Erling Wisløff
ABSTRACT
Constraints are a reality in many real-world application areas, adding feasibility to the existing multiple objective challenge. Further, the presence of complex constraints poses a significant challenge to multi-objective evolutionary algorithms. A recently proposed biphasic multi-objective evolutionary framework for constrained multi-objective optimization problems is the Push and Pull Search framework. This framework benefits from a strong exploration of the constrained landscape during the search for the unconstrained Pareto-Front during the first phase. The work herein extends the Push and Pull Search framework, extending landscape information gathering in the push phase; adding a binary search of the feasible and infeasible regions and creating a suitably diverse population and improved initialization for the push phase.
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Index Terms
- Extending the push and pull search framework with boundary search for constrained multi-objective optimization
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