Multi-objective optimization of generalized reliability design problems using feature models—A concept for early design stages

Limbourg, Philipp; Kochs, Hans-Dieter

doi:10.1016/j.ress.2007.03.032

Cited by 33 publications

(18 citation statements)

References 13 publications

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“…The feature model approach applied by Limbourg and Kochs [16] on the related redundancy allocation problem falls into the category of feasibility preservation. The authors weigh the need to constrain the search space to meaningful areas against the risk of precluding the creation of potentially desirable solutions.…”

Section: A Constraintsmentioning

confidence: 99%

The automotive deployment problem: A practical application for constrained multiobjective evolutionary optimisation

Moser

Mostaghim

2010

IEEE Congress on Evolutionary Computation

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Abstract-State-of-the art constrained multiobjective optimisation methods are often explored and demonstrated with the help of function optimisation problems from these accounts. It is sometimes hard for practitioners to extract good approaches for practical problems. In this paper we apply an evolutionary algorithm to a factual problem with realistic constraints and compare the effects of different operators and constraint handling methods. We observe that in spite of an apparently very insular search space, we consistently obtain the best results when using a repair mechanism, effectively eliminating infeasible solutions. This runs contrary to some recommendations in the optimisation literature which propose penalty functions for search spaces where feasible solutions are sparse.

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Section: A Constraintsmentioning

confidence: 99%

The automotive deployment problem: A practical application for constrained multiobjective evolutionary optimisation

Moser

Mostaghim

2010

IEEE Congress on Evolutionary Computation

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“…For the model-based approaches, different quality evaluation techniques have been used, implied by the models used for specific quality attributes. As an example reliability block diagrams [57], [58], [63], [121], [136], [137], [149], [208], [217], discrete-time Markov chains [61], [96], [97], [165], [232], and fault trees [67], [184], [199] are used for reliability; queuing networks [35], [80], [143], [144], [168], [169], [171], [245], execution graphs [85], [107], [115], and discrete-time Markov chains [210] are used to evaluate performance; fault trees [8], [180], [184], [185], [201], [223] and binary decision diagrams [8], [185] are used for safety evaluation; continuous-time Markov chains [193], Markov decision processes [212], Petri-nets [194], and Markov reward models [165] are used for evaluation of a system's energy consumption. Quantitative metrics of the quality attributes are obtained by either mathematically analyzing or simulating the models.…”

Section: Mts(2)mentioning

confidence: 99%

“…Performance (84) ADL(4) [145], [160], [161], [170], ANY(2) [133], [153], CUSTOMAM(5) [90], [152], [196]- [198], OPTIMPL(3) ALLOCATION(20) [61], [83], [88], [121]- [123], [133], [153], [155], [160], [161], [164], [175]- [178], [186], [187], [192], [214], CLUSTERING(3) [67], [122], [214], COMPONENT SELECTION(12) [7], [18], [96], [97], [147], [149], [160], [161], [177], [178], [182], [232], HARDWARE PARAMETERS(2) [160], [161], HARDWARE REPLICATION(29) [31], [51]- [57], …”

Section: Qa Architecture Representation Quality Evaluation Degrees Ofmentioning

confidence: 99%

“…Evolutionary Algorithms (EAs) [18], [33], [37], [38], [41], [57]- [60], [71], [73], [76], [88], [92], [102], [108], [118], [128], [148], [149], [157]- [159], [161], [175], [196], [216], [221], [223], [227]- [229], [231], [237] are some of the most commonly used approximate methods in architecture optimization. EAs are seen as robust algorithms that exhibit approximately similar performance over a wide range of problems [98], hence their popularity in the software engineering domain.…”

Section: Qa Optimization Strategy Type Constraint Handlingmentioning

confidence: 99%

See 1 more Smart Citation

Software Architecture Optimization Methods: A Systematic Literature Review

Aleti

Bühnová

Grunske

et al. 2013

IIEEE Trans. Software Eng.

244

146

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Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Abstract-Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.

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“…The redundancy allocation problem (RAP, [10], [11]) deals with component selection, for cost and reliability optimization at system level. In these approaches (DSE, RAP), the problem is formalized as an optimization problem, and not with the MBSE approach.…”

Section: Related Workmentioning

confidence: 99%