Ant Bee Colony Research Articles

A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images

A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.

A swarm intelligence labour division approach to solving complex area coverage problems of swarm robots

The complex area coverage problem is classical and widespread in the research field of swarm robots. In order to solve the complex area coverage problem with complex nonlinear boundary and special task area (forbidden area or threat area), firstly, the task area is adjusted and grid discretisation. Then, inspired by the labour division phenomenon of typical biological groups such as bee colony and ant colony, the paper analyses the performance characteristics of typical ant colony labour division model (response threshold model) and bee colony labour division model (activation-inhibition model) from the perspectives of individual and environment, individual and individual, and a new swarm intelligence labour division approach (activation-inhibition response threshold algorithm) to solve the complex area coverage problem of swarm robot. Three experiments are carried out to illustrate that the algorithm are endowed with great ability of area coverage and dynamic environment. It can respond to the sudden threat in time and make an efficient response, which has a good practical application prospects.

Optimization of Data using Artificial Bee Colony Optimization with Map Reduce

We are developing an idea through this paper which would give any question the perfect and best answer. Existing system is not capable of classifying according to different patterns. This compromises with efficiency of the system and quality of final result. In this project Parallel Clustering optimization method is formed by amalgamation of Map-Reduce with Ant Bee Colony Optimization Technique for improving the efficiency and success of the data science method. In addition, related running services on a Hadoop network are predicted with the help of Map Reduce algorithm.

Fuzzy grey wolf optimization for controlled low-voltage ride-through conditions in grid-connected wind turbine with doubly fed induction generator

With its enormous environmental and monetary benefits, the wind turbine has become an acceptable alternative to the generation of electricity by fossil fuel or nuclear power plants. Research remains focused on improving the performance of wind turbines with maximum flexibility and gains. The main objective of the paper is to simulate a low-voltage ride-through (LVRT) control system that is convenient for the development of a controller that should have the ability to rectify fault signals. This paper proposes a novel method called grey wolf optimization with fuzzified error (GWFE) model to simulate the optimized control system. Further, it compares the GWFE-based LVRT system with the standard LVRT system, systems with minimum and maximum gain, and conventional methods like genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), ant bee colony (ABC), and grey wolf optimization (GWO) algorithms. Accordingly, it analyses the simulation results regarding qualitative analysis like active power, [Formula: see text] comparison, gain, pitch degree, reactive power, rotor current, stator current, and [Formula: see text] and [Formula: see text] measurements; and quantitative analysis like RMSE computation of [Formula: see text] with varying speed. Hence, the proposed GWFE algorithm is beneficial for simulating the LVRT system compared to other conventional methods.

RETRACTED ARTICLE: Big data analytic diabetics using map reduce and classification techniques

Diabetes is more severe in women, according to various medical reports and surveys. Sometimes diabetes is difficult to identify due to various common symptoms, such as headache, fatigue, slow healing of cuts and blurry vision. Thus, this paper introduces novel big data and classification techniques such as effective map reducing technologies are used to recognize the diabetes. Initially, the data were collected from a large dataset, and the map reducing concept is applied to compose the small chunk of data efficiently. Following this process, the noise present in the collected dataset is removed using the normalization process. After that, the statistical features are selected using the ant bee colony approach that uses the ant characteristics such as wandering. The selected features are trained with the help of the support vector machine with multilayer neural network. The trained or learned features are efficiently classified using the associated neural network, and the efficiency of the system is evaluated with the help of experimental results in terms of error rate, sensitivity, specificity and accuracy.

Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm

Mobile crowdsensing is a recent model in which a group of mobile users uses their smart devices such as smartphones or wearable devices to cooperatively perform a large-scale sensing task. In this paper, a novel model will be introduced for recognizing/classifying human activities that were collected from sensor units on the chest, legs, and arms. The proposed model employed the k-Nearest Neighbor (k-NN) classifier which is one of the most common classifiers. k-NN has only one parameter, k, to determine the number of selected nearest neighbors to the test or unknown samples for predicting the class labels of the unknown samples. Searching for the value of k which has a great impact on the classification performance is difficult especially with high dimensional data. This paper employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal value of the k parameter in the k-NN Classifier. This paper shows first experimentally how the PSO in the proposed algorithm searches for the optimal value of k parameter to reduce the misclassification rate of the k-NN classifier. Then, in the second experiment, ten standard datasets are utilized to benchmark the performance of the proposed algorithm. For verification, the results of the PSO-kNN algorithm are compared with two well-known algorithms: Genetic Algorithm (GA) and Ant Bee Colony Optimization (ABCO). In the third experiment, the proposed PSO-kNN algorithm was employed for recognizing human activities. The experimental results proved that the PSO-kNN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k-NN classifier. The results also demonstrated lower error rates compared when GA and ABCO algorithms.

OPTIMIZATION AND IMPLEMENTATION OF WIRELESS SENSOR NETWORK PERFORMANCEUSING ANT BEE COLONY

Routing in wireless protocols is a tedious task. A wireless node is never stationary. It is moving from one location to other which makes traditional routing algorithms like OSPF, RIP inefficient in wireless routing. Aim is to produce a routing algorithm which works efficiently with constantly changing locations of the mobile node by implementing traditional biological background based ant bee colony technique. Traditional biological background based ant bee colony is used. A efficient ant bee colony based routing algorithm for Mobile Ad hoc Networks is obtained. Shortcomings in implementation of above algorithm are also discussed.

Dynamics modeling and advanced metaheuristics based LQG controller design for a Quad Tilt Wing UAV

This paper deals with the modelling and advanced metaheuristics-based tuning of a Linear Quadratic Gaussian (LQG) controller for a particular class of convertible Unmanned Aerial Vehicles (UAVs), called Quad Tilt-Wing (QTW). A dynamical model for the vertical, transition and horizontal flights is firstly derived using the Newton–Euler formalism. Since the main design parameters in the LQG control approach are the weighting matrices which are usually selected by classical and hard trials-errors based procedures, an optimization problem under operating constraints is formulated for systematically tuning these decision variables. Such an optimization-based LQG control problem is systematically solved using advanced and recent metaheuristics such as the Harmonic Search Algorithm, Water Cycle Algorithm (WCA) and Fractional Particle Swarm Optimization-based Memetic Algorithm (FPSOMA). To assess the performance of the proposed algorithms and conclude about their control parameters choices, an empirical comparison study is performed for solving different test functions from the single-objective optimization literature. The classical Particle Swarm Optimization and Ant Bee Colony algorithms are retained as comparison tools. Value- and Friedmans rank-based statistical analyses are presented. After that, experiments are conducted to achieve optimal coefficients of weighting matrices of LQG controllers in solving QTW position and heading stabilization problem. Demonstrative simulations show that the WCA, outperforming each one of other metaheuristics, provides the best results in terms of robustness and performance for the problem in hand. Results verify the efficiency the proposed metaheuristics-tuned LQG control structures.

Evaluating Prediction of Customer Churn Behavior Based On Artificial Bee Colony Algorithm

As in the competitive environment, it becomes necessary to focus on retaining churn customers as well as attracting new customers. Various algorithms of Data Mining have been used for making distinguish between customers into loyal and churn. This paper represents the ant bee colony algorithm which has been used to get accurate results. The use of ABC with two best values local best and global best makes it more effective to obtain the more pleasing and comfortable results. PSO is used to search the best solution with two best values named pbst and gbst by using iteration with initial velocity and positions.

Comparison of Three Evolutionary Algorithms: PSOA, ACOA and BCOA on Recognition Arabic Characters Problem

Intelligence techniques such as Particle swarm optimization, Genetic algorithm, Ant colony optimization , Bee Colony Optimization can apply on the system as classification method for better result This paper we survey three techniques Ant Colony Optimization Algorithm (ACOA), Particle Swarm Optimization Algorithm (PSOA), and Bee Colony Optimization Algorithm (BCOA),their algorithm and reason to use. In recent years, the area of Evolutionary Computation has come into these three. Three of the popular developed approaches are Particle Swarm Optimization Algorithm (PSOA), Ant Colony Optimization Algorithm (ACOA) and Bee Colony Optimization Algorithm (BCOA), are used in optimization problems. Since the three approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their implementation. The problem area chosen is that recognition of Final forms of Arabic handwritten characters.

A Review of Training Methods of ANFIS for Applications in Business and Economics

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Moreover, the standard gradient based learning via two pass learning algorithm is prone slow and prone to get stuck in local minima. Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. Mostly Particle Swarm Optimization (PSO) and its variants have been applied for training approaches used. Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance.

Solution to Economic Power Dispatch Planning Problem considering Generator Constraints using Artificial Bee Colony Algorithm

Optimum active and reactive power dispatch is an inherent part of power system generation planning and it is the need of an hour for the electrical utilities and power engineers to dig this area in short and long term planning scenarios. Load demand requirements subjected to economic feasible solutions matching voltage profile, power demand, minimization of losses, voltage stability and improve the capacity of the system is the need of the hour. Optimization techniques based on evolutionary computing, artificial intelligence, search method finds their applications in the area of economic load dispatch planning to reach global optimal solution for this multi-decision, multi-objective combinatorial problem subjected to different constraints. In this paper, ant bee colony based algorithm has been proposed to solve economic dispatch problem. Unlike other heuristic algorithms, ABC utilizes search space as multi-step decision process. It possesses a flexible and well-balanced operator to enhance and adapt the global and fine tune local search to follow the minimum cost path in the search boundary. The suggested technique is tested on IEEE 25 bus system. Test results are compared with other techniques presented in literature.

A Trusted Real-Time Scheduling Model for Wireless Sensor Networks

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods.

Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

Dual Population Genetic Algorithm is an effective optimization algorithm that provides additional diversity to the main population. It addresses the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. Thus it restricts their individuals to be trapped in the local optima. This paper proposes Dual Population Genetic Algorithm for solving Constrained Optimization Problems. A novel method based on maximum constrains satisfaction is applied as constrains handling technique and Dual Population Genetic Algorithm is used as meta-heuristic. This method is verified against 9 problems from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2006 Special Session on Constrained Real-Parameter Optimization problem set. The results are compared with existing algorithms such as Ant Bee Colony Algorithm, Differential Evolution Algorithm and Genetic Algorithm that have been used for solving same problem set. Analysis shows that this technique gives results close to optimum value but fails to obtain exact optimum solution. In future Dual Population Genetic Algorithm can produce more efficient solutions using alternative constrains handling technique.

Multi Objective Optimization Problem resolution based on Hybrid Ant-Bee Colony for Text Independent Speaker Verification

Today major section of automatic speaker verification (ASV) research is focused on multiple objectives like optimization of feature subset and minimization of Equal Error Rate (EER). As such, numerous systems for feature dimension reduction are proposed. This includes framework coaching and testing analysis for every feature set that could be a time esurient trip. Because of its significance, the issue of feature selection has been researched by numerous scientists. In this paper, a new feature subset selection procedure is presented. Hybrid of Ant Colony and Artificial Bee Colony optimized the feature subset over 85% thereby decreased the computational complexity of ASV. Additionally an external record is maintained to store non-dominated solution vectors for which concept of Pareto dominance is used. An overall optimization of 87% is achieved thereby improved the recognition rate of ASV.

Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification

The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and Global Artificial Bee Colony Algorithm train Multilayer Perceptron (MLP). This paper investigates the new hybrid technique called Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) algorithm. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome GABC-LM algorithm used in this work to train MLP for the boolean function classification task, the performance of GABC-LM is benchmarked against MLP training with the typical LM, PSO, ABC and GABC. The experimental result shows that GABC-LM performs better than that standard BP, ABC, PSO and GABC for the classification task.

G-HABC Algorithm for Training Artificial Neural Networks

Learning problems for Neural Network (NN) has widely been explored in the past two decades. Researchers have focused more on population-based algorithms because of its natural behavior processing. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) algorithm produced an easy way for NN training. These social based techniques are mostly used for finding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has difficulties such as trapping in local minima and slow convergence. The new method named Global Hybrid Ant Bee Colony (G-HABC) algorithm which can overcome the gaps in BP is used to train the NN for Boolean Function classification task. The simulation results of the NN when trained with the proposed hybrid method were compared with that of Levenberg-Marquardt (LM) and ordinary ABC. From the results, the proposed G-HABC algorithm has shown to provide a better learning performance for NNs with reduced CPU time and higher success rates.

Multiple Ant-Bee Colony Optimization for Load Balancing in packet-switched networks

One of the important issues in computer networks is “Load Balancing” which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint these colony members as intelligent agents to monitor the network and update the routing information. The survey includes comparison and critiques of MACO. The simulation results show a tangible improvement in the aforementioned approach.