A fast hypervolume driven selection mechanism for many-objective optimisation problems
Authors: Rostami, S. and Neri, F.
Journal: Swarm and Evolutionary Computation
Volume: 34
Pages: 50-67
ISSN: 2210-6502
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 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, 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.
https://eprints.bournemouth.ac.uk/26161/
Source: Scopus
A fast hypervolume driven selection mechanism for many-objective optimisation problems
Authors: Rostami, S. and Neri, F.
Journal: SWARM AND EVOLUTIONARY COMPUTATION
Volume: 34
Pages: 50-67
eISSN: 2210-6510
ISSN: 2210-6502
DOI: 10.1016/j.swevo.2016.12.002
https://eprints.bournemouth.ac.uk/26161/
Source: Web of Science (Lite)
A Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problems
Authors: Rostami, S. and Neri, F.
Journal: Swarm and Evolutionary Computation
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.
https://eprints.bournemouth.ac.uk/26161/
Source: Manual
A Fast Hypervolume Driven Selection Mechanism for Many-Objective Optimisation Problems.
Authors: Rostami, S. and Neri, F.
Journal: Swarm and Evolutionary Computation
Volume: 34
Issue: June
Pages: 50-67
ISSN: 2210-6502
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.
https://eprints.bournemouth.ac.uk/26161/
Source: BURO EPrints