关键词: 粮食应急, 风险管理, 路径优化, 自适应混沌蚁群算法, 有效解相似度

Abstract: 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.

Key words: grain emergency, risk management, path optimization, adaptive chaos-based Ant Colonies Optimization（ACO） algorithm, efficient solution similarity degree