基于改进粒子群算法的造林规划

一直以来造林规划的工作者主要是靠手工进行规划或者运用一些通用的计算机软件来辅助规划,表现出工作繁琐、效率低、设计粗糙、计算不科学.首先给出一种改进的粒子群算法,即使用最简单的罚函数法将约束优化问题转化为无约束优化问题,然后将不同阶段搜索进度应用到粒子群算法中.该算法在优化目标函数的分析性质上没有特殊要求,并具有较强的全局寻优能力.用该改进粒子群算法来确定在资金、计划、苗木品种和数量等约束条件下小班造林规划最优方案,结果表明该算法在约束条件下造林规划效果很理想.

Reforestation Planning Based on Improvement of Particle Swarm

For a long time,afforestation program workers mainly depend on manual planning or use of some common computer software to aid planning,which make the work tedious and inefficient,and bring about rough design and unscientific calculation.The paper first gives an improved particle swarm optimization,that is,using the simplest penalty function method for constrained optimization problems into unconstrained optimization problems,and then progress at different stages of the search applied to the particle swarm algorithm.Optimization of the algorithm in the analysis of the nature of the objective function has no special requirements,and has strong ability of global optimization.The paper sets forward improved particle swarm algorithm to determine the most effectual scheme under the restricted conditions of funds,variety and quantities of seedlings.The mathematical models and concrete methods are offered and discussed.And it is found out the satisfactory results by applying this algorithm on afforestation program under the restricted conditions.