TY - GEN
T1 - A grid-based fitness strategy for evolutionary many-objective optimization
AU - Li, Miqing
AU - Zheng, Jinhua
AU - Shen, Ruimin
AU - Li, Ke
AU - Yuan, Qizhao
PY - 2010
Y1 - 2010
N2 - Grid has been widely used in the field of evolutionary multiobjective optimization (EMO) due to its property combining convergence and diversity naturally. Most EMO algorithms of gridbased fitness perform well on problems with two or three objectives, but encounter difficulties in their scalability to many-objective optimization. This paper develops the potential of using grid technique to balance convergence and diversity in fitness for manyobjective optimization problems. To strengthen selection pressure and refine comparison level, three hierarchical grid-based criterions are incorporated into fitness to establish a completer order among individuals. Moreover, an adaptive fitness penalty mechanism in environmental selection is employed to guarantee the diversity of archive memory. Based on an extensive comparative study with three other EMO algorithms, the proposed algorithm is found to be remarkably successful in finding well-converged and welldistributed solution set.
AB - Grid has been widely used in the field of evolutionary multiobjective optimization (EMO) due to its property combining convergence and diversity naturally. Most EMO algorithms of gridbased fitness perform well on problems with two or three objectives, but encounter difficulties in their scalability to many-objective optimization. This paper develops the potential of using grid technique to balance convergence and diversity in fitness for manyobjective optimization problems. To strengthen selection pressure and refine comparison level, three hierarchical grid-based criterions are incorporated into fitness to establish a completer order among individuals. Moreover, an adaptive fitness penalty mechanism in environmental selection is employed to guarantee the diversity of archive memory. Based on an extensive comparative study with three other EMO algorithms, the proposed algorithm is found to be remarkably successful in finding well-converged and welldistributed solution set.
KW - Fitness assignment
KW - Grid
KW - Many-objective optimization
KW - Multiobjective optimization
UR - http://www.scopus.com/inward/record.url?scp=77955863089&partnerID=8YFLogxK
U2 - 10.1145/1830483.1830570
DO - 10.1145/1830483.1830570
M3 - Conference contribution
AN - SCOPUS:77955863089
SN - 9781450300728
T3 - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10
SP - 463
EP - 470
BT - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10
T2 - 12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
Y2 - 7 July 2010 through 11 July 2010
ER -