基于群智能求解带约束问题的时域鲁棒优化算法

针对现有的时域鲁棒优化算法无法解决带约束的优化问题,基于群智能优化方法,提出一种求解带约束优化问题的时域鲁棒优化算法.首先,用约束条件构造罚函数,将带约束优化问题处理成为无约束优化问题;然后,采用一个分段函数作为粒子的适应度评价函数,通过竞争规则筛选粒子,设计带约束问题的时域鲁棒优化算法.以优化碳纤维原丝的性能为背景,将算法在多组参数下进行测试和对比分析,结果表明了所提出算法的有效性.进一步分析AR模型对算法性能的影响,指出预测模型的改进是提升算法性能的一个重要手段.

Robust optimization over time for constrained optimization based on swarm intelligence

As constrained optimization problem can not be solved by existing robust optimization over time , this paper proposes a new algorithm for robust optimization over time for solving constrained optimization problems based on swarm intelligence. Firstly, a penalty function with constraints is constructed and the constrained optimization problems are processed into the unconstrained optimization problems. Then, the algorithm of robust optimization over time for constrained is designed, which the piecewise function is used as the fitness evaluation function of the particle, and the particle is selected by the competition rule. Finally, The proposed algorithm is evaluated and compared under different parameter settings for optimization of the performance of a carbon fiber precursor to demonstrate its effectiveness. Furthermore, the influence of AR model on the performance of the algorithm is analyzed. It is pointed out that the improvement of the prediction model is an important way to improve the performance of the algorithm.