Implementation of genetic algorithms in optical wavelength ring routed network design.

The design process of the ring routed wavelength optical network encompasses the search for a layout order of nodes such that the total overall traffic is minimized in terms of distant communication and traffic frequencies. The optimal solution is a member of a massive domain set reaching sizes in the order of n!, where n is the number of nodes in the network; typically n = 100. A brute force, linear search algorithm can be implemented, but when executed in search for an optimal solution, the algorithm is a computational challenge as it becomes time consuming and renders itself unfeasible in realistic design time criteria. In researching a suitable search algorithm with the flexibility to adapt to changing network traffic parameters and render a near optimal solution in reasonable design time constraints, the genetic algorithm presents a candidate solution to be tested. The goal of this thesis is to implement a custom genetic algorithm and examine its potential in reaching near optimal solutions in the optical design application framework. The course of work was divided into three major development stages. In the first stage, a simple object oriented GA model was developed (SGA). Then the model was customized to the ring routed network design application, known as the Simple Optical Genetic Algorithm (SOGA) and finally the revised algorithm is implemented and termed the Optical Genetic Algorithm (OGA). In smaller networks (up to 12 nodes) the GA is compared to a brute force linear algorithm to test its performance. For larger networks, the GA was compared to a random search algorithm to test its effectiveness. In both cases, the GA has shown to surpass the other algorithms in generating a pool of near optimal solutions in reasonable time constraints. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .K63. Source: Masters Abstracts International, Volume: 40-03, page: 0724. Adviser: Subic Bandgopadhyay. Thesis (M.Sc.)--University of Windsor (Canada), 1999

Survey of Multiple Clouds: Classification, Relationships and Privacy Concerns

When major Cloud Service Providers (CSPs) network with other CSPs, they show a predominant area over cloud computing architecture, each with different roles to serve user demands better. This creates multiple clouds computing environments, which overcome the limitations of cloud computing and bring a wide range of benefits (e.g., avoiding vendor lock-in problem). Numerous applications can use various multiple clouds types depending on their specifications and needs. Deploying multiple clouds under hybrid or public models has introduced various privacy concerns that affect users and their data in a specific application domain. To understand the nuances of these concerns, the present study conducted a survey to identify the various classifications of multiple clouds types and then extend the cloud entities’ relationships to behave in different multiple clouds settings. The survey results outline users’ privacy and data confidentiality concerns in multiple clouds types under public and hybrid deployment models

The Effects of Generalized Reciprocal Exchange on the Resilience of Social Networks: An Example from the Prehispanic Mesa Verde Region

The initial version of the model used in this study, Village 1.0, was implemented by Tim Kohler and a team of developers mostly from Washington State University. The original model addressed environmental constraints only and did not attempt to model social interaction. In a recent paper we employed Cultural Algorithms as a framework in which to add selected social considerations. In this paper we extend our previous model by adding the ability of agents to perform symmetrically initiated or asymmetrically initiated generalized reciprocal exchange. We have developed a state model for agents' knowledge and, given agents' different responses based on this knowledge. Experiments have shown that the network structure of the systems without reciprocity was the simplest but least resilient. As we allowed agents more opportunities to exchange resources we produced more complex network structures, larger populations, and more resilient systems. Furthermore, allowing the agents to buffer their requests by using a finite state model improved the relative resilience of these larger systems. Introducing reciprocity that can be triggered by both requestors and donors produced the largest number of successful donations. This represents the synergy produced by using the information from two complementary situations within the network. Thus, the network has more information with which it can work and tended to be more resilient than otherwise.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44742/1/10588_2004_Article_5270975.pd

A Hybrid Artificial Reputation Model Involving Interaction Trust, Witness Information and the Trust Model to Calculate the Trust Value of Service Providers

Agent interaction in a community, such as the online buyer-seller scenario, is often uncertain, as when an agent comes in contact with other agents they initially know nothing about each other. Currently, many reputation models are developed that help service consumers select better service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information or they are built upon witness information also known as word-of-mouth that involves the reports provided by others. Neither the interaction trust nor the witness information models alone succeed in such uncertain interactions. In this paper we propose a hybrid reputation model involving both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. A sample simulation is built to setup buyer-seller services and uncertain interactions. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputable service providers, eventually helping consumers obtain more gains. Furthermore, the trust model developed is used in calculating trust values of service providers

Learning and knowledge generation in general games

Abstract — General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and in the absence of game specific knowledge, become proficient players. Most GGP players have used standard tree-search techniques enhanced by automatic heuristic learning. In this paper we explore knowledge representation and learning in GGP using Reinforcement Learning and Ant Colony Algorithms. Knowledge is created by simulating random games. We test the quality of the knowledge by comparing the performance of players using the knowledge in a variety of games. The ideas presented in this paper provide the potential for a framework for learning and knowledge representation, given the total absence of any prior knowledge. I

Weighted SCAN for Modeling Cooperative Group Role Dynamics

Abstract—Social agents have the ability of communicating and forming groups with each other. Group members in games typically share the same role. In dynamic environments with the presence of obstacles and barriers separating members from each other presents a situation where a member separated from the rest of the group, while still a member of that group, should not have the same role or updates of the rest of the group due to the physical distance presented by the obstacles. This study introduces a weighted version of the SCAN and hierarchical SCAN graph clustering algorithms which are essentially based on neighbors. The autonomous agent players in spatial strategy game scenarios tested with the weighted SCAN have demonstrated an improved realistic behaviour in the social test settings. I