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ToolsRostami, S. and Neri, F., 2017. A Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problems. Swarm and Evolutionary Computation, 34 (June), 50-67.
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DOI: 10.1016/j.swevo.2016.12.002
Abstract
Solutions 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 nondominated 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, computational cost. The present study proposes a novel hypervolume driven selection mechanism for many-objective problems, 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 indicator 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.
| Item Type: | Article |
|---|---|
| ISSN: | 2210-6502 |
| Uncontrolled Keywords: | Multi-Objective Optimisation; Many-Objective Optimisation; Hypervolume Indicator; Selection Mechanism; Evolutionary Optimisation |
| Group: | Faculty of Science & Technology |
| ID Code: | 26161 |
| Deposited By: | Symplectic RT2 |
| Deposited On: | 13 Jan 2017 10:42 |
| Last Modified: | 14 Mar 2022 14:01 |
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A Fast Hypervolume Driven Selection Mechanism for Many-Objective
Optimisation Problems. (deposited 21 Dec 2016 10:10)
- A Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problems. (deposited 13 Jan 2017 10:42) [Currently Displayed]
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