*(English)*Zbl 1138.65047

Summary: Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages.

This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case.

##### MSC:

65K05 | Mathematical programming (numerical methods) |

90C06 | Large-scale problems (mathematical programming) |

90C30 | Nonlinear programming |

90C15 | Stochastic programming |

90C29 | Multi-objective programming; goal programming |

90C51 | Interior-point methods |

##### Keywords:

nonlinear programming; genetic algorithm; interior point method; multiobjective optimization; numerical examples; evolutionary algorithms; global optimization; large-scale problems; local search strategy##### References:

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