Robotics & Machine Learning Daily News2024,Issue(Feb.9) :36-37.DOI:10.1007/s00521-023-09218-0

Studies Conducted at Northern University on Computational Intelligence Recently Reported (Improved Genetic Algorithm Approach for Coordinating Decision-making In Technological Disaster Management)

Robotics & Machine Learning Daily News2024,Issue(Feb.9) :36-37.DOI:10.1007/s00521-023-09218-0

Studies Conducted at Northern University on Computational Intelligence Recently Reported (Improved Genetic Algorithm Approach for Coordinating Decision-making In Technological Disaster Management)

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Abstract

Current study results on Computational Intelligence have been published. According to news originating from Barranquilla, Colombia, by NewsRx correspondents, research stated, “The increasing frequency of technological events has resulted in significant damage to the environment, human health, social stability, and economy, driving ongoing scientific development and interest in emergency management (EM). Traditional EM approaches are often inadequate because of incomplete and imprecise information during crises, making fast and effective decision-making challenging.” Financial support for this research came from Sistema General de Regalas de Colombia. Our news journalists obtained a quote from the research from Northern University, “Computational Intelligence techniques (CI) offer decision-supporting capabilities that can effectively address these challenges. However, there is still a need for deeper integration of emerging computational intelligence techniques to support evidence-based decision-making while also addressing gaps in metrics, standards, and protocols for emergency response and scalability. This study presents a coordinated decision-making system for multiple types of emergency case scenarios for technological disaster management based on CI techniques, including an Improved Genetic Algorithm (IGA), and Multi-objective Particle Swarm Optimization (MOPSO). The IGA enhances emergency performance by optimizing the task assignment for multiple agents involved in emergency response with coordination mechanisms, resulting in an approximately 15% improvement compared to other state-of-the-art methods.”

Key words

Barranquilla/Colombia/South America/Algorithms/Computational Intelligence/Emerging Technologies/Genetic Algorithms/Genetics/Machine Learning/Northern University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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参考文献量44
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