粒子群优化算法在配方设计中的应用

随着群体智能研究的兴起,粒子群优化(PSO,Particle Swarm Optimization)算法已经成为新的研究热点。该算法模仿鸟类和鱼类群体觅食迁徙中个体与群体协调一致的机理,通过群体最优方向、个体最优方向和惯性权重的协调来求解实数化问题。本文从粒子群优化算法的理论分析切入,阐述了PSO算法的基本原理、算法流程,提出用PSO算法来解决卷烟配方优化设计这类组合优化问题,并对其实际应用效果进行分析和验证。

The Application of Particle Swarm Optimization to Cigarette Design

With the emergence of Swarm Intelligence research,The PSO algorithm is becoming a hot research issue in the past decade.The algorithm imitates birds and fish community looking for food in the migration and solves the real number problem through the coordination of global best value,personal best value and weight value.The thesis begins from the theory analysis of PSO algorithm,expatiates its basic principles and algorithm process.The thesis proposes a method which is using PSO algorithm to solve the optimization problem of cigarette blending design,and takes the practical application analysis and verification for the algorithm.