Evolutionary-based techniques for real-life optimisation: development and testing
Test beds that are capable of controlled simulation of the features of real-life design optimisation problems are crucial for the systematic development of optimisation algorithms. The aim of this paper is to present such a test bed that enables the evaluation of evolutionary-based algorithms on a variety of cases, which is difficult to obtain from real-life examples. The paper begins by presenting the definition, classification and features of real-life design optimisation problems, based on the results of an industrial survey and a study of existing literature. This focuses the paper on the three primary features of real-life engineering design optimisation problems: multiple objectives, multiple interacting variables and constraints. The paper makes a brief analysis of the state-of-the-art evolutionary-based optimisation techniques. This highlights the need for developing test beds to guide further development of these techniques for solving real-life problems. The paper then presents an analysis of the existing test problems, and proposes a test bed capable of simulating the features of real-life optimisation problems in a systematic and controlled manner. Further, this test bed is applied to analyse the performance of an evolutionary-based algorithm, developed by the authors for real-life design optimisation. The paper finally discusses the limitations of this work and frames the corresponding future research activities.
real-life optimisation test bed genetic algorithms evolutionary computing test functions multi-objective optimisation variable interaction inseparable function interaction generalised regression genetic algorithm