Evaluation of Metaheuristic Algorithms for the Improvement of Sustainability in the Construction Area NECADA infrastructure supports the execution of a simulation model of buildings or urban areas, taking care of environmental directives and international standards in the design process. The aim of this simulation model is to optimize the entire life cycle of the system from the point of view of sustainability (environmental, social and economic impacts), taking care of the comfort and climate change to achieve a Nearly Zero Energy Building. Due to the huge amount of factors to be considered, the number of scenarios to be simulated is huge, hence the use of optimization and specifically heuristics, is needed to get an answer in a reasonable time. This project aims to analyze the accuracy of two of the most used metaheuristics in this area. To do so we base our analysis in an extensive dataset obtained from a brute force execution, which represents a typical dataset for this kind of problem.

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.