Liger - An Open Source Integrated Optimization Environment

Although there exists a number of optimization frameworks only commercial and closed source software address, to an extent, real-world optimization problems and arguably these software packages are not very easy to use. In this work we introduce an open source integrated optimization environment which is designed to be extensible and have a smooth learning curve so that it can be used by the non-expert in industry. We call this environment, Liger. Liger is an application that is built about a visual programming language, by which optimization work-flows can be created. Additionally, Liger provides a communication layer with external tools, whose functionality can be directly integrated and used with native components. This fosters code reuse and further reduces the required effort on behalf of the practitioner in order to obtain a solution to the optimization problem. Furthermore, there exists a number of available algorithms which are fully configurable, however should the need arise new algorithms can also be created just as easily by reusing what we call operator nodes. Operator nodes perform specific tasks on a set, or a single solution. Lastly as visual exploration of the obtained solutions is essential for decision makers, we also provide state-of-the art visualization capabilities

3 Powertrain Applications Product Development,

Abstract. Evolutionary multicriteria optimization has traditionally concentrated on problems comprising 2 or 3 objectives. While engineering design problems can often be conveniently formulated as multiobjective optimization problems, these often comprise a relatively large number of objectives. Such problems pose new challenges for algorithm design, visualisation and implementation. Each of these three topics is addressed. Progressive articulation of design preferences is demonstrated to assist in reducing the region of interest for the search and, thereby, simplified the problem. Parallel coordinates have proved a useful tool for visualising many objectives in a two-dimensional graph and the computational grid and wireless Personal Digital Assistants offer technological solutions to implementation difficulties arising in complex system design.

Comparative Study of Multi/Many-Objective Evolutionary Algorithms on Hot Rolling Application

Handling multiple number of objectives in industrial optimization problems is a regular affair. The journey of development of evolutionary algorithms for handling such problems occurred in two phases. In the first phase, multi-objective optimization algorithms are developed that worked quite satisfactorily while finding Pareto set of solutions for two to three objectives. However, their success rates for finding the Pareto optimal solutions for higher number of objectives were limited which triggered the development of different sets of evolutionary algorithms under the name of many-objective optimization algorithms. In this work, we intend to compare the performance of these two classes of algorithms for an industrial hot rolling operation from a real-life steel plant. Several process, chemistry and geometry related parameters are modelled to yield different mechanical properties such as % elongation, ultimate tensile strength and yield strength of final hot rolled steel product through data-based techniques such as artificial neural networks (ANN) . Using this ANN model, the mechanical properties are maximized to obtain the Pareto trade-off solutions using both non-dominated sorting genetic algorithms II (NSGA-II) and many-objective evolutionary algorithm decomposition and dominance (MOEA/DD) and their solutions are compared using a suitable metric for identifying the extent of convergence and diversity. This kind of Pareto set provides a designer with ample of alternatives before choosing a solution for final implementation