A MOPSO based on hyper-heuristic to optimize many-objective problems

Multi-Objective Problems (MOPs) presents two or more objective functions to be simultaneously optimized. MOPs presenting more than three objective functions are called Many-Objective Problems (MaOPs) and pose challenges to optimization algorithms. Multi-objective Particle Swarm Optimization (MOPSO) is a promising meta-heuristic to solve MaOPs. Previous works have proposed different leader selection methods and archiving strategies to tackle the challenges caused by MaOPs, however, selecting the most appropriated components for a given problem is not a trivial task. Moreover, the algorithm can take advantage by using a variety of methods in different phases of the search. The concept of hyper-heuristic emerges for automatically selecting heuristic components for effectively solve a problem. However few works on the literature apply hyper-heuristics on multi-objective optimizers. In this work, we use a simple hyper-heuristic to select leader and archiving methods during the search. Unlike other studies our hyper-heuristic is guided by the R2 indicator due to its good measuring characteristics and low computational cost. An experimental study was conducted to evaluate the ability of the proposed hyper-heuristic in guiding the search towards its preferred region. The study compared the performance of the H-MOPSO and its low-level heuristics used separately regarding the R2 indicator. The results show that the hyper-heuristic proposed is able to guide the search through selecting the right components in most cases.