Kriging-surrogate-based optimization considering expected hypervolume improvement in non-constrained many-objective test problems.
Koji ShimoyamaShinkyu JeongShigeru ObayashiPublished in: IEEE Congress on Evolutionary Computation (2013)
Keyphrases
- test problems
- nsga ii
- optimization problems
- multi objective evolutionary algorithms
- uniform design
- multiobjective optimization
- knapsack problem
- benchmark problems
- pareto fronts
- branch and bound algorithm
- solution quality
- multi objective
- test functions
- pareto optimal solutions
- pareto optimal
- evolutionary algorithm
- multiple objectives
- multi objective differential evolution
- optimization algorithm
- test instances
- multi objective optimization problems
- tabu search
- multi objective problems
- concave convex procedure
- metaheuristic
- optimal solution
- global optimization
- bi objective
- lower bound
- candidate solutions
- multi objective optimization
- black box
- linear programming
- np hard
- optimization method
- special case
- pareto dominance
- objective function
- high dimensional
- cost function
- decision variables
- simulated annealing
- crossover operator
- genetic programming
- optimization process
- combinatorial optimization
- greedy algorithm