Optimization on Black Box Function Optimization Problem

Xiao, Jinke; Liu, Weimin; Li, Wei; Xiao, Xuefeng

doi:10.1155/2015/647234

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…scenario parameters) and output (i.e. simulation results) relationship can only be analyzed by exterior observation through simulations [135]. To tackle this problem, various heuristic methods can be applied to approximate the fitting function and find the global minimum iteratively, including genetic algorithm [49], [50], [108], Bayesian optimization [92], [98], and simulated annealing [51].…”

Section: Exploration Methodsmentioning

confidence: 99%

Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Zhang¹,

Tao²,

Tan

et al. 2023

IIEEE Trans. Software Eng.

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Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

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Section: Exploration Methodsmentioning

confidence: 99%

Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Zhang¹,

Tao²,

Tan

et al. 2023

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…scenario parameters) and output (i.e. simulation results) relationship can only be analyzed by exterior observation through simulations [132]. To tackle this problem, various heuristic methods can be applied to approximate the fitting function and find the global minimum iteratively, including genetic algorithm [49], [50], [105], Bayesian optimization [89], [95], and simulated annealing [51].…”

Section: Exploration Methodsmentioning

confidence: 99%

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Zhang¹,

Tao²,

Tan³

et al. 2021

Preprint

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Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

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“…O objetivo de um problema de otimização é melhorar a performance ou custooutput -ajustando as variáveis de entrada -input - (Munõz et al, 2015). Segundo Xiao et al (2015), muitos dos problemas reais de otimização esta relação entre input-output é vaga, resultando no chamado problema de otimização de função caixa-preta, ou BBFOP (black box function optimization problem).…”

Section: Introductionunclassified

“…Quando o problema de otimização consiste em minimizar o output trata-se de um problema de minimização, e quando este problema possui comportamento não convexo muitos algoritmos apresentam um mínimo local ao invés de um global, como explicitado por Vavasis (1993), sendo que os mínimos locais são fundamentais para futuras tomadas de decisão quando o mínimo global não pode ser encontrado (Xiao et al, 2015). É extremamente difícil de os pesquisadores estarem familiarizados com todos os métodos de otimização dado o aumento no número de algoritmos desenvolvidos pela comunidade nas últimas décadas, e a escolha do melhor algoritmo é não trivial, sendo que mesmo com todo o conhecimento dos algoritmos é possível que se fracasse no processo (Munõz et al, 2015).…”

Section: Introductionunclassified

Ozonioterapia no tratamento de feridas em adultos: revisão integrativa / Ozoniotherapy in the treatment of wounds in adults: integrative review

Margotto¹,

Miotto²,

Kopperschmidt³

et al. 2021

BJDV

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Este artigo realiza o estudo dos métodos de otimização determinístico, Steepest Descent, e heurístico, Differential Evolution e Particle Swarm, para um problema black box genérico com duas variáveis em sua função objetivo. O método determinístico apresentou forte dependência dos valores iniciais adotados, apresentando diversos mínimos locais, sendo necessário a adoção de múltiplos pontos iniciais. Os métodos Particle Swarm e Differential Evolution apresentam resultados razoáveis, porém o funcionamento dos algoritmos heurísticos impossibilita que o ponto encontrado seja certamente definido como mínimo global.

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Optimization on Black Box Function Optimization Problem

Cited by 5 publications

References 36 publications

Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Ozonioterapia no tratamento de feridas em adultos: revisão integrativa / Ozoniotherapy in the treatment of wounds in adults: integrative review

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