`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
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
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
A unified view on hybrid metaheuristics
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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In the recent past, hybrid metaheuristics became famous as successful optimization methods. The motivation for the hybridization is a notion of combining the best of two worlds: evolutionary black box optimization and local search. Successful hybridizations in large combinatorial solution spaces motivate to transfer the idea of combining the two worlds to continuous domains as well. The question arises: Can local search also improve the convergence to the Pareto front in continuous multiobjective solutions spaces? We introduce a relay and a concurrent hybridization of the successful multiobjective optimizer SMS-EMOA and local optimization methods like Hooke & Jeeves and the Newton method. The concurrent approach is based on a parameterized probability function to control the local search. Experimental analyses on academic test functions show increased convergence speed as well as improved accuracy of the solution set of the new hybridizations.