A compass-based hyper-heuristic for multi-objective optimization problems

Multi-objective selection hyper-heuristics have attracted more attention of researchers because of their crossdomain ability. However, for multi-objective optimization problems (MOPs), obtaining a manageable number of solutions that are well distributed and converged in the objective space is still a challenge, especially when solving high-dimensional MOPs. In order to solve this problem, this paper proposes a compass-based hyperheuristics(COHH), which is a general iterative framework that learns and selects from a set of meta-heuristics or components (named low-level heuristics, LLHs). The selected LLH is applied to solve the given MOP at the current iteration. In order to learn the potential of LLHs, the impact of the diversity of the current solution set on the final performance is studied. Then a new compass-based indicator is defined to evaluate the current solution sets. The learning strategy with new indicator can bias to diversity by adjusting the angle of a reference vector. After learning, the adaptive two-stage selection strategy triggered by the quality of the current solution set is used to choose LLH. Experiments are conducted on DTLZ, MaOP, WFG, and MaF test suites, as well as several real-world constrained test problems. Experimental results show that COHH is competitive in performance and cross-domain capability when compared with popular meta-heuristics and hyper-heuristics.