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A hybrid optimization technique coupling an evolutionary and a local search algorithm. (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:
65K05Mathematical programming (numerical methods)
90C06Large-scale problems (mathematical programming)
90C30Nonlinear programming
90C15Stochastic programming
90C29Multi-objective programming; goal programming
90C51Interior-point methods
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