Real-world problems commonly require the simultaneous consideration of multiple, often conflicting, objectives. Solving a multiobjective optimisation problem (MOP) is concerned with finding an ideal set of tradeoff solutions which are close to and uniformly distributed across the optimal tradeoff surface. Convergence and diversity are thus essential requirements of multiobjective optimisers, which are sometimes also required to focus on pertinent areas of the search space. Evolutionary computation (EC) techniques are stochastic, population-based, global search techniques well suited for solving MOPs. However, EC techniques can often involve a large number of objective function calculations which can make the convergence towards optimal tradeoff surfaces computationally expensive. Additionally, in the evolutionary multiobjective optimization community, the bi-objective case is the most heavily studied. Conclusions drawn from such low-dimensional frameworks used to be generalized for all problems’ dimensions. Research, however, has shown that high-dimensional problems (> 3 objectives) can possess different characteristics. One of the most important challenges faced in such optimisation scenarios is the conflict between convergence and diversity of solutions. In this study, new approaches are proposed for enhancing the convergence and diversification capacities of some of the best multiobjective evolutionary optimisers (MOEAs). The inclusion of quality metrics as indicators is implemented as an approach for solving the conflict between solutions’ convergence and diversity in high-dimensional optimisation problems. Moreover, a convergence acceleration technique for MOEAs which exploits the objective space, where the goal and objectives lies, is devised and assessed. In the final part of the study, some established progressive preference articulation techniques are examined, and their utility for tackling MOPs is discussed from the viewpoint of the decision maker. Progressive preference articulation techniques are effective methods for supporting the decision maker in guiding the search into pertinent regions of interest and coping with the curse of dimensionality.