生化途径参数估计的QPSO-MGbest算法

讨论了非线性动力生化过程的参数估计（反问题），描述为受一组非线性代数-微分方程约束的非线性规划问题，由于频繁的病态和多峰值，传统的算法（如梯度算法）并不能得到满意的解。提出了一种改进的量子行为粒子群优化算法求解代谢途径的参数估计，该算法采用基于全局最好位置的变异操作以提高算法的非线性逼近能力和较好的全局搜索能力。以一个三阶段代谢途径为研究对象，建立参数估计的算法模型，以实验值和预测值的误差平方加权的和为目标优化函数。实验表明改进量子行为粒子群优化算法能够较好求解该问题。

QPSO-MGbest algorithm for parameter estimation in biochemical pathways

The parameter estimation（inverse problem） of nonlinear dynamic biochemical pathways which is stated as a non- linear programming problem subject to nonlinear differential-algebraic constrains is discussed.The problem is frequently ill- conditioned and multimodal, traditional（gradient-based） local optimization methods fail to arrive at satisfactory solutions.An improved quantum-behaved particle swarm optimization is proposed to solve the inverse problem.The improve QPSO employs a mutation operation exerted on the global best position to enhance the search ability of the QPSO algorithm.A case study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark.It is shown that the improved QPSO algorithm is able to solve the problem successfully as showed by comparative experiments.