Ant Colony Framework Research Articles

A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework

The optimal feature selection problem in classification of power quality disturbances is concerned with the elimination of redundant features. Its primary objective is to improve the classification accuracy and computational time. Limited literature on this topic and possible inefficiency in the existing works, makes it imperative to explore the new feature selection techniques. This paper, thus presents an ant colony optimization model for the same. A new multiobjective feature selection scheme is proposed which minimizes the product of feature set size and classification error. A feature set is declared feasible, if and only if it offers lesser error and features than other sets. The proposed scheme is compared against two other schemes from literature in same optimization framework. S-transform and time–time transform are employed for detection and feature extraction. This combination has a proven ability of offering precise time–frequency details and a wide range of features for optimal selection problem. Three classifiers, namely decision tree, k-nearest neighbour, and support vector machine are used to check the generality of proposed methodology. Disturbances simulated as per IEEE-1159 are used for initial testing, which confirm that the previous schemes may render accuracies even worse than those obtained without optimal feature selection. Conversely, the proposed scheme ensures a remarkable accuracy even when validated with real disturbances from an experimental database. A performance comparison with some recent works is also shown.

An Ant–Based Filtering Random–Finite–Set Approach to Simultaneous Localization and Mapping

Abstract Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process. Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory. Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency with the results obtained from numerical simulations.

Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network

Due to the dynamic nature of the Mobile Ad-hoc Network (MANET), routing in MANET becomes challenging especially when certain QoS requirements (like high data packet delivery ratio, low end to end delay, low routing overhead, and low energy consumption) are to be satisfied. Though a number of routing protocols have been proposed aiming to fulfill some of these QoS requirements but none of them can support all these requirements at the same time. In this paper, we propose an enhanced version of the well-known Dynamic Source Routing (DSR) scheme based on the Ant Colony Optimization (ACO) algorithm, which can produce a high data packet delivery ratio in low end to end delay with low routing overhead and low energy consumption. In our scheme, when a node needs to send a packet to another node, like DSR, it first checks the cache for existing routes. When no routes are known, the sender node locally broadcasts the Route Request control packets (called the Req.Ant packets) to find out the routes. This is similar to the biological ants initially spreading out in all directions from their colony in search of food. Now, the ants, after finding the food source, come back to the colony and deposit pheromone on their way so that other ants get informed about the paths. Similarly, in our routing scheme, the Req.Ant packets propagate through the network according to our novel route discovery scheme and gathers information of the route (i.e. total length of the route, congestion along the route and end to end path reliability of the route), till it reaches the destination node. When the destination node receives a Req.Ant packet, it sends back Rep.Ant (Route Reply control packet) which consists the route information of the corresponding Req.Ant to the source node through the same route. On receiving such Rep.Ant packets from different routes, the source node comes to know about those routes. Under the ant colony framework, the best route is selected by the pheromone level of the route. Similarly, here we calculate the pheromone level of a route based on the number of hops in the route, the congestion along the route and end to end path reliability of the route.The route with the highest pheromone count will be selected for data packet delivery. We also propose a novel pheromone decay technique for route maintenance. The simulation results show that our ACO based Enhanced DSR (E-Ant-DSR) outperforms the original DSR and other ACO based routing algorithms.

Multi-view 3D scene reconstruction using ant colony optimization techniques

This paper presents a new method performing high-quality 3D object reconstruction of complex shapes derived from multiple, calibrated photographs of the same scene. The novelty of this research is found in two basic elements, namely: (i) a novel voxel dissimilarity measure, which accommodates the elimination of the lighting variations of the models and (ii) the use of an ant colony approach for further refinement of the final 3D models. The proposed reconstruction procedure employs a volumetric method based on a novel projection test for the production of a visual hull. While the presented algorithm shares certain aspects with the space carving algorithm, it is, nevertheless, first enhanced with the lightness compensating image comparison method, and then refined using ant colony optimization. The algorithm is fast, computationally simple and results in accurate representations of the input scenes. In addition, compared to previous publications, the particular nature of the proposed algorithm allows accurate 3D volumetric measurements under demanding lighting environmental conditions, due to the fact that it can cope with uneven light scenes, resulting from the characteristics of the voxel dissimilarity measure applied. Besides, the intelligent behavior of the ant colony framework provides the opportunity to formulate the process as a combinatorial optimization problem, which can then be solved by means of a colony of cooperating artificial ants, resulting in very promising results. The method is validated with several real datasets, along with qualitative comparisons with other state-of-the-art 3D reconstruction techniques, following the Middlebury benchmark.

A multi-level ant-colony mining algorithm for membership functions

Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of binary bits to encode the membership functions. This paper elaborates on the original approach to increase the accuracy of results by adding multi-level processing. A multi-level ant colony framework is thus designed and an algorithm based on the structure is proposed to achieve the purpose. The proposed approach first transforms the fuzzy mining problem into a multi-stage graph, with each route representing a possible set of membership functions. The new approach then extends the previous one, using multi-level processing to solve the problem in which the maximum quantities of item values in the transactions may be large. The membership functions derived in a given level will be refined in the subsequent level. The final membership functions in the last level are then outputted to the rule-mining phase to find fuzzy association rules. Experiments are also performed to show the performance of the proposed approach. The experimental results show that the proposed multi-level ant colony systems mining approach can obtain improved results.

Scheduling a single batch-processing machine with arbitrary job sizes and incompatible job families: An ant colony framework

This paper investigates the first attempt on the batch-processing machine scheduling problem, where the machine can process multiple jobs simultaneously, using an ant colony optimization metaheuristic. We consider the scheduling problem of a single batch-processing machine with incompatible job families and the performance measure of minimizing total weighted completion time. Jobs of a given family have an identical processing time and are characterized by arbitrary sizes and weights. Based on a number of developed heuristic approaches, we propose an ant colony framework (ACF) in two versions, which are distinguished by the type of embedded heuristic information. Each version is also investigated in two formats, that is the pure ACF and the hybridized ACF. To verify the performance of our framework, comparisons are made based on using a set of well-known existing heuristic and meta-heuristic algorithms taken from the literature, on a diverse set of artificially generated test problem instances. Computational results show the high performance of the proposed framework and signify its ability to outperform the comparator algorithms in most cases as the problem size increases.

Ant colony pattern search algorithms for unconstrained and bound constrained optimization

Ant colony optimization is a class of metaheuristics which succeed in NP-hard combinational optimization problems rather than continuous optimization problems. We present and analyze a class of ant colony algorithms for unconstrained and bound constrained optimization on R n : Ant Colony Pattern Search Algorithms (APSAs). APSAs use the ant colony framework guided by objective function heuristic pheromone to perform local searches, whereas global search is handled by pattern search algorithms. The analysis results of APSAs prove that they have a probabilistic, weak stationary point convergence theory. APSAs present interesting emergent properties as it was shown through some analytical test functions.

Optimization of multi-pass turning operations using ant colony system

This paper proposes a new optimization technique based on the ant colony algorithm for solving multi-pass turning optimization problems. The cutting process has roughing and finishing stages. The machining parameters are determined by minimizing the unit production cost, subject to various practical machining constraints. The results indicate that the proposed ant colony framework is effective compared to other techniques carried out by different researchers.

Dynamic optimization of chemical processes using ant colony framework

Ant colony framework is illustrated by considering dynamic optimization of six important bench marking examples. This new computational tool is simple to implement and can tackle problems with state as well as terminal constraints in a straightforward fashion. It requires fewer grid points to reach the global optimum at relatively very low computational effort. The examples with varying degree of complexities, analyzed here, illustrate its potential for solving a large class of process optimization problems in chemical engineering.

Ant colony framework for optimal design and scheduling of batch plants

This paper presents a new co-operative search approach, the ant colony optimisation paradigm, for the optimal design of batch chemical processes and illustrates it by solving (1) the combinatorial optimisation problem of multiproduct batch scheduling and (2) the continuous function optimisation problem for the design of multiproduct batch plant with single product campaigns and horizon constraints. The ant algorithm is simple to implement and results of the case studies show its ability to provide speedy and accurate solutions.