摘要
以公交费用最小和乘客平均等待时间最短为目标构建优化调度模型,针对已有算法在求解这类调度问题存在的早熟收敛、优化效率较低的缺点,提出了一种惯性权重自适应调整的量子行为粒子群优化算法。首先引入聚焦距离变化率的概念,将惯性权重因子表示为关于聚焦距离变化率的函数,从而使算法具有动态自适应性;同时在算法中嵌入了一种判断和避免搜索早熟和停滞的有效方法。优化实例的结果分析表明,该算法能有效地解决公交车辆的调度优化问题。
For the premature convergence and low efficiency optimization of the existing public transit vehicle dispatching algorithm,this paper puts forward a quantum particle swarm optimization algorithm with weight adaptive adjustments to construct optimal dispatching model aiming at the minimum cost and the shortest passenger s＇ mean waiting time.Firstly,the concept of focusing distance changing rate was introduced in this algorithm and inertial weighting factor was formulated as a function of focusing distance rate so as to provide the algorithm with effective dynamic adaptability.Meanwhile,a method of effective judgment of premature and stagnation is embedded in the algorithm.The optimization results show that this algorithm can effectively solve public transit vehicle dispatching problems.
出处
《计算机系统应用》
2012年第7期191-195,共5页
Computer Systems & Applications
基金
国家自然科学基金(61004127)
中北大学青年基金
