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中文摘要：
针对现有车辆路径问题模型对动态性和开放性的约束限制,建立了开放式动态网络车辆路径的数学模型,使用连续时间依赖函数表示动态网络,并提出了基于惯性权重自适应调整和状态分类更新的粒子群算法求解该问题。根据社会认知理论,每个粒子依据当前位置与种群最优位置和自身历史最优位置的相对关系,动态调整自身的惯性权重。为避免早熟收敛,增加群体的多样性,使用分类更新策略。对于优秀的粒子,通过计算信息熵,使用特殊的状态更新公式计算其状态;对于适应度低的粒子,通过公告板统计出现的频率,进行粒子更新。通过实验仿真,对算法的参数进行了分析,并通过与其他算法的比较,验证了该算法的有效性。
英文摘要：
Amiming at the dynamic and open constraints of the existing vehicle routing problem, the mathematical model of the open Vehicle Routing Problem （VRP） in dynamic network was established. And the time dependent function was used to represent dynamic network. Particle swarm optimization with self-adaptive inertia weight and classified status update was proposed to solve the problem. According to social cognitive theory, each particle regulated its inertia weight dynamically according to the relative value of the particle＇s current position with its best position in the history and the best position in the population. To avoid premature convergence, classified update strategies were used to increase population diversity. For the excellent particles, their information entropy was computed after server iterations and their position were updated. For the inferior particles updates, it were conducted by recording frequencies in the board and displaced by the new particles. In the experiment, the parameters were analyzed. Comparing to other algorithms on benchmarks showed that the algorithm was effective.
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