CAJ | 학술논문

标准粒子群算法（PSO）在求解多旅行商问题（MTSP）时易发生早熟收敛，为此提出一种新的加速度粒子群算法。借鉴力学思想将粒子的运动描述为受力以后在解空间中的搜索运动，粒子受个体最优、全局最优的牵引力，并受局部最优的排斥力，加速度由粒子所受的合力决定。通过审敛操作判断早熟收敛，当发生早熟时局部最优对所有粒子产生的排斥力使种群跳出局部最优继续搜索。为进一步提高算法效率，针对 MTSP 问题的特点设计了基于维度的粒子学习策略和编解码方法。仿真结果表明，该算法能够有效克服早熟收敛，从而提高解的收敛性和稳定性，为 MTSP 问题提供了一种可行的方法。
표준입자군산법（PSO）재구해다여행상문제（MTSP）시역발생조숙수렴，위차제출일충신적가속도입자군산법。차감역학사상장입자적운동묘술위수력이후재해공간중적수색운동，입자수개체최우、전국최우적견인력，병수국부최우적배척력，가속도유입자소수적합력결정。통과심렴조작판단조숙수렴，당발생조숙시국부최우대소유입자산생적배척력사충군도출국부최우계속수색。위진일보제고산법효솔，침대 MTSP 문제적특점설계료기우유도적입자학습책략화편해마방법。방진결과표명，해산법능구유효극복조숙수렴，종이제고해적수렴성화은정성，위 MTSP 문제제공료일충가행적방법。
To overcome the premature convergence of the standard particle swarm optimization (PSO)in solving multiple travelling salesman problems (MTSP),a new acceleration particle swarm optimization is constructed.Rely on the idea of mechanics,the movement of particle is de-scribed as search motion driving by force in solution space.The particle is attracted by personal best force,global best force and repelled by local best force.Thus the acceleration of particle de-pends on the resultant of forces.Using convergence criterions to estimate premature conver-gence,the local best will repel all the particles when premature convergence occurs,so the parti-cle swarm can jump out the local best and continue to search.In order to improve the efficiency of the algorithm,a dimensional learning strategy of particle and a new coding method are designed for MTSP.The simulation results show that the proposed algorithm can effectively overcome the premature convergence,and improve the quality and stability of solutions.Thus it provides a fea-sible method for MTSP.