Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

Abstract

Abstract. The use of hyper-heuristics is increasing in the multi-objective optimisation domain, and the next logical advance in such methods is to use them in the solution of many-objective problems. Such problems comprise four or more objectives and are known to present a significant challenge to standard dominance-based evolutionary algorithms. We incorporate three comparison operators as alternatives to dominance and investigate their potential to optimise many-objective problems with a hyper-heuristic from the literature. We discover that the best results are obtained using either the favour relation or hypervolume, but conclude that changing the comparison operator alone will not allow for the generation of estimated Pareto fronts that are both close to and fully cover the true Pareto front.

Bio-inspired computation: Where we stand and what's next

“…Her proposed approach controls and combines the strengths of three well-known bio-inspired multi-objective solvers (NSGA-II, SPEA2, and MOGA), which are adopted as her low-level heuristics. The indicatorbased multi-objective sequence-based hyper-heuristic (MOSSHH) algorithm proposed in [129] was the first attempt to use a hyper-heuristic in many-objective problems. The study compares three indicators (one based on average rank, the hypervolume and the favour relation) with Pareto dominance in many-objective problems.…”

Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

“…At each iteration, the child replaces the parent if the former dominates the second. However, in a further paper [167], this comparison rule was changed by strategies based on the hypervolume indicator [182], the favour relation [44] and the average rank [10]. Moreover, the hidden Markov model is updated if the child is added to the archive and if it was better than the parent.…”