Evaluation of a Network Extraction Topology Algorithm for Reducing Search Space of a GA-based Routing Approach

Abstract

In order to support multimedia communication, it is necessary to develop routing algorithms which use for routing more than one QoS parameters. This is because new services such as video on demand and remote meeting systems require better QoS. Also, for admission control of multimedia applications different QoS parameters should be considered. In our previous work, we proposed an intelligent routing and CAC strategy using cooperative agents. The proposed routing algorithm is a combination of source and distributed routing. However, in the previous research, we only considered the time delay for the routing. Also, QoS and congestion control parameters were considered just as indicators of QoS satisfication and congestion. In this work, we extend our previous work by proposing and implementing new algorithms based on fuzzy logic and genetic algorithm which use for admission control and routing many QoS parameters. Also, we evaluate by simulations the performance of the network extraction topology algorithm which reduces the search space of the proposed GA-based routing approach. Thus, the GA can find a feasible route very fast.

MOEAQ: A QoS-Aware Multicast Routing algorithm for MANET

“…Ikeda et al (2006) describe the relationship between Pareto solution and the solution space (see Fig. 1).…”

“…Albrecht (2006, 2008) import logarithmic simulated annealing (LSA) as pre-processing of GA; the algorithm utilizes the partially crossover operation (PMX) under the elitist model and the landscape analysis is presented to estimate the depth of the deepest local minimal in the landscape generated by the routing tasks and the objective function; experimental results show that the algorithm is effective on the randomly generated networks. Yang, Xu, Li, and Liu (2004) and Ikeda et al (2006) focus on creating a robust path to find solution for specified networks; the genetic algorithm is proposed and, respectively, the individuals of the population are represented by trees, algorithm uses the single point crossover and a mutation operation where the ''tree junctions" are chosen randomly, the algorithm employs the elitist model where the individual with the highest fitness value in a population is left unchanged in the next generation, the simulation results show that the algorithm is reasonably fast on small and medium size networks. Differ from the above network architecture, Rango, Tropea, Santamaria, and Marano (2007) and Mala and Swlvakumar (2006) refer a scheme called Core Based Tree (CBT) with genetic algorithm which provides a new way for realizing multicast routing protocol in wireless networks, however, it needs much running time.…”

A Distributed QoS Routing and CAC Framework: Performance Evaluation of Its SSRA and InterD Agents

“…In order to extract the ET, the network connectivity information, link and node metrics, and QoS requirement of the new connection are required. In our previous work [12], we used link parameters T SR T h, DT T h, CC T h to specify the QoS demands of a new connection. If DT < DT T h, T SR > T SR T h, CC < CC T h for a connection, this means that every path which passes via this link satisfies the connection requirements.…”

“…However, in the proposed Fuzzy Admission Control (FAC) scheme and Fuzzy Equivalent Capacity Estimator (FECE), we considered only two indicators for QoS and Congestion Control (CC) [10,11]. In [12,13], we extended our work by proposing two additional schemes: Fuzzy QoS Controller (FQC) and Fuzzy Congestion Controller (FCC), which are integrated in the previous scheme. The new scheme is called Fuzzy Admission Control for Multimedia applications (MFAC).…”

“…However, the ARGAQ method is effective only if the parameters have a relation between them and the GA fitness function can be expressed by a mathematical formula. In [12,13], we proposed a new GA-based multi-purpose optimization algorithm for QoS routing. The proposed method uses a multi-division group model for multi-purpose optimization.…”