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The Ant Colony Optimization (ACO) technique was inspired by the ants' behaviour throughout their exploration for food. The use of this technique has been very successful for several problems. Besides, Data Mining (DM) has emerged as an important technology with numerous practical applications, due to the wide availability of a vast amount of data. The collaborative use of ACO and DM is very promising...
This paper presents an intra-modal fusion environment to integrate multiple raw palm images at low level. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characteristics, fused image is convolved with Gabor wavelet transform. The Gabor wavelet feature representation reflects very high dimensional space. To reduce the high dimensionality, ant colony...
A new text classification algorithm which is based on Ant Colony Algorithm is proposed in this paper. It makes use of the advantage in solving discrete problems by ACO and discreteness of text documents' features. Texts are classified by crawling of class population ants which have class information with them to find an optimal path matching it during iteration in the algorithm. It can get a satisfactory...
Ant colony optimization (ACO) has been used successfully in data mining field to extract rule based classification systems. The Objective of this paper is to utilize ACO to extract a set of rules for diagnosis of diabetes disease. Since the new presented algorithm uses ACO to extract fuzzy If-Then rules for diagnosis of diabetes disease, we call it FADD. We have evaluated our new classification system...
In this study, a naturally inspired optimization algorithm, Ant Colony Optimization for Continuous Domains (ACOR), is used to classify six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). A radial basis function neural...
The objective of this paper is to propose a new system for fault diagnosis of train bearings using PCA and ACO. On the base of the analysis of time and frequency domain statistical features extracted from the vibration signals collected from the bearings, twenty features which were the most sensitive to different working states were chosen as the object of follow-on process. After zero-average and...
This article established artificial neural networks based on improved ant colony optimization evaluation model for residential performance. Firstly, on the basis of comprehensive analysis of the effects factors of residential building's performance, considering of the advantages of dealing with non-linear object of neural network, the neural network is trained by the sample data. While training neural...
This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare...
A novel ant colony optimization (ACO) algorithm takes inspiration from the coordinated behavior of ant swarms finding the shortest way from their nests and the food source, which has been applied on many research areas for solving optimization problems, but it has seldom been used in remote sensing data processing. ACO algorithm has many potential advantages in remote sensing data processing, such...
We used the ant colony pheromones in the intelligent learning. Proposed an intelligence network based on ant colony pheromones and TSP problem. The ant colony pheromones was in hierarchical distribution in the network, Recording of the learning and related information by the residual pheromones, so realized intelligent decision. We use the path choosing in the ant colony algorithm as decision of the...
In this paper, ACOT was provided to advise for employees in selecting the training courses (TCs). The courses planning process needs to abide the company's policies regarding training hours and costs. In order to avoid the courses of an employee are confined to only one or two areas, we stipulate that the selected TCs cover three or more areas. This paper uses ant colony optimization (ACO) algorithm...
This paper introduces a novel method for human face recognition that employs a new back propagation neural network (BPN) training algorithm performed with an ant colony optimization (ACO) to get the optimal connection weights of the BPN of the classification phase. The aim is to automate the face recognition system using computational intelligence. The input image undergoes histogram equalization...
To enhance its global optimization speed, the basic ant colony optimization (ACO) is modified, then it is used to optimize the neural networks (NN), and the optimized NN is applied to the direct torque control (DTC) system, so that the rotate speed can be observed. The DTC with speed sensorless is implemented at last. The research of simulation shows that, the modified ACO has eminent global optimization...
A novel exploration-exploitation strategy for reinforcement learning (RL) based an adaptive ant colony system is proposed in this paper, which called AACO-RL. The elitist strategy ant system (ASelitist), developing from ant system, presented by M. Dorigo, improved efficiency through imposing additional pheromone on the paths of the global optimal solution. But as the amount of elitist ant is produced...
In this paper, we propose a swarm intelligence based reinforcement learning (SWIRL) method to train artificial neural networks (ANN). Basically, two swarm intelligence based algorithms are combined together to train the ANN models. Ant Colony Optimization (ACO) is applied to select ANN topology, while Particle Swarm Optimization (PSO) is applied to adjust ANN connection weights. To evaluate the performance...
Path planning for small unmanned air vehicles (UAVs) becomes a difficult problem when accounting for wind. Wind can affect the path quality in a nonlinear manner requiring extended segment lengths for accurate following. A method is presented to find near-optimal paths through stochastic optimization based on a training set. In general the method applies to quickly find a near-optimal solution of...
The marine diesel engine is a complex system, which has the important function to guarantee the marine security. In this paper a novel approach of optimizing and training fuzzy neural network based on the ant colony algorithm is proposed for the intelligent fault diagnosis of this kind of diesel engine. The structure and the parameter of fuzzy neural network for fault diagnosis system are introduced...
Any formation change of swarms in the natural environment is one of basic problems of coordination. A new transformation scheme for a man-made swarm formation is proposed, in this paper, by using the algorithms of affine transformation with respect to generalized ant colony optimization (GACO). The affine transformation algorithm can pre-determine target positions for each member of the swarm, while...
Introducing the rank-weight method into the basic ant colony optimization (ACO), we use the modified ACO to optimize the weights and thresholds value of neural networks (NN). And when the BPNN is being trained, this method can solve the disadvantages of running into the minimum easily, and enhance the convergence speed. So we get a heuristic method, which is good at time efficiency and derivation...
This paper discusses the application of a multi-layer perceptron network to estimate direction of arrival (DOA) using ant colony optimization (ACO) for training. ACO simulates the foraging behavior of ant colonies which manage to find the shortest path from nest to feeding source. This technique was originally developed for discrete optimization problems, but recent research efforts has led to some...
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