How to strike a balance between local search and global search in multiobjective memetic algorithms for multiobjective 0/1 knapsack problems.
Hisao IshibuchiYuki TanigakiNaoya AkedoYusuke NojimaPublished in: IEEE Congress on Evolutionary Computation (2013)
Keyphrases
- multi objective
- global search
- particle swarm optimization
- genetic algorithm
- memetic algorithm
- nsga ii
- knapsack problem
- evolutionary algorithm
- simulated annealing
- crossover operator
- convergence speed
- multi objective optimization
- optimization algorithm
- particle swarm optimization algorithm
- tabu search
- evolutionary computation
- optimization problems
- global optimization
- premature convergence
- multiobjective optimization
- vehicle routing problem
- pso algorithm
- differential evolution
- search algorithm
- multiple objectives
- swarm intelligence
- objective function
- test problems
- pareto optimal
- bi objective
- neural network
- evolution strategy
- conflicting objectives
- fitness function
- multi criteria
- combinatorial optimization problems
- search space
- mutation operator
- metaheuristic
- optimal solution
- particle swarm optimization pso
- genetic programming
- job shop scheduling problem
- exhaustive search
- exact algorithms
- cost function
- ant colony optimization
- combinatorial optimization