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Multi-objective optimization problems involve several conflicting objectives that have to be optimized simultaneously. Generating a complete Pareto-optimal front (POF) can be computationally expensive or even infeasible, and for that reason there has been an enormous interest in using multi-objective evolutionary algorithms (MOEAs), which are known to generate a good approximation of the POF. MOEAs can be difficult to implement, and even for experienced optimization experts it can be a very time consuming task. For this reason several optimization libraries exist in the literature, providing off-the-shelf access to the most popular MOEAs. Some optimization libraries also provide a framework to design MOEAs. However, existing frameworks can be too stringent and do not provide sufficient flexibility for the design of more sophisticated MOEAs. To address this, a recently proposed optimization library, known as …