Name
Abstract | ||
|---|---|---|
In complicated multi-objective optimization, it often happens that points in part region of Pareto front are easy to get, but in others are difficult. To obtain evenly distributed Pareto optimal solution, we construct dynamical crossover and mutation probability which can self-adaptively adjust the number of individuals engaged in crossover and mutation, combine with the fitness function constructed by weighted min-max strategy in which the weight is uniformly designed, to present a new multi-objective evolutionary algorithm (DMOEA). To evaluate the performance of our algorithm, we compare the numerical results of our algorithm with the MOEA/D-DE and NSGA-II-DE, the comparison shows that our algorithm is very efficient. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CIS.2008.81 | CIS (1) |
Keywords | Field | DocType |
dynamical crossover,dynamic mutation probability,evolutionary computation,mutation probability,pareto optimal solution,dmoea,numerical result,weighted min-max strategy,minimax techniques,multiobjective evolutionary algorithm,pareto optimisation,complicated multi-objective optimization,new multi-objective evolutionary algorithm,genetic algorithm,min-max strategy,pareto front,multi-objective evolutionary algorithm,dynamic crossover probability,multi-objective optimization,probability,distributed pareto optimal solution,part region,simulated annealing,optimization,security,multi objective optimization,fitness function,mathematics,algorithm design and analysis | Simulated annealing,Mathematical optimization,Crossover,Algorithm design,Evolutionary algorithm,Computer science,Evolutionary computation,Multi-objective optimization,Fitness function,Artificial intelligence,Machine learning,Genetic algorithm | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3508-1 | 3 |
PageRank | References | Authors |
0.40 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai-lin Liu | 1 | 668 | 52.80 |
| Xueqiang Li | 2 | 47 | 4.54 |
| Yuqing Chen | 3 | 3 | 0.40 |