Using reference points to update the archive of MOPSO algorithms in Many-Objective Optimization
Many-Objective Optimization Problems are problems that have more than three objective functions. In general, Multi-Objective Evolutionary Algorithms scale poorly when the number of objectives increases. To overcome this limitation, in a previous study, a new MOPSO algorithm called I-MOPSO was proposed. In this study, this work is extended, and we seek to achieve two goals. The first goal is to perform an in-depth evaluation of the I-MOPSO algorithm in different many-objective scenarios. Two versions of this algorithm are studied: I-MOPSO and I-SIGMA. The second goal is to generalize the I-MOPSO algorithm; the new version is called REF-I-MOPSO, and it uses a new archiving method that guides the search in the algorithm to different regions of the Pareto Front using reference points. Two variants of this algorithm are presented: REF_M and REF_Ex. All these algorithms are evaluated with several Many-Objective Problems in terms of their convergence and diversity to the Pareto front. Additionally, we present an empirical analysis that aims to analyze the distribution of the solutions that are generated by the REF-I-MOPSO algorithm. The results showed that the solutions generated by this algorithm were close to the selected reference point. Furthermore, the results of REF-I-MOPSO were notably similar to I-MOPSO.
many-objective optimization archiving methods particle swarm optimization