Abstract

In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation.

Abstract

Modern communication networks offer advance and diverse applications, which require huge usage of network resources while providing quality of services to the users. Advance communication is based on multiple switched networks that cannot be handle by traditional IP (internet protocol) networks. GMPLS (Generalized multiprotocol label switched) networks, an advance version of MPLS (multiprotocol label switched networks), are introduced for multiple switched networks. Traffic engineering in GMPLS networks ensures traffic movement on multiple paths. Optimal path(s) computation can be dependent on multiple objectives with multiple constraints. From optimization prospective, it is an NP (non-deterministic polynomial-time) hard optimization problem, to compute optimal paths based on multiple objectives having multiple constraints. The paper proposed a metaheuristic Pareto based Bat algorithm, which uses two objective functions; routing costs and load balancing costs to compute the optimal path(s) as an optimal solution for traffic engineering in MPLS/GMPLS networks. The proposed algorithm has implemented on different number of nodes in MPLS/GMPLS networks, to analysis the algorithm performance. Document type: Part of book or chapter of book

Abstract

Multiprotocol label switched (MPLS) networks were introduced to enhance the network`s service provisioning and optimize its performance using multiple protocols along with label switched based networking technique. With the addition of traffic engineering entity in MPLS domain, there is a massive increase in the networks resource management capability with better quality of services (QoS) provisioning for end users. Routing protocols play an important role in MPLS networks for network traffic management, which uses exact and approximate algorithms. There are number of artificial intelligence-based optimization algorithms which can be used for the optimization of traffic engineering in MPLS networks. The paper presents an optimization model for MPLS networks and proposed dolphin-echolocation algorithm (DEA) for optimal path computation. For Network with different nodes, both algorithms performance has been investigated to study their convergence towards the production of optimal solutions. Furthermore, the DEA algorithm will be compared with the bat algorithm to examine their performance in MPLS network optimization. Various parameters such as mean, minimum /optimal fitness function values and standard deviation. Document type: Conference object

Abstract

Introduction of modern and diverse applications in telecommunication field has raised challenges in networking area regarding efficient use of network resources and with optimizing performance. Therefore MPLS/GMPLS (Generalized multiprotocol label switching) networks were introduced to provide a better quality of service to meet users' requirements as well as to optimize network resources. GMPLS networks use traffic engineering techniques for more efficient communication within the network and help to optimize network resources. This paper proposes BAT inspired metaheuristic algorithm for selecting an efficient route in MPLS/ GMPLS networks. In our investigation we considered routing costs as an objective function with goal to minimize it. The paper uses BAT algorithm with various levels of loudness parameter. The simulation results show performance improvements in MPLS/GMPLS networks of different size. Document type: Conference object