Can aquatic flightless birds allocate Automotive Safety requirements?

Gheraibia, Youcef; Moussaoui, Abdelouahab; Azevedo, Luís Silva; Parker, David; Papadopoulos, Yiannis; Walker, Martin

doi:10.1109/intelcis.2015.7397214

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The PeSOA was used by Gheraibia et al [54] to find an optimal ASILs allocation. The algorithm uses the concept of equivalent classes to prioritize a set of failure modes.…”

Section: Penguin Search Optimization 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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“…The PeSOA was used by Gheraibia et al [54] to find an optimal ASILs allocation. The algorithm uses the concept of equivalent classes to prioritize a set of failure modes.…”

Section: Penguin Search Optimization 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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“…ARMBGSA [25] is an association rule mining algorithm based on Newton's law of universal gravitation, namely a gravitational emulation local search algorithm, which is a nature inspired optimisation algorithm. In this problems such as automotive safety integrity levels allocation [32], capitated vehicle routing problem [33] and optimal spaced seed finding [34]. The dietary behaviour of penguins may be explained by economic reasoning: it comes to a profitable food search activity when the gain of energy is greater than the expenditure required to obtain this gain.…”

Section: Penguins Search Optimisation Algorithmmentioning

confidence: 99%

Penguins Search Optimisation Algorithm for Association Rules Mining

Gheraibia¹

2016

CIT. J. comput. inf. technol.

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Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decisionmaking process. All classic exhaustive ARM algorithms are time consuming and generate a very large number of association rules, even the recent proposed meta-heuristics based methods generate a small number of high quality rules but with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different data sets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well known meta-heuristics ARM algorithms in both execution time and solution quality.

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