Blended Ranking to Cross Infeasible Regions in ConstrainedMultiobjective Problems

Authors:
Nicholas Young
Affiliations:
Central Queensland University, Australia
Venue:
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Year:
2005

Citing 0
Cited 2

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BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems

Computers and Operations Research

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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.