Title :
Blended Ranking to Cross Infeasible Regions in ConstrainedMultiobjective Problems
Author_Institution :
Central Queensland Univ., Rockhampton, Qld.
Abstract :
We present a multiobjective evolutionary algorithm designed to reliably cross infeasible regions of objective space and find the true constrained Pareto front, which may lie across multiple disconnected feasible regions. By blending an individual´s rank in objective space with its rank in constraint space, some infeasible solutions may be selected over some feasible solutions, allowing the population to traverse infeasible regions smoothly. Results from artificial benchmark problems qualitatively illustrate this behaviour, in contrast to NSGA-II which must cross infeasible regions in a single generation
Keywords :
Pareto optimisation; constraint theory; evolutionary computation; Pareto dominance ranking; blended ranking; constrained multiobjective problem; cross infeasible region; evolutionary algorithm; Algorithm design and analysis; Australia; Automation; Biological cells; Computational intelligence; Computational modeling; Constraint optimization; Degradation; Evolutionary computation; Extraterrestrial measurements;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7695-2504-0
DOI :
10.1109/CIMCA.2005.1631467
