TY - GEN
T1 - Enhancing diversity for average ranking method in evolutionary many-objective optimization
AU - Li, Miqing
AU - Zheng, Jinhua
AU - Li, Ke
AU - Yuan, Qizhao
AU - Shen, Ruimin
PY - 2010
Y1 - 2010
N2 - The average ranking (AR) method has been shown highly effective to provide sufficient selection pressure searching towards Pareto optimal set in many-objective optimization. However, as lack of diversity maintenance mechanism, the obtained final set may only concentrate in a subregion of Pareto front. In this paper, we propose a diversity maintenance strategy for AR to balance convergence and diversity during evolution process. We employ grid to define an adaptive neighborhood for each individual, whose size varies with the number of objectives. Moreover, a layering selection scheme integrates it and AR to pick out well-converged individuals and prohibit or postpone the archive of adjacent individuals. From an extensive comparative study with original AR and two other diversity maintenance methods, the proposed method shows a good balance among convergence, uniformity and spread.
AB - The average ranking (AR) method has been shown highly effective to provide sufficient selection pressure searching towards Pareto optimal set in many-objective optimization. However, as lack of diversity maintenance mechanism, the obtained final set may only concentrate in a subregion of Pareto front. In this paper, we propose a diversity maintenance strategy for AR to balance convergence and diversity during evolution process. We employ grid to define an adaptive neighborhood for each individual, whose size varies with the number of objectives. Moreover, a layering selection scheme integrates it and AR to pick out well-converged individuals and prohibit or postpone the archive of adjacent individuals. From an extensive comparative study with original AR and two other diversity maintenance methods, the proposed method shows a good balance among convergence, uniformity and spread.
KW - Average ranking
KW - Diversity maintenance
KW - Many-objective optimization
KW - Multiobjective optimization
UR - http://www.scopus.com/inward/record.url?scp=78149232943&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15844-5_65
DO - 10.1007/978-3-642-15844-5_65
M3 - Conference contribution
AN - SCOPUS:78149232943
SN - 3642158439
SN - 9783642158438
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 647
EP - 656
BT - Parallel Problem Solving from Nature, PPSN XI - 11th International Conference, Proceedings
T2 - 11th International Conference on Parallel Problem Solving from Nature, PPSN 2010
Y2 - 11 September 2010 through 15 September 2010
ER -