TY - GEN
T1 - Finding robust solutions to dynamic optimization problems
AU - FU, Haobo
AU - SENDHOFF, Bernhard
AU - TANG, Ke
AU - YAO, Xin
PY - 2013
Y1 - 2013
N2 - Most research in evolutionary dynamic optimization is based on the assumption that the primary goal in solving Dynamic Optimization Problems (DOPs) is Tracking Moving Optimum (TMO). Yet, TMO is impractical in cases where keeping changing solutions in use is impossible. To solve DOPs more practically, a new formulation of DOPs was proposed recently, which is referred to as Robust Optimization Over Time (ROOT). In ROOT, the aim is to find solutions whose fitnesses are robust to future environmental changes. In this paper, we point out the inappropriateness of existing robustness definitions used in ROOT, and therefore propose two improved versions, namely survival time and average fitness. Two corresponding metrics are also developed, based on which survival time and average fitness are optimized respectively using population-based algorithms. Experimental results on benchmark problems demonstrate the advantages of our metrics over existing ones on robustness definitions survival time and average fitness. © Springer-Verlag Berlin Heidelberg 2013.
AB - Most research in evolutionary dynamic optimization is based on the assumption that the primary goal in solving Dynamic Optimization Problems (DOPs) is Tracking Moving Optimum (TMO). Yet, TMO is impractical in cases where keeping changing solutions in use is impossible. To solve DOPs more practically, a new formulation of DOPs was proposed recently, which is referred to as Robust Optimization Over Time (ROOT). In ROOT, the aim is to find solutions whose fitnesses are robust to future environmental changes. In this paper, we point out the inappropriateness of existing robustness definitions used in ROOT, and therefore propose two improved versions, namely survival time and average fitness. Two corresponding metrics are also developed, based on which survival time and average fitness are optimized respectively using population-based algorithms. Experimental results on benchmark problems demonstrate the advantages of our metrics over existing ones on robustness definitions survival time and average fitness. © Springer-Verlag Berlin Heidelberg 2013.
KW - Evolutionary Dynamic Optimization
KW - Population-Based Search Algorithms
KW - Robust Optimization Over Time
UR - http://www.scopus.com/inward/record.url?scp=84875649813&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37192-9_62
DO - 10.1007/978-3-642-37192-9_62
M3 - Conference paper (refereed)
SN - 9783642371912
T3 - Lecture Notes in Computer Science
SP - 616
EP - 625
BT - Applications of Evolutionary Computing : 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013, Proceedings
A2 - ESPARCIA-ALCÁZAR, Anna I.
PB - Springer
T2 - 16th European Conference on the Applications of Evolutionary Computation
Y2 - 3 April 2013 through 5 April 2013
ER -