A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem.
Wenqiang ZhangChen LiMitsuo GenWeidong YangGuohui ZhangPublished in: Expert Syst. Appl. (2024)
Keyphrases
- memetic algorithm
- energy efficient
- multi objective
- particle swarm optimization
- distributed heterogeneous
- genetic algorithm
- evolutionary algorithm
- vehicle routing problem
- evolutionary computation
- crossover operator
- wireless sensor networks
- tabu search
- energy consumption
- multi objective optimization
- metaheuristic
- optimization algorithm
- job shop scheduling problem
- sensor networks
- nsga ii
- particle swarm optimization pso
- differential evolution
- combinatorial optimization
- objective function
- reinforcement learning
- data sources
- swarm intelligence
- base station
- simulated annealing
- energy efficiency
- ant colony optimization
- routing protocol
- multi core architecture
- optimization problems
- query processing
- fitness function
- data transmission
- state space
- genetic programming
- computational intelligence
- neural network
- data sets
- real time
- feasible solution
- data center
- routing algorithm
- sensor nodes