Enhanced differential evolution using local Lipschitz underestimate strategy for computationally expensive optimization problems.
Xiaogen ZhouGui-jun ZhangXiao-hu HaoDong-wei XuLi YuPublished in: Appl. Soft Comput. (2016)
Keyphrases
- computationally expensive
- differential evolution
- evolutionary algorithm
- optimization problems
- evolutionary strategy
- constrained optimization problems
- differential evolution algorithm
- convergence speed
- multi objective
- test functions
- multi objective optimization problems
- mutation operator
- optimization algorithm
- particle swarm optimization
- particle swarm optimization pso
- optimization method
- computationally efficient
- multi objective optimization
- genetic algorithm
- numerical optimization problems
- evolutionary computation
- evolution strategy
- metaheuristic
- parameter optimization
- premature convergence
- global optimization problems
- optimization methods
- fitness function
- candidate solutions
- hybrid algorithm
- genetic operators
- crossover operator
- direct search
- function optimization problems
- particle swarm
- nsga ii
- real coded
- traveling salesman problem
- genetic programming
- evolutionary programming
- test problems
- knapsack problem
- function optimization
- harmony search
- initial population
- solving global optimization problems
- multi objective evolutionary algorithms
- neural network
- particle swarm optimization algorithm
- simulated annealing
- cost function