Ant colony algorithm for automotive safety integrity level allocation

Gheraibia, Youcef; Djafri, Khaoula; Krimou, Habiba

doi:10.1007/s10489-017-1000-6

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…These models could stimulate computer scientists using household nontraditional tools to solve the application problems [3]. Now a lot of swarm intelligence optimization algorithms are proposed, such as particle swarm optimization (PSO) [4], ant colony algorithm (ACO) [5], bat algorithm (BA) [6], social learning optimization (SLO) algorithm [7], and chicken swarm optimization (CSO) algorithm [8]. They can be used in the dictionary learning remote sensing data, automotive safety integrity level positioning, economic dispatch, composition, and examples of the Cloud Service Composition of QOS awareness.…”

Section: Introductionmentioning

confidence: 99%

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

Wang

2018

Journal of Electrical and Computer Engineering

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The bat algorithm (BA) is a new bionic intelligent optimization algorithm to simulate the foraging behavior and the echolocation principle of the bats. The parameter initialization of the discussed binary bat algorithm (BBA) has important influence on the convergence speed, convergence precision, and good global searching ability of the BBA. The convergence speed and algorithm searching precision are determined by the pulse of loudness and pulse rate. The simulation experiments are carried out by using the six typical test functions to discuss this influence. The simulation results show that the convergence speed of the BBA is relatively sensitive to the setting of the algorithm parameters. The convergence precision reduces when increasing the rate of bat transmitted pulse alone and the convergence speed increases the launch loudness alone. The proper combination of BBA parameters (the rate of bat transmitted pulse and the launch loudness) can flexibly improve the algorithm's convergence velocity and improve the accuracy of the searched solutions.

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Section: Introductionmentioning

confidence: 99%

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

Wang

2018

Journal of Electrical and Computer Engineering

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“…Jang and Kim [16] considered a redundancy allocation problem (RAP) in series-parallel to select optimal redundancy solution for components and modules of a system by tabu search. Gheraibia et al [17] solved an automotive SIL allocation problem by applying an ant colony algorithm to maximize safety requirements and minimize costs. Yildiz [18] presented a comparative study of state-of-the-art optimization techniques, such as the hybrid technique, for solving multi-pass turning operation problems.…”

Section: Related Workmentioning

confidence: 99%

“…Eqs. (16)(17)(18) are inserted in the model to retain the linearity of the objective function and its constraints. Eq.…”

Section: Sffmentioning

confidence: 99%

An Optimal Design Procedure based on the Safety Integrity Level for Safety-related Systems

Kim¹,

Kim²

2018

KSII TIIS

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Safety-related systems (SRSs) has widely used in shipbuilding and power generation to prevent fatal accidents and to protect life and property. Thus, SRS performance is a high priority. The safety integrity level (SIL) is the relative performance level of an SRS with regard to its ability to operate reliably in a safe manner. In this article, we proposed an optimal design procedure to achieve the targeted SIL of SRSs. In addition, a more efficient failure mode and effects diagnostic analysis (FMEDA) process and optimization model were developed to improve cost efficiency. Based on previous IEC 61508 diagnostic analyses that revealed unnecessary costs associated with excessive reliability, the new approach consists of two phases: (i) SIL evaluation by FMEDA, and (ii) solution optimization for achieving the target SIL with minimal cost using integer-programming models. The proposed procedure meets the required safety level and minimizes system costs. A case study involving a gas-detection SRS was conducted to demonstrate the effectiveness of the new procedure.

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“…Recently, a methodology has been proposed by Gheraibia et al [55] for ASILs allocation problem. This method extends the work presented in [56] and uses the Ant Colony Optimization (ACO) [57,58] to allocate safety requirements.…”

Section: Ant Colony Algorithmmentioning

confidence: 99%

An Overview of the Approaches for Automotive Safety Integrity Levels Allocation

Gheraibia

Kabir

Djafri

et al. 2018

J Fail. Anal. and Preven.

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ISO 26262, titled Road Vehicles-Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to accomplish the goal of designing and developing dependable automotive systems, ISO 26262 uses the concept of Automotive Safety Integrity Levels (ASILs), the adaptation of Safety Integrity Levels. ASILs are allocated to the components and subsystems that can cause system failure and malfunctions that lead to hazards. ASILs allocation is a hard problem consists of finding the optimal allocation of safety levels to the system architecture which must guarantee that the highest safety requirements are met while development cost of the automotive system is kept minimum. There were many successful attempts to solve this problem using different techniques. However, it is worth pointing out that there is an absence of a review that provides an in-depth study of all the existing methods and highlights their merits and demerits. This paper presents an overview of different approaches that were used to solve ASILs allocation problem. The review provides an overview of safety requirements including the related standards followed by a study of the resolution methods of the existing approaches. The study of each approach provides a detailed explanation of the used methodology and a discussion of its strength and weaknesses including the main open challenges.

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Ant colony algorithm for automotive safety integrity level allocation

Cited by 10 publications

References 18 publications

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

An Optimal Design Procedure based on the Safety Integrity Level for Safety-related Systems

An Overview of the Approaches for Automotive Safety Integrity Levels Allocation

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