Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms.

The past five years have seen a rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. Using a real-world scenario from the Leeds Metropolitan Area as a case study, we demonstrate an effective way to combine macro-level mobility simulations based on open data (i.e., geographic information system information and transit timetables) with evolutionary optimisation techniques to discover realistic optimised integration routes for CAVs. The macro-level mobility simulations are used to assess the quality (i.e., fitness) of a potential CAV route by quantifying geographic accessibility improvements using an extended version of Dijkstra's algorithm on an abstract multi-modal transport network

DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm.

We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wide range of multi-objective optimization problems (MOOPs) as it successfully combines positive traits from three main classes of multi-objective evolutionary algorithms (MOEAs): classical approaches that use Pareto-based selection for survival criteria, approaches that rely on differential evolution, and decomposition-based strategies. A key part of our hybrid evolutionary approach lies in the proposed fitness sharing mechanism that is able to smoothly transfer information between the coevolved subpopulations without negatively impacting the specific evolutionary process behavior that characterizes each subpopulation. The proposed MOEA also features an adaptive allocation of fitness evaluations between the coevolved populations to increase robustness and favor the evolutionary search strategy that proves more successful for solving the MOOP at hand. Apart from the new evolutionary algorithm, this paper also contains the description of a new hypervolume and racing-based methodology aimed at providing practitioners from the field of multi-objective optimization with a simple means of analyzing/reporting the general comparative run-time performance of multi-objective optimization algorithms over large problem sets

A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines.

Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic features. The main strength of our approach is that it provides decision makers with a clear overview of the optimal trade-offs between data collection costs, the expected modelling errors and the overall explainability of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller deployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise regularisation technique that can be applied to outline domain-relevant mappings between LR variables and thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic thermal models when training on data associated with a limited set of load points within the safe operating area of the electrical machine under study

Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D.

For engineers to create durable and effective electrical assemblies, modelling and controlling heat transfer in rotating electrical machines (such as motors) is crucial. In this paper, we compare the performance of three multi-objective evolutionary algorithms, namely NSGA-II, NSGA-III, and MOEA/D in finding the best trade-offs between data collection costs/effort and expected modelling errors when creating low-complexity Linear Regression (LR) models that can accurately estimate key motor component temperatures under various operational scenarios. The algorithms are integrated into a multi-objective thermal modelling strategy that aims to guide the discovery of models that are suitable for microcontroller deployment. Our findings show that while NSGA-II and NSGA-III yield comparably good optimisation outcomes, with a slight, but statistically significant edge for NSGA-II, the results achieved by MOEA/D for this use case are below par

Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design.

This work presents a critical analysis of the suitability of surrogate models for finite element method application. A case study of a finite element method (FEM) structural problem was selected in order to test the performance of surrogate algorithms. A simple design of experiments (DoE) approach, based on 1D kernel density estimations, is employed to construct a representative pool of real FEM simulations, which becomes the dataset for five different surrogate models, two linear and three non-linear, whose most relevant hyperparameters were tuned (model selection). Results in a real bushing case study show that surrogate models can accurately mimic FEM simulations outcomes, in this case four types of stiffnesses (axial, radial, torsion, and cardanic)

Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems.

In the last two decades, multi-objective evolutionary algorithms (MOEAs) have become ever more used in scientific and industrial decision support and decision making contexts the require an a posteriori articulation of preference. The present work is focused on a comparative analysis of the performance of two master–slave parallelization (MSP) methods, the canonical generational scheme and the steady-state asynchronous scheme. Both can be used to improve the convergence speed of multi-objective evolutionary algorithms that must use computationally-intensive fitness evaluation functions. Both previous and present experiments show that a correct choice for one or the other parallelization method can lead to substantial improvements with regard to the overall duration of the optimization process. Our main aim is to provide practitioners of MOEAs with a simple but effective method of deciding which MSP option is better given the particularities of the concrete optimization process. This in turn, would give the decision maker more time for articulating preferences (i.e., more flexibility). Our analysis is performed based on 15 well-known MOOP benchmark problems and two simulation-based industrial optimization processes from the field of electrical drive design. For the first industrial MOOP, when comparing with a preliminary study, applying the steady-state asynchronous MSP enables us to achieve an overall speedup (in terms of total wall-clock computation time) of ≈25%. For the second industrial MOOP, applying the steady-state MSP produces an improvement of ≈12%. We focus our study on two of the best known and most widely used MOEAs: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2)

Multi-objective optimal design of obstacle-avoiding two-dimensional Steiner trees with application to ascent assembly engineering.

We present an effective optimization strategy that is capable of discovering high-quality cost-optimal solution for two-dimensional (2D) path network layouts (i.e., groups of obstacle-avoiding Euclidean Steiner trees) that, among other applications, can serve as templates for complete ascent assembly structures (CAA-structures). The main innovative aspect of our approach is that our aim is not restricted to simply synthesizing optimal assembly designs with regard to a given goal, but we also strive to discover the best trade-offs between geometric and domain-dependent optimal designs. As such, the proposed approach is centred on a variably constrained multi-objective formulation of the optimal design task and on an efficient co-evolutionary solver. The results we obtained on both artificial problems and realistic design scenarios based on an industrial test case empirically support the value of our contribution to the fields of optimal obstacle-avoiding path generation in particular and design automation in general

Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation.

We propose two surrogate-based strategies for increasing the convergence speed of multi-objective evolutionary algorithms (MOEAs) by stimulating the creation of high-quality individuals early in the run. Both offspring generation strategies are designed to leverage the fitness approximation capabilities of light-weight interpolation-based models constructed using an inverse distance weighting function. Our results indicate that for the two solvers we tested with, NSGA-II and DECMO2++, the application of the proposed strategies delivers a substantial improvement of early convergence speed across a test set consisting of 31 well-known benchmark problems

Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement.

Rosters are often used for real-world staff scheduling requirements. Multiple design factors such as demand variability, shift type placement, annual leave requirements, staff well-being and the placement of trainees need to be considered when constructing good rosters. In the present work we propose a metaheuristic-based strategy for designing optimal cyclical rosters that can accommodate uneven demand patterns. A key part of our approach relies on integrating an efficient optimal trainee placement module within the metaheuristic-driven search. Results obtained on a real-life problem proposed by the Port of Aberdeen indicate that by incorporating a demand-informed random rota initialisation procedure, our strategy can generally achieve high-quality end-of-run solutions when using relatively simple base solvers like simulated annealing (SA) and evolution strategies (ES). While ES converge faster, SA outperforms quality-wise, with both approaches being able to improve the man-made baseline

On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems.

While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimise the benefits of adding connected and autonomous vehicles (CAVs) to existing multi-modal transport systems. We propose a strategy that combines multi-objective evolutionary algorithms with macro-level mobility simulations based on publicly available data (i.e., Open Street Maps data sets and transit timetables) to automatically discover optimal cost-benefit trade-offs of introducing a new CAV-centred PT service to an existing transport system. The insightful results we obtained on a real-life case study aimed at improving the average commuting time in a district of the Leeds Metropolitan Area are very promising and indicative of our strategy’s great potential to support efficient data-driven public transport planning