Browsing by Author "Goteng, Gokop"

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    Development of a grid service for multi-objective design optimisation
    (Cranfield University, 2009-04) Goteng, Gokop; Tiwari, Ashutosh; Roy, Rajkumar
    The emerging grid technology is receiving great attention from researchers and applications that need computational and data capabilities to enhance performance and efficiency. Multi-Objective Design Optimisation (MODO) is computationally and data challenging. The challenges become even more with the emergence of evolutionary computing (EC) techniques which produce multiple solutions in a single simulation run. Other challenges are the complexity in mathematical models and multidisciplinary involvement of experts, thus making MODO collaborative and interactive in nature. These challenges call for a problem solving environment (P SE) that can provide computational and optimisation resources to MODO experts as services. Current PSEs provide only the technical specifications of the services which is used by programmers and do not have service specifications for designers that use the system to support design optimisation as services. There is need for PSEs to have service specification document that describes how the services are provided to the end users. Additionally, providing MODO resources as services enabled designers to share resources that they do not have through service subscription. The aim of this research is to develop specifications and architecture of a grid service for MODO. The specifications provide the service use cases that are used to build MODO services. A service specification document is proposed and this enables service providers to follow a process for providing services to end users. In this research, literature was reviewed and industry survey conducted. This was followed by the design, development, case study and validation. The research studied related PSEs in literature and industry to come up with a service specification document that captures the process for grid service definition. This specification was used to develop a framework for MODO applications. An architecture based on this framework was proposed and implemented as DECGrid (Decision Engineering Centre Grid) prototype. Three real-life case studies were used to validate the prototype. The results obtained compared favourably with the results in literature. Different scenarios for using the services among distributed design experts demonstrated the computational synergy and efficiency in collaboration. The mathematical model building service and optimisation service enabled designers to collaboratively build models using the collaboration service. This helps designers without optimisation knowledge to perform optimisation. The key contributions in this research are the service specifications that support MODO, the framework developed which provides the process for definining the services and the architecture used to implement the framework. The key limitations of the research are the use of only engineering design optimisation case studies and the prototype is not tested in industry.
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    Evolutionary Computing within Grid Environment.
    (2007-01-01T00:00:00Z) Tiwari, Ashutosh; Goteng, Gokop; Roy, Rajkumar
    Evolutionary computing (EC) techniques such as genetic algorithm (GA), genetic programming (GP), evolutionary programming (EP) and evolution strategies (ES) mimic nature through natural selection to perform complex optimisation processes that require more than one solutions. Grid-enabled environment provides suitable framework for EC techniques due to its computational and data capabilities. In addition, the semantic and knowledge Grids aid in the design search and exploration for multi-objective optimisation tasks. This chapter explores some problem solving environments such as Geodise (Grid-Enabled Optimisation Design Search for Engineering), FIPER (Federated Intelligent Product Environment), SOCER (Service-Oriented Concurrent Environment), DAME (Distributed Aircraft Maintenance Environment) and Globus toolkit to demonstrate how EC techniques can be performed more efficiently within Grid environment. Service-oriented and autonomic computing features of Grid are discussed to highlight how EC algorithms can be published as services by service providers and used by service requestors dynamically. Grid computational steering and visualisation are features that can be used for real-time tuning of parameters and visual display of optimal solutions. This chapter demonstrates that grid-enabled evolutionary computing marks the future of optimisation techniques.
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    Grid computing for engineering design optimisation: Evolution and future trends
    (2007-03-01T00:00:00Z) Goteng, Gokop; Tiwari, Ashutosh; Roy, Rajkumar; Editor
    Grid Computing is fast gaining ground both within academia and the commercial sectors. It has shifted from its traditional scientific-based applications to serviceoriented problem solving environments for commerce and business. Engineering design optimisation (EDO) is characteristically computationally and data intensive. EDO is also a multidisciplinary field which requires the collaboration of different domain experts to work on a design to yield improved versions. Grid Computing offers a suitable platform for design engineers to collaboratively work together and share knowledge and expertise in addition to the computational and data facility that can be combined to bear on complex designs. In this paper, the trend of Grid Computing evolution shows a clear emergence of application areas, starting from computational grid, data grid, visualisation grid and semantic grid to service-oriented problem solving environments (SO-PSE). This evolution is classified as first, second and third generation of Grid Computing for the purpose of understanding how researchers have tried to provide solutions to the problems and challenges in implementing Grid applications. The future of Grid Computing research areas such as autonomic computing, ubiquitous computing and economic Grid models as well as concurrent engineering design problem solving environments feature in the report. Autonomic computing enables grid services and resources to have self-management, self adjustable and adaptability to changing and dynamic situations using agent-based technology while ubiquitous computing allows computers to perceive the environment and act accordingly.
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    Grid Services for Multi-objective Optimisation
    (Cranfield University Press, 2009-03-31) Goteng, Gokop; Tiwari, Ashutosh; Roy, Rajkumar; Rajkumar Roy; Essam Shehab
    The emerging grid technology is defined as an infrastructure for secure and coordinated large-scale resource sharing. In this paper, we describe the architecture and grid services of DECGrid. DECGrid enables distributed design experts to collaborate and share resources during design optimisation. Mathematical models are built using services by experts. These models are then directly linked to NSGA-II optimisation algorithm service and allow design experts to enter design parameters of their choice. A real-life case study-welded beam problem was used to validate the prototype. The results obtained showed a wider spread in the solution space compared to the results in literature.