This paper introduces a new method of partitioning the solution space of a multi-objective optimisation problem for parallel processing, called Efficient Projection Partitioning. This method projects solutions down into a single dimension, greatly reducing the cost of partitioning the search space. We test EPP on a variety of randomly generated multi-objective combinatorial optimisation problems. The results are compared with the state of the art in parallel partitioning, and we show that in all scenarios tested, our new algorithm performs significantly better. Our proposed method allows the generation of non-dominated sets of larger problems with more decision variables or objective functions through the use of highly parallel computational infrastructure. Source code is provided to allow others to utilise, build upon and improve the algorithm
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