An improved genetic algorithm based on explosion mechanism

Wu, Yong; Hu, Guanhua; Yang, Qiming; Wang, Xiyue; Chen, Long

doi:10.1109/icca54724.2022.9831829

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…This improvement guides the population toward the global optimum over iterations, increasing the genetic algorithm's chance of converging to the global optimum. 9 Crossover operator improvement: We enhance the simulated binary crossover (SBX) operator's efficiency. Initially, we set a small value for γ to broaden the search space and gradually decrease it in later stages for improved accuracy.…”

Section: Selection Operator Improvementmentioning

confidence: 99%

Train energy efficiency optimization scheme based on speed profile correction curve

Zhang,

Chen

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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Urban rail transit, characterized by safety, efficiency, punctuality, and speed, plays a significant role in urban transportation, facilitating people’s mobility. However, the operation of urban rail transit consumes a consider- able amount of energy, resulting in high operating costs and impeding its further development. In this study, focusing on the operation within a single train interval, an improved genetic algorithm is employed to optimize the train’s speed profile, aiming to minimize the traction energy consumption within the interval. Additionally, the deviation in speed profiles due to variations in train’s basic resistance parameters is analyzed, and corresponding correction schemes are proposed. Simulation results demonstrate that a single traction/braking scheme in the train correction plan leads to lower traction energy consumption and better alignment with the operational requirements of urban rail transit.

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Section: Selection Operator Improvementmentioning

confidence: 99%

Train energy efficiency optimization scheme based on speed profile correction curve

Zhang,

Chen

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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“…As a result, most fireworks SMS research is focused on the sensitivity and hyperactivity of individual components and materials. An early attempt to investigate what led to an explosion in the Italian FI was proposed by [18], who found that the manufacturing process lacked stringent controls and inspections mandated by a SMS for high-risk facilities. The lack of standardized equipment, tools, and manufacturing procedures, as well as an incomplete understanding of the thermochemistry and explosive nature of fireworks, are the leading causes of accidents [19].…”

Section: Related Workmentioning

confidence: 99%

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Nallathambi

Savaram²,

Sengan

et al. 2023

Sensors

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In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study’s objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

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An improved genetic algorithm based on explosion mechanism

Cited by 2 publications

References 5 publications

Train energy efficiency optimization scheme based on speed profile correction curve

Train energy efficiency optimization scheme based on speed profile correction curve

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

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