自适应混沌蚁群算法的粮食应急路径优化研究

摘要

针对风险管理下的粮食应急路径优化问题,将“运输风险最小”和“运输时间最小”作为目标,建立相应的优化模型.利用“最大最小蚂蚁系统”进行求解,为避免过早陷入局部最优,提出自适应混沌蚁群优化算法.该算法利用有效解相似度来判断蚁群当前状态,根据情况对信息素进行全局更新和混沌扰动,可以有效地提高最优解的精度.实验表明该算法优于传统的演化算法,较好地解决了粮食应急运输路径优化问题.%According to the problem of grain emergency path optimization of risk management, a multi-objective optimization model, which treats the least transportation time and risk as the optimization target is established. To avoid the remaining local optima of the MAX-MIN Ant System (MMAS), an adaptive chaos-based ACO is introduced. In order to achieve the objective of improving the precision of the optimal solution, this algorithm utilizes the efficient solution similarity degree to judge ant colonies current quality so that a chaotic disturbance of pheromone update is realized according to changed conditions. The experiment shows that the algorithm is superior to conventional evolutionary algorithm and can better solve the problem of grain emergency path optimization.