A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms

Bossek Jakob, Kerschke Pascal, Trautmann Heike


Abstract
We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume (HV)indicator commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers.

Keywords
Algorithm selection, Multi-objective optimization, Performance measurement, Combinatorial optimization, Traveling Salesperson Problem



Publication type
Article in Journal

Peer reviewed
Yes

Publication status
Published

Year
2020

Journal
Applied Soft Computing

Volume
2020

Issue
88

Pages range
105901

ISSN
1568-4946

DOI

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