Title :
A Highly Efficient Multi-objective Optimization Evolutionary Algorithm
Author_Institution :
South-central Univ. for Nationalities, Wuhan
Abstract :
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.
Keywords :
Pareto optimisation; computational complexity; evolutionary computation; NSGAII; SPEA2; approximated Pareto front; computational complexity; multi-objective optimization evolutionary algorithms; multi-objective optimization problems; Computer science; Educational institutions; Evolutionary computation; Genetic algorithms; Pareto optimization; Sorting; Testing;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.43
