A Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problems




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Peer reviewed



olutions to real-world problems often require the simultaneous optimisation of multiple conflicting objectives. In the presence of four or more objectives, the problem is referred to as a “many-objective optimisation problem”. A problem of this category introduces many challenges, one of which is the effective and efficient selection of optimal solutions. The hypervolume indicator (or s-metric), i.e. the size of dominated objective space, is an effective selection criterion for many-objective optimisation. The indicator is used to measure the quality of a non- dominated set, and can be used to sort solutions for selection as part of the contributing hypervolume indicator. However, hypervolume based selection methods can have a very high, if not infeasible, computa- tional cost. The present study proposes a novel hypervolume driven selection mechanism for many-objective prob- lems, whilst maintaining a feasible computational cost. This approach, named the Hypervolume Adaptive Grid Algorithm (HAGA), uses two-phases (narrow and broad) to prevent population-wide calculation of the contributing hypervolume indicator. Instead, HAGA only calculates the contributing hypervolume indica- tor for grid populations, i.e. for a few solutions, which are close in proximity (in the objective space) to a candidate solution when in competition for survival. The result is a trade-off between complete accuracy in selecting the fittest individuals in regards to hypervolume quality, and a feasible computational time in many-objective space. The real-world efficiency of the proposed selection mechanism is demonstrated within the optimisation of a classifier for concealed weapon detection.


The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.


Multi-Objective Optimisation, Many-Objective Optimisation, Hypervolume Indicator, Selection Mechanism, Evolutionary Optimisation


Rostami, S. and Neri, F. (2017) A Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problems. Swarm and Evolutionary Computation, 34, pp. 50-67


Research Institute

Institute of Artificial Intelligence (IAI)