Future Challenges

Miettinen, Kaisa; Deb, Kalyanmoy; Jahn, Johannes; Ogryczak, Włodzimierz; Shimoyama, Koji; Vetschera, Rudolf

doi:10.1007/978-3-540-88908-3_16

Cited by 9 publications

(6 citation statements)

References 57 publications

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“…In public policy decisions the interests of multiple stakeholders have to be taken into account. Even when the decision is ultimately made by one individual, a politician, that individual has to consider the interests of different parties (Miettinen et al 2008 and references therein). For instance, multicriteria analysis was needed for exploring stakeholders preferences for diverse future energy technologies developed with the European Integrated Project NEEDS (Makowski et al 2009).…”

Section: Importance Weighting Of Partial Achievementsmentioning

confidence: 99%

Reference point method with importance weighted ordered partial achievements

Ogryczak

Kozłowski

2009

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Section: Importance Weighting Of Partial Achievementsmentioning

confidence: 99%

Reference point method with importance weighted ordered partial achievements

Ogryczak

Kozłowski

2009

TOP

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“…classical or evolutionary algorithms, design of experiment studies, machine/statistical learning and so forth. 19…”

Section: State Of the Artmentioning

confidence: 99%

“…20,21 Therefore, many-objective problems will often need to be solved, and this generic field has actually got increasing attention recently. 19,[22][23][24][25][26][27] Although current evolutionary MOO procedures are quite successful in solving two-or three-objective problems, they have some computational deficiencies in finding multiple and well spread solutions in the case of problems comprising more than three objectives. Besides improving the inefficiency of selection operators available in current evolutionary MOO algorithms (i.e.…”

Section: Future Challengesmentioning

confidence: 99%

“…For the process simulation models, the important issue here will be the quality of the solvers for the resulting algebraic equation systems as well as the efficiency of the numerical schemes used for iterations due to non-linearities, whereas for the optimisation part, the available MOO algorithms and software at hand will be determining, i.e. classical or evolutionary algorithms, design of experiment studies, machine/statistical learning and so forth 19…”

Section: State Of the Artmentioning

confidence: 99%

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Numerical optimisation of friction stir welding: Review of future challenges

Tutum

Hattel

2011

Science and Technology of Welding and Joining

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During the last decade, the combination of increasingly more advanced numerical simulation software with high computational power has resulted in models for friction stir welding (FSW), which have improved the understanding of the determining physical phenomena behind the process substantially. This has made optimisation of certain process parameters possible and has in turn led to better performing friction stir welded products, thus contributing to a general increase in the popularity of the process and its applications. However, most of these optimisation studies do not go well beyond manual iterations or limited automation. The present paper thus attempts to give a brief overview of some of the successful autonomous optimisation applications of FSW in combination with what determines the state of the art in the field. Finally, this is followed by a discussion of some of the trends and future challenges that we foresee in the rapidly expanding area of autonomous optimisation of FSW.

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“…Real-world applications normally involve uncertainties because of operating conditions or manufacturing process [1]. Robust optimization tries to find flexibility by its way of solving the latter problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive IDEA for Robust Multiobjective Optimization, Application to the r-TSALBP-m/A

Chica

Bautista

Damas

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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Abstract-Robust optimization tries to find flexible solutions when solving problems with uncertain scenarios and vague information. In this paper we present a multiobjective evolutionary algorithm to solve robust multiobjective optimization problems. This algorithm is a novel adaptive method able to evolve separate populations of robust and non-robust solutions during the search. It is based on the infeasibility driven evolutionary algorithm (IDEA) and uses an additional objective to evaluate the robustness of the solutions. The original and adaptive IDEAs are applied to solve the r-TSALBP-m/A, an assembly line balancing model that considers a set of demand production plans and includes robustness functions for measuring the temporal overloads of the stations of the assembly line with respect to the plans. Our results show that the proposed adaptive IDEA gets more robust non-dominated solutions for the problem. Also, we show that, for the case of the r-TSALBP-m/A, we can obtain Pareto fronts with a higher convergence by using the adaptive version of the algorithm.

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Future Challenges

Cited by 9 publications

References 57 publications

Reference point method with importance weighted ordered partial achievements

Reference point method with importance weighted ordered partial achievements

Numerical optimisation of friction stir welding: Review of future challenges

Adaptive IDEA for Robust Multiobjective Optimization, Application to the r-TSALBP-m/A

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