Coupling of multi-agent based simulation and particle swarm optimization for environmental planning and decision making
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Environmental design making modeling is a vital part in environmental decision making process to help to conclude which decisions should be made and how to find alternatives for each decision. However, the complicated circumstances, massive data, uncertainties and multiple criteria standards make the decision-making process sophisticated and hard to realize. This research focused on developing new environmental modelling methods by dynamic coupling of agent based modelling (ABM) and a multi-agent system (MAS) with PSO optimization algorithm and other kinds of traditional environmental simulation models for supporting environmental engineering decision making. Firstly, a novel multi-agent hybrid particle swarm optimization (MAHPSO) approach was developed for a wastewater treatment plant network design. A hybrid particle swarm optimization module was proposed to account for both continuous and binary variables, and then integrated with the concept of multi-agent to enhance solution convergence and stability. The feasibility and effectiveness of method was tested and demonstrated by a case based on the wastewater treatment plants network of the city of St. John’s, Canada. The results were compared with those of the traditional GA approach and the HPSO method. The proposed MAHPSO approach was approved to be capable of significantly enhancing solution convergence without sacraficing the computation time/efficiency, and of providing optimal results with high accuracy and repeatability. The approach could be used as an effective evolutionary algorithm for complex system optimization and planning problems in environmental and other fields. Secondly, a simulation-based multi-agent particle swarm optimization (SA-PSO) approach was developed for supporting dynamic decision making in offshore oil spill responses. The ABM as an emerging simulation method was introduced into oil spill responses in the first time to simulate the response actions with consideration of dynamic interactions among individual devices and/or response centre. A PSO method was further adopted to optimize the allocation of response devices/vessels among spill sites and warehouses with minimal total cost and time. Through a hypothetical oil spill case, the proposed SA-PSO approach showed strong capability and efficiency in reducing response time and optimizing responses. The results indicated that the proposed SA-PSO approach could efficently decrease the total response time, and dynamically optimize the allocation of response equipment. It had the strong potential to be applied to decision making problems in environmental and other fields. This research developed two new modeling methods for supporting WWTP network designs and oil spill responses, respectively. The results of two case studies demonstrated the value of the integration of emerging artificial intelligence approaches with traditional environmental simulation models for facilitating environmental engineering and management.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle