An Evolutionary Approach to Active Robust Multiobjective Optimisation

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• 作者：Shaul Salomon (16)
  Robin C. Purshouse (16)
  Gideon Avigad (17)
  Peter J. Fleming (16)
  
  16. Department of Automatic Control and Systems Engineering ; University of Sheffield ; Mappin Street ; Sheffield ; S1 3JD ; UK
  17. Department of Mechanical Engineering ; ORT Braude College of Engineering ; Karmiel ; Israel
  
• 关键词：Robust optimisation ; Uncertainties ; Multi ; objective optimisation ; Adaptation ; Gearbox ; Design
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9019
• 期：1
• 页码：141-155
• 全文大小：294 KB
• 参考文献：1. Salomon, S, Avigad, G, Fleming, PJ, Purshouse, RC (2014) Active Robust Optimization - Enhancing Robustness to Uncertain Environments. IEEE Transactions on Cybernetics 44: pp. 2221-2231 CrossRef
  2. Beyer, HG, Sendhoff, B (2007) Robust Optimization - A Comprehensive Survey. Computer Methods in Applied Mechanics and Engineering 196: pp. 3190-3218 CrossRef
  3. Branke, J, Rosenbusch, J New approaches to coevolutionary worst-case optimization. In: Rudolph, G, Jansen, T, Lucas, S, Poloni, C, Beume, N eds. (2008) Parallel Problem Solving from Nature 鈥?PPSN X. Springer, Heidelberg, pp. 144-153 CrossRef
  4. Avigad, G, Coello, CA (2010) Highly Reliable Optimal Solutions to Multi-Objective Problems and Their Evolution by Means of Worst-Case Analysis. Engineering Optimization 42: pp. 1095-1117 CrossRef
  5. Alicino, S., Vasile, M.: An evolutionary approach to the solution of multi-objective min-max problems in evidence-based robust optimization. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1179鈥?186 (2014)
  6. Teich, J Pareto-front exploration with uncertain objectives. In: Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, Corne, D eds. (2001) Evolutionary Multi-Criterion Optimization. Springer, Heidelberg, pp. 314-328 CrossRef
  7. Hughes, EJ Evolutionary multi-objective ranking with uncertainty and noise. In: Zitzler, E, Thiele, L, Deb, K, Coello Coello, CA, Corne, D eds. (2001) Evolutionary Multi-Criterion Optimization. Springer, Heidelberg, pp. 329-343 CrossRef
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  12. Bui, L.T., Abbass, H.A., Essam, D.: Fitness inheritance for noisy evolutionary multi-objective optimization. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 779鈥?85, New York. ACM (2005)
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  16. Paenke, I, Branke, J, Jin, Y (2006) Efficient Search for Robust Solutions by Means of Evolutionary Algorithms and Fitness Approximation. IEEE Transactions on Evolutionary Computation 10: pp. 405-420 CrossRef
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  18. Fleming, PJ, Purshouse, RC, Lygoe, RJ Many-objective optimization: an engineering design perspective. In: Coello Coello, CA, Hern谩ndez Aguirre, A, Zitzler, E eds. (2005) Evolutionary Multi-Criterion Optimization. Springer, Heidelberg, pp. 14-32 CrossRef
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  22. Salomon, S, Avigad, G, Goldvard, A, Sch眉tze, O PSA 鈥?a new scalable space partition based selection algorithm for MOEAs. In: Sch眉tze, O, Coello Coello, CA, Tantar, A-A, Tantar, E, Bouvry, P, Del Moral, P, Legrand, P eds. (2012) EVOLVE - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation II. Springer, Heidelberg, pp. 137-151 CrossRef
  
• 作者单位：Evolutionary Multi-Criterion Optimization
• 丛书名：978-3-319-15891-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

An Active Robust Optimisation Problem (AROP) aims at finding robust adaptable solutions, i.e. solutions that actively gain robustness to environmental changes through adaptation. Existing AROP studies have considered only a single performance objective. This study extends the Active Robust Optimisation methodology to deal with problems with more than one objective. Once multiple objectives are considered, the optimal performance for every uncertain parameter setting is a set of configurations, offering different trade-offs between the objectives. To evaluate and compare solutions to this type of problems, we suggest a robustness indicator that uses a scalarising function combining the main aims of multi-objective optimisation: proximity, diversity and pertinence. The Active Robust Multi-objective Optimisation Problem is formulated in this study, and an evolutionary algorithm that uses the hypervolume measure as a scalarasing function is suggested in order to solve it. Proof-of-concept results are demonstrated using a simplified gearbox optimisation problem for an uncertain load demand.