Bacteria foraging optimization algorithm with immigrants and memory schemes for dynamic shortest path routing problem in mobile adhoc network

Abstract

In an Internet Routing mechanism, Swarm Intelligence (SI) techniques like Particle Swarm Optimization, Ant Colony Optimization and Bacteria Foraging Optimization are confronting static Shortest Path (SP) Routing Problem. With the advancement of wireless communications networks, the numbers of mobile network devices are drastically increasing in the wireless networking environment due to their dynamic nature. The mobile network devices such as sensor networks and Mobile Adhoc NETworks(MANET s) are unable to adhere to the dynamic changing environment. newlineMANET is self-motivated and self-configuring multi hop wireless network, which contains a collection of mobile nodes. One of the important characteristics of MANET is topology changes due to node mobility. Therefore, the SP routing problem in MANETs turns out into Dynamic Optimization Problem(DOP). Bacteria Foraging Optimization (BFO) based routing is best in terms of quicker convergence, overall search space, robustness and accuracy when compared to Swarm Intelligence techniques and traditional routing algorithms. This technique has a bunch of completely different benefits relating to native minima, randomness, direction of movement, attraction/repelling, swarming and so on. In this thesis, Bacteria Foraging Optimization Algorithm (BFOA) with immigrants and memory schemes is proposed to solve the Dynamic Shortest Path Routing Problem (DSPRP) in MANETs. newline newline newline