Picture of model of urban architecture

Open Access research that is exploring the innovative potential of sustainable design solutions in architecture and urban planning...

Strathprints makes available scholarly Open Access content by researchers in the Department of Architecture based within the Faculty of Engineering.

Research activity at Architecture explores a wide variety of significant research areas within architecture and the built environment. Among these is the better exploitation of innovative construction technologies and ICT to optimise 'total building performance', as well as reduce waste and environmental impact. Sustainable architectural and urban design is an important component of this. To this end, the Cluster for Research in Design and Sustainability (CRiDS) focuses its research energies towards developing resilient responses to the social, environmental and economic challenges associated with urbanism and cities, in both the developed and developing world.

Explore all the Open Access research of the Department of Architecture. Or explore all of Strathclyde's Open Access research...

A reinforcement learning based hybrid evolutionary algorithm for ship stability design

Turan, Osman and Cui, Hao (2011) A reinforcement learning based hybrid evolutionary algorithm for ship stability design. In: Variants of Evolutionary Algorithms for Real-World Applications. Springer Berlin Heidelberg, Berlin, pp. 281-303. ISBN 9783642234231

Full text not available in this repository. Request a copy from the Strathclyde author


Over the past decades, various search and optimisation methods have been used for ship design – a dynamic and complicated process. While several advantages of using these methods have been demonstrated, one of the main limiting factors of optimisation applications in ship design is the high runtime requirement of the involved simulations. This severely restricts the number of real applications in this area. This chapter presents a hybrid evolutionary algorithm that uses reinforcement learning to guide the search. Through giving and correcting the search direction, the runtime of optimisation can be effectively reduced. The NSGA-II, a well known multi-objective evolutionary algorithm, is utilised together with reinforcement learning to form the hybrid approach. As an important optimisation application field, the ship stability design problem has been selected for evaluating the performance of this new method. A Ropax (roll on/roll off passenger ship) damage stability problem is selected as a case study to demonstrate the effectiveness of the proposed approach.