Interaction among decision variables is inherent to a number of real-life engineering design optimisation problems. The aim of this paper is to analyse multi-objective optimisation problems from the perspective of inseparable function interaction. In spite of its immense potential for real-life problems, lack of systematic research has plagued the field of interaction for a long time. The paper attempts to fill this gap by devising a formal definition and classification of interaction. It then uses this analysis as a background for identifying the challenges that interaction poses for optimisation algorithms. A number of existing test problems are also listed and analysed in this paper. The paper uses the viewpoint of inseparable function interaction developed here to devise a solution strategy and to propose an algorithm capable of handling complex multi-objective optimisation problems. The performance of the proposed algorithm is compared to that of a high performing evolutionary-based multi-objective optimisation algorithm, NSGA-II, using three test problems chosen from a set of existing problems listed and analysed in this paper. The paper concludes by giving the current limitations of the proposed algorithm and the future research directions.
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