A reduced-uncertainty hybrid evolutionary algorithm for solving dynamic shortest-path routing problem

The need for effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a Reduced Uncertainty Based Hybrid Evolutionary Algorithm (RUBHEA) to solve Dynamic Shortest Path Routing Problem (DSPRP) effectively and rapidly. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A Complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks

Network routing optimisation and effective multimedia transmission to enhance QoS in communication networks

With the increased usage of communication services in networks, finding routes for reliable transmission and providing effective multimedia communication have become very challenging problems. This has been a strong motivation to examine and develop methods and techniques to find routing paths efficiently and to provide effective multimedia communication. This thesis is mainly concerned with designing, implementing and adapting intelligent algorithms to solve the computational complexity of network routing problems and testing the performance of intelligent algorithms’ applications. It also introduces hybrid algorithms which are developed by using the similarities of genetic algorithm (GA) and particle swarm optimization (PSO) intelligent systems algorithms. Furthermore, it examines the design of a new encoding/decoding method to offer a solution for the problem of unachievable multimedia information in multimedia multicast networks. The techniques presented and developed within the thesis aim to provide maximum utilization of network resources for handling communication problems. This thesis first proposes GA and PSO implementations which are adapted to solve the single and multi-objective functions in network routing problems. To offer solutions for network routing problems, binary variable-length and priority based encoding methods are used in intelligent algorithms to construct valid paths or potential solutions. The performance of generation operators in GA and PSO is examined and analyzed by solving the various shortest path routing problems and it is shown that the performance of algorithms varies based on the operators selected. Moreover, a hybrid algorithm is developed based on the lack of search capability of intelligent algorithms and implemented to solve the single objective function. The proposed method uses a strategy of sharing information between GA and PSO to achieve significant performance enhancement to solve routing optimization problems. The simulation results demonstrate the efficiency of the hybrid algorithm by optimizing the shortest path routing problem. Furthermore, intelligent algorithms are implemented to solve a multi-objective function which involves more constraints of resources in communication networks. The algorithms are adapted to find the multi-optimal paths to provide effective multimedia communication in lossy networks. The simulation results verify that the implemented algorithms are shown as efficient and accurate methods to solve the multi-objective function and find multi-optimal paths to deliver multimedia packets in lossy networks. Furthermore, the thesis proposes a new encoding/decoding method to maximize throughput in multimedia multicast networks. The proposed method is combined with two most used Multiple Description Coding (MDC) methods. The utilization of the proposed method is discussed by comparing two the MDC methods. Through analyzing the simulation results using these intelligent systems algorithms, it has been shown that feasible solutions can be obtained by optimizing complex network problems. Moreover, the methods proposed and developed, which are hybrid algorithms and the encoding/decoding method also demonstrate their efficiency and effectiveness as compared with other techniques

K- shortest path network problem solution with a hybrid Genetic Algorithm: Particle Swarm Optimization algorithm

This paper presents a hybrid evolutionary algorithm (HGAPSO) to maximize utilization and improve the Quality of Service (QoS) in expanding networks. Two meta-heuristic optimization algorithms, namely a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are combined to find the feasible solution within a search space of telecommunication networks. By employing a local search based priority encoding method, each individual in the GA and each particle in PSO is represented as a potential solution for the routing problem. The performance of HGAPSO is compared to both the GA and PSO alone for finding the K-shortest paths, demonstrating its superiority