Study On Cutting Plan Application Based On Improved Particle Swarm Optimization Algorithm

Planing the harvesting of the forest, which is the forest manager's important work, is the main basis of the forest harvesting, as well as the insurance of enlarging the forest covering area and fulfill the target of "the more harvesting, the more and the better for harvesting, forever the green hill, forever to be used". The appropriate amount of the forest harvesting depends whether a region or a manufacturing unit can organize the wood production scientificly, make use of the forest resource rationaly, improve the resource structure of the forest, implement multiple benefits of the forest, and satisfy the social wood request to the largest extent. Thus, planing for the harvesting is an effective measure to make sure the harvesting process is scientific, the amount of the harvesting is reasonable, and the forestry development is sustainable.The optimizationg of forest harvesting is kind of constrained optimization, with the features of multiple control variables, multiple constraining conditions, continuous and integer variables mixed, and uncertainty. The conventional methods of mathematical programming have limitation in solving such kind of problem. Formula method is used most often in proving the resonable amount of the annual harvesting, while this method is simple at calculating, the factors considerd is few, hense, this method is subjective, not comprehensive, and not conductive to the adjustment of the age class and the relatively constant amount of the wood. In this paper, the method of particle swarm optimization (PSO for short) and its application in harvesting plan have been studyed, a new thinking of solving is proposed: one improved PSO, which is based on the computer intelligent theory, incorporating a penalty function to handle constraint, is applicated in the optimization. Besides, a scientific and reasonable harvesting plan model is proposed, as well as harvesting plan mechanism, which can be used to solve the harvesting plan model.About the optimization of the harvesting plan, this paper contains a comprehensive and deep-seated discussion, which involves the construction of the particle, how to handle the constrains on the equality or inequality. In addition, this paper also discuss the optimization mechanism of PSO and the controlling parameters, and introduce a new PSO, which is based on exterior penalty function SUMT method, called as CPSO. CPSO is a method based on PSO, according to the relation among each parameters and particle swarm fitness, and the a penalty function is introduced here to handle constraint, thus each parameters could change according to different contraint conditions in different optimization process, and to get the best solutions finally.On this basis, with a computer program, the visualization of the model is carried, and a simulation of the harvesting plan system based on the CPSO is given, and the result of the simulation is to get the best solution, and to make sure that the harvesting amount is largest after the ensurance of the forever used of the forest resource.The experiment shows that, CPSO has a high quality in optimizing, easy calculation, quickness in resolving, hence, is suitable in resolving mass harvesting plan. Besides, the improved PSO owns a superiority in reducing the complexity of calculating, expands the method in harvesting plan, have a good prospect.