Ong Hang See

A Coarse-Grained Parallel Genetic Algorithm with Migration for Shortest Path Routing Problem

Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a coarse-grained parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. The migration scheme, which is commonly used in coarse-grained parallel genetic algorithm, is also employed in the proposed algorithm. This algorithm is developed and run on an MPI cluster. This paper studies the effect of migration on the proposed algorithm and the performance of the algorithm as compared to its serial counterpart.

A Parallel Genetic Algorithm for Shortest Path Routing Problem

Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. This algorithm is developed and run on an MPI cluster. Based on experimental result, there is a tradeoff between computation time and the result accuracy. However, for the same level of accuracy, the proposed parallel algorithm can perform much faster compared to its non-parallel counterpart.

A Robust GA-based QoS Routing Algorithm for Solving Multi-constrained Path Problem

To support networked multimedia applications, it is important for a network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill certain constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is NP-complete and finding an exact solution can be difficult. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. However, the actual link metrics in a QoS-aware network is dynamic and may continuously change over time and since the path given by the routing algorithm is computed using the state information available to the router, which may or may not be up-to-date, it is possible that a feasible path returned by the algorithm may turn out to be no longer valid. This paper presents a GA-based QoS routing algorithm for solving the general k -constrained problem which has the capability to return multiple feasible paths in a single run. This makes the algorithm more robust in the case that the rate of change of state information in the network is higher than the rate of state information received by the router. Simulation results show that this algorithm consistently achieve higher feasibility ratio relative to existing well-known MCP routing algorithms when state information in the router lags behind the network.

Dynamic residential load scheduling based on adaptive consumption level pricing scheme

Abstract Demand response (DR) for smart grids, which intends to balance the required power demand with the available supply resources, has been gaining widespread attention. The growing demand for electricity has presented new opportunities for residential load scheduling systems to improve energy consumption by shifting or curtailing the demand required with respect to price change or emergency cases. In this paper, a dynamic residential load scheduling system (DRLS) is proposed for optimal scheduling of household appliances on the basis of an adaptive consumption level (CL) pricing scheme (ACLPS). The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance (CA) of the proposed DR pricing scheme to achieve lower energy bills. Simulation results show that employing the proposed DRLS system benefits the customers by reducing their energy bill and the utility companies by decreasing the peak load of the aggregated load demand. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by up to 35%.

A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogeneous wireless networks

Network selection mechanisms have a significant role in guaranteeing the QoS for users in a heterogeneous wireless networks environment. These mechanisms allow the selection of an optimal wireless network to satisfy the needs of users. Users are provided with the opportunity to select from multiple connectivity opportunities available all over various wireless networks. Furthermore, the network operators themselves can execute active selection strategies that facilitate proper decision making, in which user preferences are considered. This study proposes a new noncooperative competing game-theoretic model and strategy space based on user preference. This model can solve network selection problems and capture the inter-linkages of decisions taken by various networks. A generalized simple additive weighting method is incorporated into the framework of noncooperative game theory. In addition, the utility function is employed to assess the usefulness of the system. Simulation results and analysis illustrate the efficacy of the suggested model in attaining optimum network utility for heterogeneous wireless networks while optimizing user satisfaction. Copyright

Finding multi-constrained path using genetic algorithm

To properly support networked multimedia applications, it is important for the network to provide quality-of- service (QoS) guarantees. One way to provide QoS guarantees is for the network to perform QoS routing, where the path taken must fulfill certain constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is NP-complete and therefore no exact algorithm can be found. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. This paper presents a solution to the MCP problem using genetic algorithm (GA). Through simulation, this algorithm has been shown to give a high probability of finding a feasible path if such paths exist.

An intelligent selection method based on game theory in heterogeneous wireless networks

In next-generation networks, users can optimize the choice with seamless transfer of different access technologies to maximize cost saving and improve quality of service. In such heterogeneous wireless environments, users equipped with multimode wireless devices that can access rich media services via one or more access networks. These networks may differ in terms of monetary cost, coverage range, technology, available bandwidth, energy usage and so on. The new challenge faced by network operators is to ensure users always connect to the best available network, whenever they want and wherever they are or another word to stay Always-Best-Connected. Therefore, a new intelligent method is proposed here to optimize vertical handover in selection processes to ensure Always-Best-Connected in heterogeneous wireless networks. The proposed solution identifies a new game network selection model for choosing the best connections from different candidate wireless networks. The selected network should satisfy the typical user demands. The proposed selection model is implemented by using the non-cooperative game model. The proposed solution is evaluated through in-depth analysis and simulation-based testing. Results clearly prove that the proposed solution exhibits the highest performance compared with other solutions. Copyright

The effect of GA parameters on the performance of GA-based QoS routing algorithm

Genetic algorithm (GA) is a powerful search and optimization algorithm inspired by the theory of genetics and natural selection. However, the performance of GA depends largely on the values chosen for the GA parameters. In the previous work, a GA-based QoS routing algorithm for solving the multi-constrained path (MCP) problem has been developed. This paper presents the simulation result of the effect of three GA parameters which are maximum iterations, population size and mutation probability on the developed algorithm.

A user-centric game selection model based on user preferences for the selection of the best heterogeneous wireless network

Next-generation wireless networks offer Internet connection through various technologies anytime and anywhere. The selection of an optimal technology from these available technologies is essential to guarantee user mobility and service continuity in a heterogeneous wireless environment. This paper proposes a new network selection model which is based on the integration of simple additive weighting (SAW) method in the framework of trading market non-cooperative game theory and the analytic hierarchy process (AHP) method was utilized to estimate the weights of the parameters that affect the network selection process. The proposed solution enables the mobile user to negotiate with competing networks by providing the user preference to be considered for the network selection process. The proposed solution is analyzed and tested through simulations. The results show the efficiency of proposed method which is able to optimize the user’s satisfaction.

QoS Routing for Multiple Additive QoS Parameters using Genetic Algorithm

This paper presents an algorithm for QoS routing using genetic algorithm. The algorithm concentrates on solving the problem of multiple additive QoS parameters, which has been proven to be NP-complete. This paper discusses the various aspects of genetic algorithm design including selection, fitness function, crossover and mutation. The algorithm was implemented and tested on a 5times5 mesh network to test for its effectiveness. The simulation result shows that this algorithm can perform well regardless of the number of QoS parameters used