony and Seeker Optimization algorithm, has been proposed. The initial population for the hybrid technique is obtained from the two conventional local- based approaches, i.e., Fuzzy C-means and K-means clustering algorithms. The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with a Jaccard Index of 0.91 from 720 psoriasis images. The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.

Artificial Bee Colony Algorithm for Fresh Food Distribution without Quality Loss by Delivery Route Optimization

This paper enlightens the use of artificial intelligence (AI) for distribution of fresh foods by searching more viable route to keep intact the food attributes. In recent years, very hard-hitting competition is for food industries because of the individuals living standards and their responsiveness for fresh food products demand within stipulated time period. Food industry deals with the extensive kind of activities such as food processing, food packaging and distribution, and instrumentation and control. To meet market demand, customer satisfaction, and maintaining its own brand and ranking on global scale, artificial intelligence can play a vibrant role in decision-making by providing analytical solutions with adjusting available resources. Therefore, by integrating innovative technologies for fresh food distribution, potential benefits have been increased, and simultaneously risk associated with the food quality is reduced. Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. SI is a branch of nature-inspired algorithm, having a capability of global search, and gives optimized solution for real-time problems adaptive in nature. An artificial bee colony (ABC) optimization and cuckoo search (CS) algorithm also come into the category of SI algorithm. Researchers have implemented ABC algorithm and CS algorithm to optimize the distribution route for fresh food delivery in time window along with considering other factors: fixed number of delivery vehicles and fixed cost and fuel by covering all service locations. Results show that this research provides an efficient approach, i.e., artificial bee colony algorithm for fresh food distribution in time window without penalty and food quality loss.

Mining of structural motifs in proteins using artificial bee colony optimization framework for druggability.

In this work, we have developed an optimization framework for digging out common structural patterns inherent in DNA binding proteins. A novel variant of the artificial bee colony optimization algorithm is proposed to improve the exploitation process. Experiments on four benchmark objective functions for different dimensions proved the speedier convergence of the algorithm. Also, it has generated optimum features of Helix Turn Helix structural pattern based on the objective function defined with occurrence count on secondary structure. The proposed algorithm outperformed the compared methods in convergence speed and the quality of generated motif features. The motif locations obtained using the derived common pattern are compared with the results of two other motif detection tools. 92% of tested proteins have produced matching locations with the results of the compared methods. The performance of the approach was analyzed with various measures and observed higher sensitivity, specificity and area under the curve values. A novel strategy for druggability finding by docking studies, targeting the motif locations is also discussed.

Explosion risk-based water spray mitigation analysis of ultra-deep-water semi-submersible platforms

Water spray is an economical and environment-friendly alternative to mitigate gas explosion. This study conducts explosion risk analysis (ERA)-based water spray mitigation analysis of ultra-deep-water semi-submersible platform. 5000 gas explosion simulations with and without water spray are conducted by using FLACS codes. The outcome demonstrates that water spray with droplet size 700 μm and water application rate (WAR) 26 L/m2 min is most effective to mitigate explosion scenarios. The droplet size is the significant factor to stimulate the mitigation effect of water spray under middle and small exceedance frequencies, e.g., 1E-05 and 1E-06. WAR becomes dominate to awaken the water spray's negative effect under large exceedance frequency, e.g., 1E-04. A precise and efficient approach of BRANN integrated with artificial bee colony optimization algorithm is used to determine the optimal water spray configuration given the desirable exceedance frequency and the expected design accidental load that the ultra-deep-water semi-submersible platform structures could resist.