A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Combining gradient techniques for numerical multi-objective evolutionary optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Local search for multiobjective function optimization: pareto descent method
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new memetic strategy for the numerical treatment of multi-objective optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The Parallel Predator-Prey Model: A Step towards Practical Application
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Designing multi-objective variation operators using a predator-prey approach
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
On gradient based local search methods in unconstrained evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Parallel predator---prey interaction for evolutionary multi-objective optimization
Natural Computing: an international journal
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Traditionally, Predator-Prey Models--although providing a more nature-oriented approach to multi-objective optimization than many other standard Evolutionary Multi-Objective Algorithms--suffer from inherent diversity loss for non-convex problems. Still, the approach to peg single objectives to a predator allows a very simple algorithmic design. The building-block configuration of the predators offers potent means for fine-tuning and tackling multi-objective problems in a problem-specific way. In the work at hand, we propose the integration of local search heuristics into the classic model approach in order to overcome the unsatisfactory behavior for the aforementioned problem class. Our results show that, introducing a gradient-based local search mechanism to the system, deficiencies with respect to diversity loss can be highly ameliorated while keeping the beneficial properties of the Predator-Prey Model.