Name
Abstract | ||
|---|---|---|
We proposed a new evolutionary algorithm for multiobjective optimization problems. The influence of constraints on search space and Pareto front are analyzed first. According to the analysis, a new clustering method based on domination is proposed, in which the infeasible solutions are employed. Then, aiming to converge to Pareto fronts of the multiobjective problems quickly, a differential evolution based crossover operator is designed. In the designed crossover operator, uniform design method was used. At last, a square search method is employed to update the feasible nondominated solutions to improve the precision. Experiments on 10 selected test problems and comparisons with NSGA-II are made. Simulation results indicate that our proposal is effective and sound, and our proposal outperforms NSGA-II on the selected test problems. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CIS.2013.27 | CIS |
Keywords | Field | DocType |
crossover operator,evolutionary algorithm,pattern clustering,multiobjective optimization problems,search space analysis,clustering method,differential evolution based crossover operator,evolutionary algorithms,new evolutionary algorithm,search problems,pareto analysis,enhanced domination based evolutionary algorithm,convergence,multiobjective problem,nsga-ii,uniform design method,constraint handling,genetic algorithms,selected test problem,square search,pareto front analysis,multiobjective optimization,multiobjective optimization problem,search space,pareto front,new clustering method,enhanced domination,square search method,multi-objective problems | Mathematical optimization,Crossover,Evolutionary algorithm,Computer science,Multi-objective optimization,Differential evolution,Artificial intelligence,Pareto analysis,Cluster analysis,Machine learning,Genetic algorithm,Pareto principle | Conference |
ISBN | Citations | PageRank |
978-1-4799-2548-3 | 0 | 0.34 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Fan | 1 | 48 | 5.70 |
| Xiyang Liu | 2 | 159 | 18.55 |