A Highly Efficient Multi-objective Optimization Evolutionary Algorithm
Bojin Zheng · 2007
Multi-objective Optimization Evolutionary Algorithms (MOEAs) are effective for solving Multi-objective Optimization Problems. Here a new algorithm is proposed and is compared with some famous MOEAs at the state of the art. The experimental results imply that the approximated Pareto Fronts which are obtained by this algorithm are better than the approximated Pareto Fronts by SPEA2, NSGAII etc. when dealing with the chosen test problems within satisfactory computational time.