A Novel Multiobjective Evolutionary Algorithm Based on Min-Max Strategy
A new fitness function is proposed in the paper at first, in which the fitness of an individual is defined by the maximum value of the weighted normalized objectives. In order to get the required weights, the sphere coordinate transformation is used. The fitness constructed in this way can result in a group of uniform search directions in the objective space. By using these search directions, the evolutionary algorithm can explore the objective space uniformly, keep the diversity of the population and find uniformly distributed solutions on the Pareto frontier gradually. The numerical simulations indicate the proposed algorithm is efficient and has a better performance than the compared ones.
Unable to display preview. Download preview PDF.
- 6.Deb, K.: Multiobjective genetic algorithms: Problem difficulties and construction of test functions Evolutionary Computation 7, 205–230 (1999)Google Scholar
- 9.Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) Proceedings of the Parallel Problem Solving from Nature VI Conference. Springer, Heidelberg (2000)Google Scholar