As many-objective optimization algorithms mature, the problem owner is faced with visualizing and understanding a set of mutually nondominating solutions in a high dimensional space. We review existing methods and present new techniques to address this problem. We address a common problem with the well-known heatmap visualization, since the often arbitrary ordering of rows and columns renders the heatmap unclear, by using spectral seriation to rearrange the solutions and objectives and thus enhance the clarity of the heatmap. A multiobjective evolutionary optimizer is used to further enhance the simultaneous visualization of solutions in objective and parameter space. Two methods for visualizing multiobjective solutions in the plane are introduced. First, we use RadViz and exploit interpretations of barycentric coordinates for convex polygons and simplices to map a mutually nondominating set to the interior of a regular convex polygon in the plane, providing an intuitive representation of the solutions and objectives. Second, we introduce a new measure of the similarity of solutions - the dominance distance - which captures the order relations between solutions. This metric provides an embedding in Euclidean space, which is shown to yield coherent visualizations in two dimensions. The methods are illustrated on standard test problems and data from a benchmark many-objective problem.

This paper presents an artificial neural network-based model of domestic water consumption. The model is based on real-world data collected from smart meters, and represents a step toward being able to model real-time smart meter data. A range of input schemas are examined, including real meter readings and summary statistics derived from readings, and it is found that the models can predict some consumption but struggle to accurately match in cases of peak usage

River quality analysis is an important activity which, in Serbia, has been performed using the Serbian Water Quality Index (SWQI). This is a measure based on a weighted aggregation of 10 water quality parameters. In this work, alternative methods drawing on visualisation approaches used in multi-criterion decision analysis are applied to the problem of evaluating river quality in the Danube. Two methods are considered: one which constructs a graph using the dominance relation combined with a further multi-criterion ranking method, average rank, and the other in which the dimensionality of the data is reduced using PCA for visualisation. Results for data collected in 2010 are analysed and compared with the corresponding SWQI values for the river in that year, and we find that by employing these methods it is possible to reveal more information within the data than is possible by using SWQI alone.

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

Background: Multimorbidity, which is associated with significant negative outcomes for individuals and health care systems, is increasing in the United Kingdom. However, there is a lack of knowledge about the risk factors (including health, behavior, and environment) for multimorbidity over time. An interdisciplinary approach is essential, as data science, artificial intelligence, and engineering concepts (digital twins) can identify key risk factors throughout the life course, potentially enabling personalized simulation of life-course risk for the development of multimorbidity. Predicting the risk of developing clusters of health conditions before they occur would add clinical value by enabling targeted early preventive interventions, advancing personalized care to improve outcomes, and reducing the burden on health care systems. Objective: This study aims to identify key risk factors that predict multimorbidity throughout the life course by developing an intelligent agent using digital twins so that early interventions can be delivered to improve health outcomes. The objectives of this study are to identify key predictors of lifetime risk of multimorbidity, create a series of simulated computational digital twins that predict risk levels for specific clusters of factors, and test the feasibility of the system. Methods: This study will use machine learning to develop digital twins by identifying key risk factors throughout the life course that predict the risk of later multimorbidity. The first stage of the development will be the training of a base predictive model. Data from the National Child Development Study, the North West London Integrated Care Record, the Clinical Practice Research Datalink, and Cerner’s Real World Data will be split into subsets for training and validation, which will be done following the k-fold cross-validation procedure and assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). In addition, 2 data sets—the Early-Life Data Cross-linkage in Research study and the Children and Young People’s Health Partnership randomized controlled trial—will be used to develop a series of digital twin personas that simulate clusters of factors to predict different risk levels of developing multimorbidity. Results: The expected results are a validated model, a series of digital twin personas, and a proof-of-concept assessment. Conclusions: Digital twins could provide an individualized early warning system that predicts the risk of future health conditions and recommends the most effective intervention to minimize that risk. These insights could significantly improve an individual’s quality of life and healthy life expectancy and reduce population-level health burdens. International Registered Report Identifier (IRRID): PRR1-10.2196/35738

Hyper-heuristics have been used widely to solve optimisation problems, often single-objective and discrete in nature. Herein, we extend a recently-proposed selection hyper-heuristic to the multi-objective domain and with it optimise continuous problems. The MOSSHH algorithm operates as a hidden Markov model, using transition probabilities to determine which low-level heuristic or sequence of heuristics should be applied next. By incorporating dominance into the transition probability update rule, and an elite archive of solutions, MOSSHH generates solutions to multi-objective problems that are competitive with bespoke multi-objective algorithms. When applied to test problems, it is able to find good approximations to the true Pareto front, and yields information about the type of low-level heuristics that it uses to solve the problem.