Abstract This paper presents AUGMECON-Py, a Python framework for solving large and complex multi-objective linear programming problems under uncertainty, optimally robustly capturing all solutions. On the core of AUGMECON-Py software lies integration well-established optimisation algorithm (AUGMECON) with Monte Carlo analysis that helps maximise robustness against stochastic thereby av...

We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using ...

Objectives/Expected Outcomes: The unit is intended primarily to graduate students and senior undergraduate students with some background in linear algebra, and with basic knowledge of FORTRAN, C++ or Matlab. After completion of this unit, students will have a much deeper understanding of methods used in modern design optimisation for linear and non-linear problems. Such problems are becoming in...

In this paper, multi-objective and thermodynamic optimisation procedures are used to investigate the performance of a parabolic trough receiver with perforated plate inserts. Three dimensionless perforated plate geometrical parameters considered in the optimisation include the dimensionless orientation angle, the dimensionless plate diameter and the plate spacing per unit meter. The Reynolds nu...

Modern vehicles possess an increasing number of software and hardware components that are integrated in electronic control units (ECUs). Finding an optimal allocation for all components is a multi-objective optimisation problem, since every valid allocation can be rated according to multiple objectives like costs, busload, weight, etc. Additionally, several constraints mainly regarding the avai...

The classic NFL theorems are invariably cast in terms of single objective optimization problems. We confirm that the classic NFL theorem holds for general multiobjective fitness spaces, and show how this follows from a ‘single-objective’ NFL theorem. We also show that, given any particular Pareto Front, an NFL theorem holds for the set of all multiobjective problems which have that Pareto Front...

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to...