摘要
针对各种启发式算法在求车辆路径问题(VRP)中的缺陷,提出了改进的混合粒子群算法(MHPSO)的求解方法。分析了基于速度-位置更新策略传统粒子群算法在解决离散的和组合优化问题的不足。考虑到算法在求解过程中种群多样性的损失过快,引进了种群的多样性测度参数-平均粒距,以保持种群的多样性。同时利用混沌运功的随机性、遍历性和规律性等特性,采用混沌初始化粒子编码。详细讨论了该算法在车辆路径问题中的求解策略。针对同一个实例,将改进的混合粒子群算法与遗传算法从多个角度进行比较。仿真结果表明,论文所提出的算法性能较好,可以快速、有效求得车辆路径问题的优化解或近似优化解。
Many modern heuristic algorithms have been applied to the vehicle routing problem （VRP） and they all have their shortcoming. Modified hybrid particle swarm optimization （MHPSO） is proposed. The insufficiency of the traditional PSO in solving the discrete and combination optimization problem based on updating mechanism of velocity - position is analyzed. Considering the large lost in swarm diversity during the evolution, diversity - measure is introduced into the proposed algorithm. In order to utilize the ergodicity, stochastic property and regularity of chaos, it constructs the initial solution with chaos. It discusses the solving strategy of VRP based on MHPSO. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithms （GA）. It shows that MHPSO has better performance, and can quickly find the optimum or approximate solution. The results also demon- strate the advantage of HPSO in searching efficiency and premature problem.
出处
《计算机仿真》
CSCD
2008年第4期267-270,共4页
Computer Simulation
关键词
车辆路径问题
粒子群优化
群智能
优化
Vehicle muting problem
Particle swarm optimization
Swarm intelligence
Optimization
