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Novel dynamic source routing protocol ( DSR ) based on genetic algorithm‐bacterial foraging optimization ( GA‐BFO )
Author(s) -
Zhang Degan,
Liu Si,
Liu Xiaohuan,
Zhang Ting,
Cui Yuya
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3824
Subject(s) - computer science , destination sequenced distance vector routing , dynamic source routing , algorithm , overhead (engineering) , node (physics) , routing (electronic design automation) , routing protocol , mathematical optimization , computer network , mathematics , operating system , structural engineering , engineering
Summary The goal of mobile ad hoc network (MANET) routing protocol is to establish a correct and effective route with minimal control overhead and bandwidth consumption. Dynamic source routing protocol (DSR) is a simple on‐demand routing protocol designed specifically for MANETs. The choice of routing path only adopts the simplest minimum hop count algorithm, while it did not take the node energy into account. In order to improve the control overhead of the network, we comprehensively consider the node energy information when searching the routes to the destination nodes. We propose a genetic algorithm (GA)‐bacterial foraging optimization algorithm to perform the selection of the optimal routing. After searching out multiple routes to the destination node, the paths are initialized then the GA algorithm is started. This algorithm quickly finds the positions of the maximum probability optimal paths, which are the initial positions of bacteria for the bacteria foraging optimization (BFO) algorithm. Through using the BFO algorithm, it is easy to search out the extreme value and the optimal path in order to compensate for the poor accuracy of GA algorithm. Our proposed optimized strategy improves the routing selection algorithm without change the complexity of DSR and proves the convergence of the algorithm to the global optimal solution. The simulation shows that the proposed algorithm is feasible and applicable and also has better experimental result.