This article aims to create a theoretical evaluation model based on decision support methods for the residential house construction materials and elements selection. The research and analyses of most of the scientists are invoked on individual elements and materials of the building, which have an impact on different aspects of sustainability. Meanwhile, the integrated model covering all the key...

In real-world multiobjective optimisation, where problem complexity is typically high, systems are increasingly reliant on distributed computation to reduce processing times. While a number of successful distributed techniques have been proposed, they are dependent on high degrees of commonality between communicating processes – requiring homogeneity with respect to platform, environment or imp...

a novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (wds) for the purpose of its hydraulic model calibration is presented. the problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. the objectives are to maximise the calibrated model accuracy and to minimise the number of sampling d...

Multi-objective optimisation is a popular approach for finding solutions to complex problems with large search spaces that reliably yields good results. However, the rise of cyber-physical systems, emerges new challenge noisy fitness functions, whose objective value given configuration non-deterministic, producing varying results on each execution. This leads an process based stochastically sam...

Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is partly due to re-emergence of bio-inspired modern artificial neural networks (ANNs) along with availability computation power, vast labelled data and ingenious human-based knowledge well optimisation approaches that can...