Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multicriteria Optimization
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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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Evolutionary algorithms are efficient population based algorithms for solving multi-objective optimization problems. Recently various authors have discussed the efficacy of combining gradient based classical methods with evolutionary algorithms. This is done since gradient information leads to convergence to Pareto-optimal solutions with a linear convergence rate. However none of existing studies have explored how to exploit second order or Hessian information in evolutionary multiobjective algorithms. Second order information though costly, leads to a quadratic convergence to Pareto-optimal solutions. In this paper, we take Levenberg-Marquardt methods from classical optimization and show two possible ways of hybrid algorithms. These algorithms require gradient and Hessian information which is obtained using finite difference techniques. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of resulting hybrid algorithms in solving a large class of complex multi-objective optimization problems.