TY - GEN
T1 - The hypervolume indicator as a performance measure in dynamic optimization
AU - Oliveira, Sabrina
AU - Wanner, Elizabeth F.
AU - de Souza, Sérgio R.
AU - Bezerra, Leonardo C.T.
AU - Stützle, Thomas
PY - 2019/2/3
Y1 - 2019/2/3
N2 - In many real world problems the quality of solutions needs to be evaluated at least according to a bi-objective non-dominated front, where the goal is to optimize solution quality using as little computational resources as possible. This is even more important in the context of dynamic optimization, where quickly addressing problem changes is critical. In this work, we relate approaches for the performance assessment of dynamic optimization algorithms to the existing literature on bi-objective optimization. In particular, we introduce and investigate the use of the hypervolume indicator to compare the performance of algorithms applied to dynamic optimization problems. As a case study, we compare variants of a state-of-the-art dynamic ant colony algorithm on the traveling salesman problem with dynamic demands (DDTSP). Results demonstrate that our proposed approach accurately measures the desirable characteristics one expects from a dynamic optimizer and provides more insights than existing alternatives.
AB - In many real world problems the quality of solutions needs to be evaluated at least according to a bi-objective non-dominated front, where the goal is to optimize solution quality using as little computational resources as possible. This is even more important in the context of dynamic optimization, where quickly addressing problem changes is critical. In this work, we relate approaches for the performance assessment of dynamic optimization algorithms to the existing literature on bi-objective optimization. In particular, we introduce and investigate the use of the hypervolume indicator to compare the performance of algorithms applied to dynamic optimization problems. As a case study, we compare variants of a state-of-the-art dynamic ant colony algorithm on the traveling salesman problem with dynamic demands (DDTSP). Results demonstrate that our proposed approach accurately measures the desirable characteristics one expects from a dynamic optimizer and provides more insights than existing alternatives.
KW - Dynamic optimization
KW - Multi-objective optimization
KW - Performance assessment
UR - http://www.scopus.com/inward/record.url?scp=85063033985&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-12598-1_26
U2 - 10.1007/978-3-030-12598-1_26
DO - 10.1007/978-3-030-12598-1_26
M3 - Conference publication
AN - SCOPUS:85063033985
SN - 9783030125974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 319
EP - 331
BT - Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings
A2 - Mostaghim, Sanaz
A2 - Miettinen, Kaisa
A2 - Deb, Kalyanmoy
A2 - Goodman, Erik
A2 - Coello Coello, Carlos A.
A2 - Klamroth, Kathrin
A2 - Reed, Patrick
PB - Springer
T2 - 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019
Y2 - 10 March 2019 through 13 March 2019
ER -