Shortest Paths Routing Problem in MANETs

The need for communication services is rapidly increasing, because the mobile communication service is synonymous with an ideal communication style realizing communication anytime, anywhere and with anyone. The availability of a path depends on the number of links and the reliability of each link forming the path. Many routing metrics in terms of number of links have been proposed, such as the shortest path routing. Shortest path routing selects a path having minimum cost to forward the data to the destination node. Shortest path routing algorithm selection depends on direct traffic form source to destination, maximizing the network performance and minimizing the cost. Performance of the network can be enhanced through shortest path routing but it also depends upon the functionality of the routing protocol and the parameters that are selected for the shortest path routing. The primary goal of such an adhoc network routing protocol is correct and efficient route establishment between a pair of nodes so that messages may be delivered in a timely manner. Route construction should be done with a minimum of cost, overhead and bandwidth consumption. Some of researchers explored the concept of shortest path routing over ad hoc network. Each one uses his own parameters with different topology. No one uses all parameters. In this paper, we will discuss the solutions ideas that have been proposed by them

Mobile Robot Path Finding using Nature Inspired Algorithms - A Review

 In today’s world, Mobile Robot has been widely used for various purposes across several aspects of life. The environments could be static and dynamic. Path planning for mobile robot is a very important problem in robotics. Path Planning for robot could be referred to the determination of a path; a robot takes in to perform a task given a set of key inputs. To find the best and optimal path from the starting point to the goal point, such that time and distance is reduce, in any given environment avoiding collision with obstacles is an interesting area for research. This research presents a review on the application of nature inspired algorithms in solving the problem of mobile robot path planning such that the robot reaches the target station from source station without collision with obstacles. The future of these nature-inspired algorithms on mobile robot is also discussed

A Comprehensive Evaluation of Nature Inspired Routing Algorithm for Mobile Ad Hoc Network : DEA and BCA

This paper discussed about the comprehensive evaluation of nature inspired routing algorithms such as Dolphin Echolocation Algorithm (DEA) and Bee colony Algorithm (BCA) use for distance optimization. The influence of DEA and BCA algorithms on Quality of Service (QoS) performance matrices for Mobile Ad hoc Network (MANET) is analyzed. Ultimately with the help of DEA it is possible to achieve optimized routing path between source and destination nodes. Further this paper have the analysis of various results which gives the comprehensive evaluation of DEA algorithm and it is suitable for MANET for achieving good Throughput, packet delivery ratio, delay and overhand

Planificación de trayectorias usando metaheurísticas

In this work, a comparison between two metaheuristic methods to solve the path planning problem is presented. These methods are 1) Artificial ant colony and 2) Artificial bee colony. The following metrics are used to evaluate these implementations: 1) Path length and 2) Execution time. The comparison was tested using ten maps obtained from the University of Prague Department of Intelligent Cybernetics and the Mobil Robotics Group. Several runs were carried out to find the best algorithm parameters and get the best algorithm for the route planning task. The best algorithm was the artificial bee colony. These evaluations were visualized using the VPython package; here, a differential mobile robot was simulated to follow the trajectory calculated by the best algorithm. This simulation made it possible to observe that the robot makes the correct trajectory from the starting point to the objective point in each evaluated map.En este trabajo se presenta una comparación entre dos métodos metaheurísticos para resolver problemas de planificación de rutas. Estos métodos son: 1) Colonia de hormigas artificiales y 2) Colonia de abejas artificiales. Para evaluar estas implementaciones, se utilizan las siguientes métricas: 1) Longitud de ruta y 2) Tiempo de ejecución. El comparativo se probó utilizando diez mapas obtenidos del Departamento de Cibernética Inteligente y Mobil Robotics Group de la Universidad de Praga. Se realizaron varias ejecuciones con el objetivo de encontrar los mejores parámetros de los algoritmos y obtener el mejor algoritmo para la tarea de planificación de ruta. El mejor algoritmo fue la colonia de abejas artificiales. Estas evaluaciones se visualizaron utilizando el paquete VPython, aquí se simuló un robot móvil diferencial para seguir la trayectoria calculada por el mejor algoritmo. A partir de esta simulación fue posible observar que el robot realiza la trayectoria correcta desde el punto de inicio hasta el punto objetivo en cada uno de los mapas evaluados

Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

[Extract] Population based optimization algorithms are the techniques which are in the set of the nature based optimization algorithms. The creatures and natural systems which are working and developing in nature are one of the interesting and valuable sources of inspiration for designing and inventing new systems and algorithms in different fields of science and technology. Evolutionary Computation (Eiben& Smith, 2003), Neural Networks (Haykin, 99), Time Adaptive Self-Organizing Maps (Shah-Hosseini, 2006), Ant Systems (Dorigo & Stutzle, 2004), Particle Swarm Optimization (Eberhart & Kennedy, 1995), Simulated Annealing (Kirkpatrik, 1984), Bee Colony Optimization (Teodorovic et al., 2006) and DNA Computing (Adleman, 1994) are among the problem solving techniques inspired from observing nature. In this chapter population based optimization algorithms have been introduced. Some of these algorithms were mentioned above. Other algorithms are Intelligent Water Drops (IWD) algorithm (Shah-Hosseini, 2007), Artificial Immune Systems (AIS) (Dasgupta, 1999) and Electromagnetism-like Mechanisms (EM) (Birbil & Fang, 2003). In this chapter, every section briefly introduces one of these population based optimization algorithms and applies them for solving the TSP. Also, we try to note the important points of each algorithm and every point we contribute to these algorithms has been stated. Section nine shows experimental results based on the algorithms introduced in previous sections which are implemented to solve different problems of the TSP using well-known datasets