计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
14期
58-63
,共6页
车辆路径规划%遗传算法%反向传播(BP)算法%改进混合算法
車輛路徑規劃%遺傳算法%反嚮傳播(BP)算法%改進混閤算法
차량로경규화%유전산법%반향전파(BP)산법%개진혼합산법
vehicle routing problem%genetic algorithm%Back Propagation(BP)algorithm%improved hybrid algorithm
针对物流活动中需要找出各个配货节点之间的最短路径，用以指导物流车辆调度的问题，提出一种将遗传算法与BP神经网络相结合的新方法，规划车辆的路径，达到节约运送成本的目标。对遗传算法进行了改进，克服了遗传算法局部搜索能力差、易早熟和总体可行解质量不高的缺点。该混合算法有效弥补了遗传算法的不足，同时在遗传优化操作中引入最优保存策略，并在选择操作中采用锦标赛选择法，使算法的效率和功能得到了很大提高。通过对基于遗传算法的改进混合算法求解车辆路径优化问题的性能进行仿真，并与自适应遗传算法和免疫遗传算法进行对比分析，验证了改进混合算法的优点和有效性。
針對物流活動中需要找齣各箇配貨節點之間的最短路徑，用以指導物流車輛調度的問題，提齣一種將遺傳算法與BP神經網絡相結閤的新方法，規劃車輛的路徑，達到節約運送成本的目標。對遺傳算法進行瞭改進，剋服瞭遺傳算法跼部搜索能力差、易早熟和總體可行解質量不高的缺點。該混閤算法有效瀰補瞭遺傳算法的不足，同時在遺傳優化操作中引入最優保存策略，併在選擇操作中採用錦標賽選擇法，使算法的效率和功能得到瞭很大提高。通過對基于遺傳算法的改進混閤算法求解車輛路徑優化問題的性能進行倣真，併與自適應遺傳算法和免疫遺傳算法進行對比分析，驗證瞭改進混閤算法的優點和有效性。
침대물류활동중수요조출각개배화절점지간적최단로경，용이지도물류차량조도적문제，제출일충장유전산법여BP신경망락상결합적신방법，규화차량적로경，체도절약운송성본적목표。대유전산법진행료개진，극복료유전산법국부수색능력차、역조숙화총체가행해질량불고적결점。해혼합산법유효미보료유전산법적불족，동시재유전우화조작중인입최우보존책략，병재선택조작중채용금표새선택법，사산법적효솔화공능득도료흔대제고。통과대기우유전산법적개진혼합산법구해차량로경우화문제적성능진행방진，병여자괄응유전산법화면역유전산법진행대비분석，험증료개진혼합산법적우점화유효성。
Aiming to the requirements to find the shortest path among distribution nodes in logistics activities to guide logis-tics vehicle scheduling, this paper provides a new method which combines genetic algorithm and BP algorithm for vehicle path planning to achieve the goal of saving transporting cost. In this paper, the genetic algorithm is improved, and the shortcomings of the genetic algorithm, such as poor local search capability, easy to be early-maturing and not good enough global feasible solution, are covered. This hybrid algorithm covers the shortage of genetic algorithm efficiently, and adopts the optimal preservation strategy in the genetic optimize operation and tournament selection method in the selec-tion operation, which improves the efficiency and function of the algorithm highly. Through the simulation to the perfor-mance of this improved hybrid algorithm of vehicle path planning based on genetic algorithm, and compared with adap-tive genetic algorithm and immune genetic algorithm, and analyzing the results, the advantages and effectiveness of the hybrid algorithm are proved.
