Spatial MultiObjective Land Use Optimization toward Livability Based on BoundaryBased Genetic Algorithm: A Case Study in Singapore In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multiobjective land use optimization modelling. In addition, a multiobjective land use optimization model was constructed using goal programming and a weightedsum approach, followed by a boundarybased genetic algorithm adapted to help address the spatial multiobjective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multiobjective land use optimization model developed in this research as well as the robustness and reliability of computergenerated solutions. In addition, the comparison between the computergenerated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.

A multiobjective optimisation evolutionary approach for the Multidimensional Scaling Problem The Multidimensional Scaling (MDS) strategies allow visualising the similarity between different objects reducing the number of dimensions. MDS has been widely used to perform exploratory analyses in different fields of the knowledge. The current strategies designed to deal with the MDS problem are able to consider exclusively one measure in a same time, however, most of the reallife problems usually require to analyse more than one measure simultaneously. The multiobjective optimisation techniques have been successfully used to deal with in problems from different areas considering multiples criteria (two or three criteria). In this work, we propose a genetic algorithm to deal with the multiobjective MDS problem being evaluated by using classical data sets from the related literature. The results show that the proposed strategy is able to identify a Pareto set of solutions that include new representations which were nondominated by solutions from the current state of the art singleobjective optimisation approaches, and new solutions which combine the features of the different inputs. These results make our proposal a real alternative to deal with problems which require to visualise different similarity inputs.