Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

Tavakkoli–Moghaddam, Reza; Safari, Jalal; Sassani, Farrokh

doi:10.1016/j.ress.2007.02.009

Cited by 263 publications

(145 citation statements)

References 12 publications

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“…Tavakkoli- Moghaddam et al (2008) used a genetic algorithm to solve the redundancy problem. Chambari et al (2012) studied a specific type of redundancy allocation problem (RAP) and developed a bi-objective RAP (BORAP).…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient optimization of multi-objective redundancy allocation problems in series-parallel systems

Arjestan¹

2017

10.5267/j.dsl

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Section: Literature Reviewmentioning

confidence: 99%

Efficient optimization of multi-objective redundancy allocation problems in series-parallel systems

Arjestan¹

2017

10.5267/j.dsl

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“…Alternatively, evolutionary algorithms are meta-heuristics that possess the strong capability of global search, and are usually not very sensitive to initial solutions. Their effectiveness has been demonstrated on a large spectrum of problems in the reliability optimization field, such as resource management and task partition in grid systems [19]- [21], redundancy allocation [22], [23], reliability optimization of weighted voting systems [24], and OTRAPs [7]- [12], [15], [17], [18]. In particular, evolutionary algorithms have been reported to perform better than some other techniques on OTRAPs [7], [9]- [12], [15], [17], [18].…”

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

2010

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Abstract-Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based Multi-Objective Evolutionary Algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs.Index Terms-Multi-objective evolutionary algorithm, parallelseries modular software system, software engineering, software reliability, software testing, star-structure modular software system. ACRONYMS

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“…Moreover, the redundancy strategy is considered as an additional decision variable in RAPs for first time in [10]. Also, Tavakkoli-Moghaddam et al [11] solved Coit (2003) model using GA with a new definition of chromosomes, crossovers and mutations. Also, there are many papers in RAP scope which used meta-heuristic methods like [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

et al. 2018

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Reliability improvement for electronics and mechanical systems is vital for engineers in order to design of these systems. For this reason, there are many researches in this scope to help engineers in real world applications. One of the useful methods in reliability optimization is redundancy allocation problem (RAP). In the most previous works, the failure rates of system components are considered to be constant based on negative exponential distribution; whereas, nearly all systems in real world have components with time-dependent failure rates; i.e., the failure rates of system components will be changed time by time. In this paper, we have worked on a RAP for a system under k-out-of-n subsystems with time-dependent components failure rates based on Weibull distribution. Also, the redundancy policy of the proposed system is considered as mixed strategy and the optimization method was based on the simulation technique to obtain reliability function as implicit function. Finally, a branch and bound algorithm has been used to solve the model, exactly.

show abstract

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

Cited by 263 publications

References 12 publications

Efficient optimization of multi-objective redundancy allocation problems in series-parallel systems

Efficient optimization of multi-objective redundancy allocation problems in series-parallel systems

Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

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