Real-world optimization problems often involve multiple conflicting objective functions to be optimized simultaneously. The optima to such multiobjective optimization problems are trade-off solutions constituting a so-called Pareto front. Practically, a few representative solutions are of interest for decision-making in lieu of the entire Pareto front as the latter is computationally and cognitively demanding to generate and analyze, respectively. However, finding a set of a few uniformly distributed solutions on the Pareto front is challenging for large-scale problems with irregularly-shaped Pareto fronts. We address this gap for a special type of problems inspired by a real-world case study in forest management. The proposed iterative Pareto representer uses an achievement scalarizing function to identify and prune certain search directions that are likely to yield duplicate or densely packed solutions. A subset selection technique is then used to identify the next search direction for achieving a relatively uniform distribution. Numerical experiments on 23 problems demonstrate the improved performance of the proposed approach over three alternate ones.