论文部分内容阅读
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。
In order to solve the problem of grain emergency routing under risk management, the objective of “minimum transportation risk” and “minimum transportation time” is set up, and corresponding optimization model is established. Using “Max-Min Ant System” to solve, in order to avoid premature fall into local optimum, an adaptive chaotic ant colony optimization algorithm is proposed. The algorithm uses the effective solution similarity to judge the current state of the ant colony. According to the situation, the pheromone can be updated globally and disturbed by chaos, which can effectively improve the precision of the optimal solution. Experiments show that the proposed algorithm outperforms the traditional evolutionary algorithm and solves the problem of grain emergency transportation route optimization.