Prediction of energy consumption in a NSGA-II-based evolutionary algorithm.
Salvador MorenoJulio OrtegaMiguel DamasAntonio F. DíazJesús GonzálezHéctor PomaresPublished in: GECCO (Companion) (2018)
Keyphrases
- evolutionary algorithm
- energy consumption
- nsga ii
- multi objective
- multi objective optimization
- optimization problems
- wireless sensor networks
- energy efficient
- energy saving
- pareto optimal
- multiobjective optimization
- energy efficiency
- mutation operator
- sensor networks
- multiobjective evolutionary algorithm
- evolutionary computation
- constrained multi objective optimization problems
- differential evolution
- fitness function
- energy conservation
- data transmission
- data center
- routing protocol
- test problems
- genetic programming
- multi objective optimization problems
- optimization algorithm
- save energy
- bi objective
- evolutionary multiobjective optimization
- evolutionary multiobjective
- genetic algorithm
- routing algorithm
- base station
- electricity consumption
- sensor nodes
- multi objective evolutionary algorithms
- genetic operators
- simulated annealing
- crossover operator
- total energy
- residual energy
- multi hop
- particle swarm optimization
- benchmark problems
- knapsack problem
- dynamic programming
- greenhouse gas emissions
- neural network