Using multi-objective optimisation to integrate alpine regions in groundwater flow models

Multi-objective Optimisation with Multiple Preferred Regions

The typical goal in multi-objective optimization is to find a set of good and well-distributed solutions. It has become popular to focus on specific regions of the objective space, e.g., due to market demands or personal preferences. In the past, a range of different approaches has been proposed to consider preferences for regions, including reference points and weights. While the former techni...

Multi-objective optimisation using the Bees Algorithm

This paper describes the first application of the Bees Algorithm to multi-objective optimisation problems. The Bees Algorithm is a search procedure inspired by the way honey bees forage for food. A standard mechanical design problem, the design of a welded beam structure, was used to benchmark the Bees Algorithm. The results obtained show the robust performance of the Bees Algorithm.

Phylogenetic Inference using Evolutionary Multi-objective Optimisation

Evolutionary relationships among species are usually (i) illustrated by means of a phylogenetic tree and (ii) inferred by optimising some measure of fitness, such as the total evolutionary distance between species or the likelihood of the tree (given a model of the evolutionary process and a data set). The combinatorial complexity of inferring the topology of the best tree makes phylogenetic in...

Mapping Habitats in Alpine Regions Using Multi-Temporal RapidEye Data

This work aimed to investigate the potential of remote sensing to provide information on the spatial distribution of habitats in the Alpine region. Specifically, the performances of different classification methods, namely Maximum Likelihood (ML), Decision Tree (DT) and Support Vector Machine (SVM) were investigated for land-cover mapping using multitemporal RapidEye data. Results showed that S...

Multi-objective Binary Search Optimisation