Handling changes of performance requirements in multi-objective problems

Avigad, Gideon; Eisenstadt, Erella; Schuetze, Oliver

doi:10.1080/09544828.2011.630656

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…Note that the choice of the number of test points near a solution x 0 that have to be chosen in order to find a search direction merely depends on the number of objectives involved in the MOP, and not on the dimension of the parameter space. Finally, we intend to utilize the DS/DDS in other applications, e.g., in the context of changing market demands as described in [2].…”

Section: Discussionmentioning

confidence: 99%

The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algorithms

Lara¹,

Alvarado

Avigad

et al. 2013

Advances in Intelligent Systems and Computing

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Recently, the Directed Search Method has been proposed as a point-wise iterative search procedure that allows to steer the search, in any direction given in objective space, of a multi-objective optimization problem. While the original version requires the objectives' gradients, we consider here a possible modification that allows to realize the method without gradient information. This makes the novel algorithm in particular interesting for hybridization with set oriented search procedures, such as multi-objective evolutionary algorithms. In this paper, we propose the DDS, a gradient free Directed Search method, and make a first attempt to demonstrate its benefit, as a local search procedure within a memetic strategy, by integrating the DDS into the well-known algorithm MOEA/D. Numerical results on some benchmark models indicate the advantage of the resulting hybrid.

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Section: Discussionmentioning

confidence: 99%

The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algorithms

Lara¹,

Alvarado

Avigad

et al. 2013

Advances in Intelligent Systems and Computing

Self Cite

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“…There are only a few papers that consider switching cost. In [1,9,20,28], switching cost was considered as an objective in multi-objective problems. However, all of these works considered the switching cost as a separable and independent objective from the optimality objective function and the connection between these two objectives was not considered.…”

Section: Methods Considering Switching Costmentioning

confidence: 99%

“…A multi-objective problem was deined which considered the cost of the adaptation and the optimality while the adaptation takes place. In [1], the need for rapid, low-cost changes in a design, in response to changes in performance requirements, within multi-objective problems, was investigated. An algorithm called ROOT⁄SC [9] was designed for ROOT.…”

Section: Methods Considering Switching Costmentioning

confidence: 99%

Changing or keeping solutions in dynamic optimization problems with switching costs

Yazdani

Branke

Omidvar

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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“…Different concepts are then compared in the bi-objective domain of optimality-variability, to support a selection of one of them. Avigad et al (2011) introduced a different approach for concept-based optimization. In this study they considered the versatility of a family of designs to satisfy several requirements, expressed as a set of ROIs.…”

Section: Set Evaluation In Concept-based Optimizationmentioning

confidence: 99%

Active Robust Optimization: Optimizing for Robustness of Changeable Products

Salomon

2019

Springer Theses

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To succeed in a demanding and competitive market, great attention needs to be given to the process of product design. Incorporating optimization into the process enables the designer to find high-quality products according to their simulated performance. However, the actual performance may differ from the simulation results due to a variety of uncertainty factors. Robust optimization is commonly used to search for products that are less affected by the anticipated uncertainties. Changeability can improve the robustness of a product, as it allows the product to be adapted to a new configuration whenever the uncertain conditions change. This ability provides the changeable product with an active form of robustness.Several methodologies exist for engineering design of changeable products, none of which includes optimization. This study presents the Active Robust Optimization (ARO) framework that offers the missing tools for optimizing changeable products. A new optimization problem is formulated, named Active Robust Optimization Problem (AROP). The benefit in designing solutions by solving an AROP lies in the realistic manner adaptation is considered when assessing the solutions' performance.The novel methodology can be applied to optimize any product that can be classified as a changeable product, i.e., it can be adjusted by its user during normal operation. This definition applies to a huge variety of applications, ranging from simple products such as fans and heaters, to complex systems such as production halls and transportation systems. The ARO framework is described in this dissertation and its unique features are studied. Its ability to find robust changeable solutions is examined for different sources of uncertainty, robustness criteria and sampling conditions. Additionally, a framework for Active Robust Multi-objective Optimization is developed. This generalisation of ARO itself presents many challenges, not encountered in previous studies. Novel approaches for evaluating and comparing changeable designs comprising multiple objectives are proposed along with algorithms for solving multi-objective AROPs.The framework and associated methodologies are demonstrated on two applications from different fields in engineering design. The first is an adjustable optical table, and the second is the selection of gears in a gearbox.iii Statement of OriginalityUnless otherwise stated in the text, the work described in this thesis was carried out solely by the candidate. None of this work has already been

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Handling changes of performance requirements in multi-objective problems

Cited by 9 publications

References 33 publications

The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algorithms

The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algorithms

Changing or keeping solutions in dynamic optimization problems with switching costs

Active Robust Optimization: Optimizing for Robustness of Changeable Products

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