Ant Bee Colony Research Articles

Optimization of Distributed Energy Resources Operation in Green Buildings Environment.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.

Optimizing solar panel maximum power point tracking and parasitic parameter extraction in partial shading with Enhanced Slime Mold optimization

Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find near-optimal power points by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). The proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5 S–5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABSO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to the above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.

Enhanced LA-HBO technique for optimal position of PMU with complete prominence

This manuscript proposes a hybrid technique for determining the optimal positioning of phasor measurement units (PMUs) in power systems. The PMUs play a crucial role in power system control, wide-area monitoring, and protection. The proposed hybrid method is the joint execution of the Lichtenberg algorithm (LA) and the heap-based optimization (HBO) technique. Hence, it is named the LA-HBO technique. The objective of the proposed method is to place the PMUs in the power system for observability. The goal is to enhance the efficiency and accuracy of PMU placement, ensuring optimal positioning for improved grid monitoring capabilities. The Lichtenberg Algorithm (LA) enhances PMU placement by addressing system observability challenges and ensuring that selected locations provide comprehensive coverage of the power grid. The heap-based approach optimizes PMU placement by efficiently managing the selection process, considering factors like computational efficiency and scalability. The proposed hybrid technique is implemented in IEEE-30 and -14 bus systems. The MATLAB-based simulation results are compared with the various existing methods, such as Sea Lion Optimization (SLO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC). By then, the outcome reveals the efficacy of the proposed method for defining the optimum PMU locations. The proposed method shows a low computational time of 0.02348 sec for the IEEE-14 bus, and 0.03565 sec for the IEEE-30 bus compared with other existing methods.

Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms.

Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis

Many research papers and much legislation has been published in recent years to control or reduce factory pollution. However, only a few articles have discussed pollution from manufacturing facilities, specifically shop floors, even though this a specific single objective problem. In this research framework, a new variant technique of the jelly fish concept adaptive salp swarm optimization (ASSO) with a familiar Lagrangian relaxation model for lowering Total Material Handling Costs (TMHC) and carbon dioxide (CO2) emissions is presented. Using the Mat Lab software and the improved ASSO, the dragon fly optimization (DFO) algorithm technique, experimental simulations of the existing and recognized design of the studied industry were performed. The simulation results were validated and compared to those of other optimization techniques such as ant bee colony (ABC), simulated annealing (SA), and genetic algorithm (GA). It was determined that the proposed methodology, ASSO, was the most efficient, resulting in 40 % reduction compared to ABC, 38 % DFO, 50 % SA, and 40 % GA in the lowest TMHC, as well as an average 20 % reduction of emission rate in green layout design. These techniques could be combined into a hybrid format for further reduction of the emission rate up to 80 %.

A novel chaotic flower pollination algorithm for function optimization and constrained optimal power flow considering renewable energy sources

This study presents an improved chaotic flower pollination algorithm (CFPA) with a view to handle the optimal power flow (OPF) problem integrating a hybrid wind and solar power and generate the optimal settings of generator power, bus voltages, shunt reactive power, and tap setting transformers. In spite of the benefits of FPA, it encounters two problems like other evolutionary algorithms: entrapment in local optima and slow convergence speed. Thus, to deal with these drawbacks and enhance the FPA searching accuracy, a hybrid optimization approach CFPA which combines the stochastic algorithm FPA that simulates the flowering plants process with the chaos methodology is applied in this manuscript. Therefore, owing to the various nonlinear constraints in OPF issue, a constraint handling technique named superiority of feasible solutions (SF) is embedded into CFPA. To confirm the performance of the chaotic FPA, a set of different well-known benchmark functions were employed for ten diverse chaotic maps, and then the best map is tested on IEEE 30-bus and IEEE 57-bus test systems incorporating the renewable energy sources (RESs). The obtained results are analyzed statistically using non-parametric Wilcoxon rank-sum test in view of evaluating their significance compared to the outcomes of the state-of-the-art meta-heuristic algorithms such as ant bee colony (ABC), grasshopper optimization algorithm (GOA), and dragonfly algorithm (DA). From this study, it may be established that the suggested CFPA algorithm outperforms its meta-heuristic competitors in most benchmark test cases. Additionally, the experimental results regarding the OPF problem demonstrate that the integration of RESs decreases the total cost by 12.77% and 33.11% for the two systems, respectively. Thus, combining FPA with chaotic sequences is able to accelerate the convergence and provide better accuracy to find optimal solutions. Furthermore, CFPA (especially with the Sinusoidal map) is challenging in solving complex real-world problems.

Topology based energy efficient routing using integration of fuzzy based markov chain cluster‐optimized novel ant bee colony approach in FANET

SummaryFlying adhoc network (FANET) is a rapidly flourishing technology in recent years which has an excellent scope of growth in the to the improvised mobility and the frequency changing topology FANETs are considered as a challenging technology to design an optimal network model. This problem is fixed with the topology based routing technique. Hence a novel hybrid clustering algorithm where fuzzy based Markov chain cluster (FMC2) is integrated with the optimized novel ant bee colony (ONABC) optimization algorithm (IoFMC2‐ONABC). This algorithm demonstrates an improvised efficiency with the novel ant bee colony algorithm. The proposed model simulation is done in an NS‐2 simulator and results are compared against the existing conventional approaches like adhoc on‐demand distance vector, efficient intelligent packet broadcasting protocol, energy‐efficient routing in flying ad hoc networks, and energy‐efficient gray wolf. This method aims at achieving improvised connectivity, optimizing the power consumption thereby increasing the residual energy augmenting the performance efficiency of the network through selection of the proper cluster center during each transmission. Thus the energy consumption of the node was optimized to 7.6 J with the highest packet delivery ratio of about 85.15 ensuring minimal packet losses during huge data transmission.

Memetic swarm clustering with deep belief network model for e‐learning recommendation system to improve learning performance

AbstractIn this article, an efficient clustering and classification model using memetic swarm clustering and deep belief network called MSC‐DBN model, to derive a recommendation system for web‐based learning. In the proposed method, the three main phases are considered: (i) clustering, (ii) classification, and (iii) recommendation. In first phase, MSC uses ant bee colony, Particle swarm optimization and k‐MEANS clustering. In the second phase, DBN is used for classification in the clustered based users. Clustered based user's has three categories of learners. Finally, MSC‐DBN system is automatically recommends the learning materials to the learners based on the complexity level of the material and learner's capability. A detailed experimental analysis is conducted to ensure the effective recommendation performance. The proposed method is activated in Java. The proposed method attains higher precision 93.26%, 96.12%, and 91.11%, higher recall 89.56%, 92.36%, and 95.66% and higher F‐score 93.12%, 97.14%, and 92.11% compared with the existing approaches, like generalized sequential pattern (GSP) model with context aware (CA)‐collaborative filtering (CF) called (GSP‐CA‐CF), context aware (CA)‐collaborative filtering (CF) called (CF‐CA), and generalized sequential pattern (GSP). Finally, the simulation outcomes demonstrate that the proposed method can be able to find the optimal solutions efficiently and accurately.

Advanced pest detection strategy using hybrid optimization tuned deep convolutional neural network

PurposeAgriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.Design/methodology/approachThe paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.FindingsThe analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.Originality/valueThe proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Optimal Solution of Reactive Power Dispatch in Transmission System to Minimize Power Losses Using Sine-Cosine Algorithm

Optimal reactive power dispatch (ORPD) has a crucial impact to enhance safety, reliability, and economical operation of the electric power system. ORPD is a non-linear, non-convex and mixed variable problem, which has been solved by many researchers via different meta-heuristic algorithms during the last decade. In this work, a novel algorithm named sine-cosine algorithm (SCA) is utilized to solve ORPD problem by considering both dependent and independent control variable constraints. SCA has been tested and validated on standard 14, 30 and 57-bus power systems. To validate the superiority of proposed algorithm, the outcomes obtained through SCA are compared with recent published results attained through particle swarm optimization (PSO), modified Gaussian barebones teaching–learning based optimization (BBTLBO), ant bee colony optimization (ABCO), whale optimization algorithm (WOA) and backtracking search algorithms (BSA). The results attained using SCA show the improvement in the power losses minimization. Thus, with standard 14-bus system, the power losses are minimized from 0.04% to 4.78%. While, using standard 30-bus, the power losses are minimized from 0.4% to 3.4% and with standard 57-bus, power losses are reduced from 0.9% to 1.99%. Furthermore, a comparative analysis with 30 independent runs on the above-mentioned bus systems is performed to examine the functioning of the proposed method in terms of probability density function (PDF) and cumulative density function (CDF). For such analysis, well-known meta-heuristic algorithms such as PSO, WOA, differential evolution (DE) are compared with proposed SCA in solving the ORPD problem. The results of this analysis clearly show that proposed algorithm is robust, effective, and computationally easy in solving the ORPD problem compared to the existing meta-heuristic algorithms.

Feedback deer hunting optimization algorithm for intrusion detection in cloud based deep residual network

Cloud computing is the distributed computing paradigm continually exposed to different attacks and threats of various origins. The data stored in the cloud framework is easier for external and internal intruders, as access to the cloud framework is done through internet services. Various intrusion detection (ID) methods are developed to detect network intruders in the cloud, but these methods are not primarily effective in generating accurate detection results. Hence, an effective intrusion detection system (IDS) is designed to solve the security issues that unfavorably influence the sustainable development of the cloud and enhance the protection of the cloud from malicious attacks. The IDS is modeled using the proposed Feedback Deer Hunting Optimization (FDHO)-based Deep Residual network to detect network intrusions. However, the proposed FDHO algorithm is designed by integrating Feedback Artificial Tree (FAT) with Deer Hunting Optimization (DHOA), respectively. Moreover, the detection of malicious attacks is carried out using a Deep Residual network that significantly increases the training speed, reduces the computational complexity, and generates effective detection results. The performance of the proposed method is comparatively analyzed with the existing techniques, such as Stacked Contractive Auto-Encoder and Support Vector Machine (SCAE+SVM), Artificial Neural Network with ant bee colony optimization algorithm+fuzzy clustering (ANN+ABC+fuzzy clustering), Improved dynamic immune algorithm (IDIA), and Normalized K-means (NK) clustering algorithm with RNN named, (NK-RNN), FAT-based Deep Residual network, and DHOA-based Deep Residual network using the BoT-IoT dataset and KDD cup-99 dataset. The proposed method achieved outstanding performance by considering the metrics, like specificity, accuracy, and sensitivity, with the values of 0.9526, 0.9498, and 0.9214 using the BoT-IoT dataset.

PAPR Reduction for FBMC Systems with Ant Bee Colony Optimization

Multi carrier systems are most prevalent modern wireless communication systems which are employed for high data rate transmission. At present, FBMC (Filter bank multi carrier) with OQAM (Offset-QAM) has drawing attention for many researchers to implement new physical layers and wireless protocols. As in the case of multi carrier system, this FBMC also suffers from high PAPR, so this paper presents a PAPR reduction approach with ant bee colony (ABC) optimization. ABC algorithm functionality is similar to honey mining where the labor is shared for nectar source and hence find out the optimal solution. Experimental results shows that the proposed approach could able to attain considerable improvement in decreasing the PAPR whencompared against the conventional FBMC and SLM (selective mapping method) based FBMC approaches.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm (SFLA) based tuning approach of an Integral Sliding Mode Controller (ISMC) for a quadrotor type of Unmanned Aerial Vehicles (UAV). Based on the Newton–Euler formalism, a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes. Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law, which are usually selected by repetitive and time-consuming trials-errors based procedures, a constrained optimization problem is formulated for the systematically tuning of these unknown variables. Under time-domain operating constraints, such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation- and swarm intelligence-based algorithms such as the Crow Search Algorithm (CSA), Fractional Particle Swarm Optimization Memetic Algorithm (FPSOMA), Ant Bee Colony (ABC) and Harmony Search Algorithm (HSA). Numerical experiments are carried out for various sets of algorithms’ parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization. Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence. Demonstrative results verified that the proposed metaheuristics-based approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox

Wind turbines (WTs) are often operated in harsh and remote environments, thus making them more prone to faults and costly repairs. Additionally, the recent surge in wind farm installations have resulted in a dramatic increase in wind turbine data. Intelligent condition monitoring and fault warning systems are crucial to improving the efficiency and operation of wind farms and reducing maintenance costs. Gearbox is the major component that leads to turbine downtime. Its failures are mainly caused by the gearbox bearings. Devising condition monitoring approaches for the gearbox bearings is an effective predictive maintenance measure that can reduce downtime and cut maintenance cost. In this paper, we propose a hybrid intelligent condition monitoring and fault warning system for wind turbine's gearbox. The proposed framework encompasses the following: a) clustering filter- (based on power, rotor speed, blade pitch angle, and wind speed signals)-using the automatic clustering model and ant bee colony optimization algorithm (ABC), b) prediction of gearbox bearing temperature and lubrication oil temperature signals- using variational mode decomposition (VMD), group method of data handling (GMDH) network, and multi-verse optimization (MVO) algorithm, and c) anomaly detection based on the Mahalanobis distances and wavelet transform denoising approach. The proposed condition monitoring system was evaluated using 10 min average SCADA datasets of two 2 MW on-shore wind turbines located in the south of Sweden. The results showed that this strategy can diagnose potential anomalies prior to failure and inhibit reporting alarms in healthy operations.

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.

Enhancement of low voltage ride-through ability of the photovoltaic array aided by the MPPT algorithm connected with wind turbine

PurposeBy means of the massive environmental and financial reimbursements, wind turbine (WT) has turned out to be a satisfactory substitute for the production of electricity by nuclear or fossil power plants. Numerous research studies are nowadays concerning the scheme to develop the performance of the WT into a doubly fed induction generator-low voltage ride-through (DFIG-LVRT) system, with utmost gain and flexibility. To overcome the nonlinear characteristics of WT, a photovoltaic (PV) array is included along with the WT to enhance the system’s performance.Design/methodology/approachThis paper intends to simulate the control system (CS) for the DFIG-LVRT system with PV array operated by the MPPT algorithm and the WT that plays a major role in the simulation of controllers to rectify the error signals. This paper implements a novel method called self-adaptive whale with fuzzified error (SWFE) design to simulate the optimized CS. In addition, it distinguishes the SWFE-based LVRT system with standard LVRT system and the system with minimum and maximum constant gain.FindingsThrough the performance analysis, the value of gain with respect to the number of iterations, it was noted that at 20th iteration, the implemented method was 45.23% better than genetic algorithm (GA), 50% better than particle swarm optimization (PSO), 2.3% better than ant bee colony (ABC) and 28.5% better than gray wolf optimization (GWO) techniques. The investigational analysis has authenticated that the implemented SWFE-dependent CS was effectual for DFIG-LVRT, when distinguished with the aforementioned techniques.Originality/valueThis paper presents a technique for simulating the CS for DFIG-LVRT system using the SWFE algorithm. This is the first work that utilizes SWFE-based optimization for simulating the CS for the DFIG-LVRT system with PV array and WT.

Traditional and Soft Computing Techniques for Image Enhancement

Now-a-days, there is a growing demand for image processing. The target of image enhancement is to find details present in images having low luminance for better image quality. Enhancement is required to improve the picture quality. In this process, we can enhance an image, by applying the suitable technique. In enhancement, there is a conversion in image contrast, quality, color vision, brightness, clarity etc. So we need image enhancement. A comparative survey is carried out in this paper, explaining traditional and soft computing techniques. This paper clarifies a study of traditional such as edge detection of an image and fuzzy logic based soft computing for improvement of an image. In the result section output of image is shown as edges using traditional as well as fuzzy. A small description is also study for picture improvement using different soft computing and optimization techniques such as Neural network, Convolution Neural Network, Ant Bee Colony, Particle Swarm Optimization etc. in literature survey and in comparative table. It is concluded that Image enhancement can be done by traditional method, soft computing and optimization method. Image enhancement has found various vision applications that have the ability to enhance the visibility of images. To enhance an image it is very important that image should be clear, so before using the enhancement techniques we should need to learn about the enhancement. So this paper described a survey of image enhancement with different techniques. In future scope of this paper we can find out different type of parameters like PSNR, MSE and execution time, also we can use optimization technique. We are also showing a comparison table of image enhancement based on traditional, soft computing and optimization techniques with its open scope.

Simulation and Energy Management in Smart Environment Using Ensemble of GA and PSO

Smart, intelligent and sustainable power consumption model in residential sector received attraction of the researchers in last couple of years. Numerous techniques have been implemented for green and smart power management but the problem of minimum power consumption without compromising user comfort in green buildings is a big challenge to the researchers. In the past, we have presented power consumption optimization models for green buildings which are constructed on principles of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Up-to some extent, the previous intelligent models accomplished better output results, but the results shows that there is space to improve the results furthermore. In this paper, we propose an advanced energy optimization and energy control model based on multiprocessing, ensemble of PSO and GA named Advanced Energy Optimization (AEO) to provide better occupants comfort index and efficient power utilization of the energy sources. The focus of the proposed AEO model is to maximize occupant’s indoor comfort and minimize power consumption. The paper also emphases on the application of a simulator to control the actuators and update the indoor environment. The proposed AEO intelligent building model delivers power efficient green environment by minimizing power utilization and enhancing occupant’s comfort as opposed to GA based power consumption model (GAP). The proposed AEO model also provides better comfort index as compared to GAP, Single Optimization with Hybrid Prediction (SOHP), PSO and Ant Bee Colony with Knowledge Base (ABCKB) models. The results shows the usefulness of the proposed AEO model in reducing consumed power and improving the user’s comfort as compared to existing models. The model also control the building actuators based on the control information’s provided by the model.

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

Preterm Birth Prediction using Hybrid Ant Colony-Genetic Optimization Algorithm in Data Mining

Many data mining (DM) methods are used to explore the risk factors of Preterm birth (PTB) and to predict preterm birth. High rates of infant mortality, preterm births and maternal mobility and continuous variation in pregnancy outcomes are an important public health issue in India and worldwide. In this paper, aims to develop and evaluate prevent factors of preterm birth using hybrid algorithm which is optimized the model with genetic algorithm and ant bee colony algorithm based also analysis of risk factor of preterm birth prediction. It is identified that variables which were highly influenced to forecast less weight child birth are Mother’s weight (pounds) before pregnant, age of Mother, during first three months the number of physician meet, number of early premature labors. The results of this work have improved prediction accuracy when compared with other optimization techniques. Maximum accuracy of 0.9629 is produced in proposed method.