Self-Adaptive Intelligent Routing in Dynamic WSN using Natural Inspired Computing

International Conference on Materials, Alloys and Experimental Mechanics 2017

材料科学;金属学;机械制造

Mustary, Nareshkumar R.^1 ; Phanikumar, S.^1

Department of Computer Engineering, GITAM University, Andrapradesh, Hyderabad, India^1

关键词: Changing environment;    Constriction factor;    Dynamic optimization problem (DOP);    Inertia weight;    MANETs;    Modified particle swarm optimization;    Mutation operators;    Optimization problems;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/225/1/012123/pdf
DOI  :  10.1088/1757-899X/225/1/012123

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Mobile Adhoc Networks are designed dynamically without any infrastructure and each node is accountable for routing information amongst them. In MANET's, the network topology dynamically variations over time to time due to energy preservation or changes in node position. Thus both routing problem turn out to be dynamic optimization problem in MANET's. Hence it is crucial to design solution for the optimization problem is to quickly adopt to changing environment and produce high quality optimization using Modified Particle Swarm Optimization. The Particle Swarm Optimization is effective in determining optimal solutions in fixed locations, but it suffered from poor performance in locating a changing extreme. It was also necessary to impose a maximum value Vmaxto avoiding the particle exploded because of there was no exist a mechanism for controlling the velocity of a particle. PSO searches wide areas effectively, but difficult to search in local precision. Hence, introduced a control parameter called the inertia weight, "w", to damp the velocities over time, allowing the swarm to converge more accurately and efficiently.