Genetic algorithm based multi-objective reliability optimization in interval environment

Sahoo, Laxminarayan; Bhunia, Asoke Kumar; Kapur, P. K.

doi:10.1016/j.cie.2011.09.003

Cited by 104 publications

(36 citation statements)

References 29 publications

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“…According to Sahoo et al (2012) while most reliability optimization problems have been formulated into the single objective optimization approach, it can be recognized that most real-world design problems involving reliability optimization require a broader perspective, and that is done by optimizing simultaneously more than one objective function.…”

Section: Introductionmentioning

confidence: 99%

Design Selection Decisions

Almeida

Cavalcante

Alencar

et al. 2015

International Series in Operations Research &Amp; Management Science

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Abstract:The design selection problem in the RRM context considers long-term performance, and represents higher additional costs if unforeseen features that should have been included during the project design phase had to be implemented afterwards. Design decision involves multiple aspects and may be more critical depending on the kind of item, such as consumer appliances, industrial equipment or projects that have to consider safety aspects (airplanes or facilities). Reliability has an essential role for design selection although other aspects have to be considered such as maintainability and risk depending on the specific design problem. Therefore, a multidimensional approach is usually required. In this chapter, all these aspects are discussed in order to illustrate the importance of a broader perspective when facing design decision problems. The fundamental requirements are to consider reliability, maintainability and risk aspects so as to establish features in the design project, including the definition of material, redundancies, control systems and safety barriers. To illustrate these decisions, aspects such as reliability (e.g. MTBF), maintainability (e.g. MTTR), safety, cost, service life, efficiency, are discussed as criteria for these problems. Multi-attribute utility theory (MAUT) is applied in this chapter to illustrate how reliability, maintainability and risk aspects are included in an MCDM/A model for design selection incorporating states of nature. The decision regarding the selection of which features to include in a design project may be considered as an MCDM/A portfolio problem. Finally, an introductory view is given of how the redesign problem arises in the maintenance context with multicriteria approaches.

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Section: Introductionmentioning

confidence: 99%

Design Selection Decisions

Almeida

Cavalcante

Alencar

et al. 2015

International Series in Operations Research &Amp; Management Science

View full text Add to dashboard Cite

show abstract

“…Solving such kinds of problems is extremely difficult in the context of optimization, due to expensively computational cost. The main achievements focus on studying model transformation on either general or engineering INP problems and intelligent optimization approaches solving them [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Efficient micro immune optimization approach solving constrained nonlinear interval number programming

Zhang

Tao

2015

Appl Intell

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This work investigates a possibility degree-based micro immune optimization approach to seek the optimal solution of nonlinear interval number programming with constraints. Such approach is designed under the guideline of the theoretical results acquired in the current work, relying upon interval arithmetic rules, interval order relation and immune theory. It involves in two phases of optimization. The first phase, based on a new possibility degree approach, assumes searching efficient solutions of natural interval extension optimization. This executes five modules -constraint bound handling, population division, dynamic proliferation, mutation and selection, with the help of a varying threshold of interval bound. The second phase collects the optimal solution(s) from these efficient solutions after optimizing the bounds of their objective intervals, in terms of the theoretical results. The numerical experiments illustrated that such approach with high efficiency performs well over one recent nested genetic algorithm and is of potential use for complex interval number programming.

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“…Sahoo et al [5] state that mutation rate generally ranges between 0.05 and 0.02, but that sometimes is calculated as 1/n, where n is the number of chromosomal genes. Mutation rate is an important parameter of genetic algorithms, because, as Cao et al show in [6] and Maaita et al in [7], too many or too few mutations adversely affect convergence.…”

Section: Related Workmentioning

confidence: 99%

Translation of the Mutation Operator from Genetic Algorithms to Evolutionary Ontologies

Contras¹,

Matei²

2016

ijacsa

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Abstract-Recently introduced, evolutionary ontologies represent a new concept as a combination of genetic algorithms and ontologies. We have defined a new framework comprising a set of parameters required for any evolutionary algorithm, i.e. ontological space, representation of individuals, the main genetic operators such as selection, crossover, and mutation. Although a secondary operator, mutation proves its importance in creating and maintaining evolutionary ontologies diversity. Therefore, in this article, we widely debate the mutation topic in evolutionary ontologies, marking its usefulness in practice by experimental results. Also we introduce a new mutation operator, called relational mutation, concerning mutation of a relationship through its inverse.

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Genetic algorithm based multi-objective reliability optimization in interval environment

Cited by 104 publications

References 29 publications

Design Selection Decisions

Design Selection Decisions

Efficient micro immune optimization approach solving constrained nonlinear interval number programming

Translation of the Mutation Operator from Genetic Algorithms to Evolutionary Ontologies

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