Article

Finding robust solutions to dynamic optimization problems

Authors:

Haobo Fu,

Bernhard Sendhoff,

Ke Tang,

Xin YaoAuthors Info & Claims

EvoApplications'13: Proceedings of the 16th European conference on Applications of Evolutionary Computation

Pages 616 - 625

https://doi.org/10.1007/978-3-642-37192-9_62

Published: 03 April 2013 Publication History

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Abstract

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.

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Cited By

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Yazdani DBranke JOmidvar MNguyen TYao XTakadama K(2018)Changing or keeping solutions in dynamic optimization problems with switching costsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205484(1095-1102)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205484
Jiang SYang S(2017)A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2016.257462121:1(65-82)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/TEVC.2016.2574621
Huang YDing YHao KJin Y(2017)A multi-objective approach to robust optimization over time considering switching costInformation Sciences: an International Journal10.1016/j.ins.2017.02.029394:C(183-197)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.02.029
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Finding robust solutions to dynamic optimization problems
1. Information systems
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    1. Database administration
2. Theory of computation
  1. Design and analysis of algorithms

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EvoApplications'13: Proceedings of the 16th European conference on Applications of Evolutionary Computation

April 2013

635 pages

ISBN:9783642371912

Editor:
Anna I. Esparcia-Alcázar
S2 Grupo, C/ Ramiro de Maeztu 7, Valencia, Spain

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 03 April 2013

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Yazdani DBranke JOmidvar MNguyen TYao XTakadama K(2018)Changing or keeping solutions in dynamic optimization problems with switching costsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205484(1095-1102)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205484
Jiang SYang S(2017)A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2016.257462121:1(65-82)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/TEVC.2016.2574621
Huang YDing YHao KJin Y(2017)A multi-objective approach to robust optimization over time considering switching costInformation Sciences: an International Journal10.1016/j.ins.2017.02.029394:C(183-197)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.02.029
Yao XAlba E(2013)Challenges and opportunities in dynamic optimisationProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2480771(1761-1762)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2480771

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Analysis of fitness landscape modifications in evolutionary dynamic optimization

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