Hierarchical adaptive Multi-objective resource management for many-core systems

2019 ◽  
Vol 97 ◽  
pp. 416-427 ◽  
Author(s):  
André Luís del Mestre Martins ◽  
Alzemiro Henrique Lucas da Silva ◽  
Amir M. Rahmani ◽  
Nikil Dutt ◽  
Fernando Gehm Moraes
Author(s):  
Heba Khdr ◽  
Santiago Pagani ◽  
Muhammad Shafique ◽  
Jörg Henkel

2009 ◽  
Vol 11 (1) ◽  
pp. 79-88 ◽  
Author(s):  
M. Janga Reddy ◽  
D. Nagesh Kumar

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.


Sign in / Sign up

Export Citation Format

Share Document