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Biologically Inspired Energy Efficient Routing Protocol in Disaster Situation

Chavan Shankar D., Thorat Amruta S., Gunjal Monica S., Vibhute Anup S., Desai Kamalakar R.

International Journal of Electronics and Telecommunications

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2023

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Vol. 69, No. 1

163--168

EN

Wireless sensor network (WSN) plays a crucial role in many industrial, commercial, and social applications. However, increasing the number of nodes in a WSN increases network complexity, making it harder to acquire all relevant data in a timely way. By assuming the end node as a base station, we devised an Artificial Ant Routing (AAR) method that overcomes such network difficulties and finds an ideal routing that gives an easy way to reach the destination node in our situation. The goal of our research is to establish WSN parameters that are based on the biologically inspired Ant Colony Optimization (ACO) method. The proposed AAR provides the alternating path in case of congestion and high traffic requirement. In the event of node failures in a wireless network, the same algorithm enhances the efficiency of the routing path and acts as a multipath data transmission approach. We simulated network factors including Packet Delivery Ratio (PDR), Throughput, and Energy Consumption to achieve this. The major objective is to extend the network lifespan while data is being transferred by avoiding crowded areas and conserving energy by using a small number of nodes. The result shows that AAR is having improved performance parameters as compared to LEACH, LEACH-C, and FCM-DS-ACO.

2

Detectability of Elusive Reptiles under Artificial Cover Objects is Species- and Year-Specific

Kolanek Aleksandra, Bury Stanisław

Polish Journal of Ecology

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2020

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Vol. 68, nr 4

342--347

EN

Field surveys of rare and elusive reptiles often encounter the problem of low detectability. Therefore, several techniques have been invented to improve detection probability and artificial cover objects (ACOs) are among the most commonly used in reptile studies. However, the methodological effectiveness of ACOs has been rarely evaluated and focused mostly on spatial aspects. The temporal dimension of the ACOs effectiveness remains still understudied, despite well-known seasonal variation in reptile activity patterns. Here, we examined seasonal and between-year variation in the fraction of occupied ACOs, as a proxy for detectability, in two elusive reptile species, the slow worm Anguis fragilis and smooth snake Coronella austriaca. We found that the use of ACOs was species-specific and showed high temporal variation. In the case of smooth snakes, monthly usage varied between years; specifically within-year variation of the proportion in occupied ACOs was most pronounced in 2015, but seems vanishing in consecutive years. This loss of of seasonal pattern occurs only in the last year of survey in the case of slow worm and monthly use of ACOs seem not to vary between years. Considerably low detectability of the studied species by the ACO method in some years may not necessarily indicate their low population density, but rather results from shifts in their diurnal activity and/or microhabitat use dependent on ambient temperatures. Increasing between-year variation in weather conditions may reduce repeatability of seasonal patterns of ACO usage, making we suggest additional detection techniques that could bee incorporated.

3

Ants Colony Optimisation of a Measuring Path of Prismatic Parts on a CMM

Stojadinovic S. M., Majstorovic V. D., Durakbasa N. M., Sibalija T. V.

Metrology and Measurement Systems

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2016

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Vol. 23, nr 1

119--132

EN

This paper presents optimisation of a measuring probe path in inspecting the prismatic parts on a CMM. The optimisation model is based on: (i) the mathematical model that establishes an initial collision-free path presented by a set of points, and (ii) the solution of Travelling Salesman Problem (TSP) obtained with Ant Colony Optimisation (ACO). In order to solve TSP, an ACO algorithm that aims to find the shortest path of ant colony movement (i.e. the optimised path) is applied. Then, the optimised path is compared with the measuring path obtained with online programming on CMM ZEISS UMM500 and with the measuring path obtained in the CMM inspection module of Pro/ENGINEER® software. The results of comparing the optimised path with the other two generated paths show that the optimised path is at least 20% shorter than the path obtained by on-line programming on CMM ZEISS UMM500, and at least 10% shorter than the path obtained by using the CMM module in Pro/ENGINEER®.

4

An improved ant colony optimization algorithm and its application to text-independent speaker verification system

Aghdam M. H.

Journal of Artificial Intelligence and Soft Computing Research

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2012

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Vol. 2, No. 4

301--315

EN

With the growing trend toward remote security verification procedures for telephone banking, biometric security measures and similar applications, automatic speaker verification (ASV) has received a lot of attention in recent years. The complexity of ASV system and its verification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we concentrate on optimizing dimensionality of feature space by selecting relevant features. At present there are several methods for feature selection in ASV systems. To improve performance of ASV system we present another method that is based on ant colony optimization (ACO) algorithm. After feature selection phase, feature vectors are applied to a Gaussian mixture model universal background model (GMM-UBM) which is a text-independent speaker verification model. The performance of proposed algorithm is compared to the performance of genetic algorithm on the task of feature selection in TIMIT corpora. The results of experiments indicate that with the optimized feature set, the performance of the ASV system is improved. Moreover, the speed of verification is significantly increased since by use of ACO, number of features is reduced over 80% which consequently decrease the complexity of our ASV system.

5

An ACO Path Planner Using a FIS for Path Selection Adjusted with a Simple Tuning Algorithm

Porta-Garcia M., Montiel O., Sepulveda R.

Journal of Automation Mobile Robotics and Intelligent Systems

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2008

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Vol. 2, No. 1

5-11

EN

This paper presents a path planner application for mobile robots based on Ant Colony Optimization (ACO). The selection of the optimal path relies in the criterion of a Fuzzy Inference System (FIS), which is adjusted using a Simple Tuning Algorithm (STA). The path planner can be executed in Mode I and Mode II. The first mode only works in the virtual environment of the interface, while Mode II embraces the wireless communication with a real robot; once the ACO algorithm finds the best route, the coordinates are sent to a mobile robot via Bluetooth communication; if the robot senses a new obstacle, the computer is notified and does a rerouting routine in order to avoid the obstacle and reach the goal. In other words, the application supports dynamic search spaces.

6

Comparative study : AGO and EC for TSP

Boryczka U., Skinderowicz R., Swistowski D.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2006

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z. 156

69-76

EN

Evolutionary Computing (EC) and Ant Colony Optimization (ACO) apply stochastic searching, parallel investigation as well as autocatalitic process (or stigmergy) to solve optimization problems. This paper concentrates on the Traveling Salesman Problem (TSP) solved by evolutionary and ACO algorithms. We consider the sets of parameters and operators which influence the acting of these algorithms. Two algorithmic structures emphasizing the selection problem are discussed. We describe experiments performed for different instances of TSP problems. The comparison concludes that evolution, which is exploited especially in evolutionary algorithms, can also be observed in the performance of the ACO approach.