植物多向生长模拟算法

针对整数规划问题提出了一种以植物向光性为启发式准则的智能优化算法——植物多向生长模拟算法. 改进了植物生长激素的分配方式并将随机选择机制引入新枝生长方向的选择，更符合植物生长的自然机理. 利用马尔可夫链描述算法迭代过程，证明了算法的收敛性. 利用无约束和有约束两类具有多个全局最优解的非线性整数规划实例测试了植物多向生长模拟算法的性能，并与基本植物生长模拟算法、填充函数法、罚函数法以及基于遗传算法的混合算法进行了对比. 植物多向生长模拟算法不仅提高全局寻优能力，增加解的多样性和准确性而且提高收敛速度.

Plant multi-direction growth simulation algorithm

An intelligent optimization algorithm based on the heuristic criteria of plant phototropism is proposed, called plant multi-direction growth simulation algorithm (PMGSA), to solve the integer programming. It highlights the natural mechanism of plant growth by improving the distribution form of the phytohormone and introducing a random mechanism into the selection of new branch growth direction. Markov chain is used to describe the algorithm iterative process, which proves the convergence of the algorithm. Two kinds of nonlinear integer programming instances (unconstrained and constraint programming) with multiple global optimal solution are used to test the performance of the new algorithm, compared with basic plant growth simulation algorithm (PGSA) filled function method, penalty function method and hybrid algorithm based on GA. PMGSA not only improves the global optimization capability, but also increases the solution diversity and improves the convergence speed.