Multi-objective worst case optimization by means of evolutionary algorithms

Book English OPEN
Branke, Jürgen ; Avigad, Gideon ; Moshaiov, Amiram
  • Publisher: WBS, University of Warwick
  • Subject: QA | HB

Many real-world optimization problems are subject to uncertainty. A possible goal is then to find a solution which is robust in the sense that it has the best worst-case performance over all possible scenarios. However, if the problem also involves mul- tiple objectives, which scenario is “best” or “worst” depends on the user’s weighting of the different criteria, which is generally difficult to specify before alternatives are known. Evolutionary multi-objective optimization avoids this problem by searching for the whole front of Pareto optimal solutions. This paper extends the concept of Pareto dominance to worst case optimization problems and demonstrates how evolu- tionary algorithms can be used for worst case optimization in a multi-objective setting.
  • References (33)
    33 references, page 1 of 4

    Avigad, G., A. Moshaiov, and N. Brauner (2005). MOEA-based approach to delayed decisions for robust conceptual design. In Applications of Evolutionary Computing, Volume 3449 of LNCS, pp. 584 - 589. Springer.

    Branke, J. (1998). Creating robust solutions by means of an evolutionary algorithm. In A. E. Eiben, T. Ba¨ck, M. Schoenauer, and H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature, Volume 1498 of LNCS, pp. 119-128. Springer.

    Branke, J. (2001a). Evolutionary Optimization in Dynamic Environments. Kluwer.

    Branke, J. (2001b). Reducing the sampling variance when searching for robust solutions. In L. S. et al. (Ed.), Genetic and Evolutionary Computation Conference (GECCO'01), pp. 235-242. Morgan Kaufmann.

    Branke, J., K. Deb, H. Dierolf, and M. Osswald (2004). Finding knees in multi-objective optimization. In Parallel Problem Solving from Nature, Number 3242 in LNCS, pp. 722-731. Springer.

    Buchholz, P. and A. Thu¨ mmler (2005). Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization. In M. E. Kuhl et al. (Eds.), Winter Simulation Conference, pp. 842-852. IEEE.

    Coello Coello, C. A., D. A. V. Veldhuizen, and G. B. Lamont (2002). Evolutionary Algorithms for Solving MultiObjective Problems. Kluwer.

    Das, I. (2000). Robustness optimization for constrained nonlinear programming problems. Engineering Optimization 32(5), 585-618.

    Daum, D., K. Deb, and J. Branke (2007). Reliability-based optimization for multiple constraints with evolutionary algorithms. In Congress on Evolutionary Computation, pp. ?? IEEE.

    Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley.

  • Metrics
    views in OpenAIRE
    views in local repository
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Warwick Research Archives Portal Repository - IRUS-UK 0 60
Share - Bookmark