Solving many-objective optimisation problems using partial dominance

Most optimisation problems have multiple, often conflicting, objectives. Due to the conflicting objectives, a single solution does not exist, and therefore, the goal of a multi-objective optimisation algorithm (MOA) is to find a set of optimal trade-off solutions. Pareto dominance is used to guide the search and compare the quality of two solutions of a multi-objective optimisation problem, where solutions equal in quality are referred to as being non-dominated. However, many-objective optimisation problems (MaOPs) have more than three objectives and the number of non-dominated solutions increases as the number of objectives increases. Therefore, Pareto dominance is no longer an effective approach to guide the search. Recently, a partial dominance approach has been proposed to address this problem. Preliminary results indicate that the partial dominance relation shows promise and scales well with increasing number of objectives. This article extends that study by incorporating the relation in another MOA, applying the relation at different frequencies and evaluating the performance of the relation against both the original MOAs and state-of-the-art algorithms. The results provide further evidence that the partial dominance relation is an efficient approach to solve MaOPs.

Automated summary

This article extends the study on the application of partial dominance relation in another multi-objective optimization algorithm and evaluates its performance. The results provide further evidence that the partial dominance relation is an efficient approach to solve multi-objective optimization problems.