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.

Benchmarking discrete optimization heuristics with IOHprofiler Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons carry the potential to provide insights into the strengths and weaknesses of different approaches, which can be leveraged into designing new algorithms. Carefully selected benchmark problems are also needed as training sets in the context of algorithm selection and configuration. With the ultimate goal to create a meaningful benchmark set for iterative optimization heuristics, we compile and assess in this work a selection of discrete optimization problems that subscribe to different types of fitness landscapes. All problems have been implemented and tested within IOHprofiler, our recently released software built to assess iterative heuristics solving combinatorial optimization problems. For each selected problem we compare performances of eleven different heuristics. Apart from fixedtarget and fixedbudget results for the individual problems, we also derive ECDF results for groups of problems. To obtain these, we have implemented an addon for IOHprofiler which allows aggregation of performance data across different benchmark functions.