Aiming at the existing fuzzy dependent-chance programming model in uncertainty planning field and the
optimization approach of group intelligence-differential evolution algorithm, a novel hybrid intelligent algorithm for fuzzy
dependent-chance programming models is designed. The proposed algorithm uses hybrid differential evolution algorithm
improved by particle swarm optimization algorithm and fuzzy simulation to solve this kind of programming problems. It
does not need a long time and complex calculations to get reasonable results like the hybrid intelligent algorithm based on
traditional genetic algorithm. Numerical examples show the rationality and effectiveness of the proposed hybrid intelligent
algorithm.