摘要
针对原始花授粉算法(FPA)无法用于求解组合优化问题,提出一种离散的花授粉算法,并将其应用于求解旅行商问题(TSP)。通过重新定义花朵、全局搜索与局部搜索等概念;并对莱维飞行用一种新的方法进行分段,有效避免算法过早陷入局部最优,增强算法的全局搜索能力。最后通过对10个国际通用的TSP数据(TSPLIB)进行测试,并将实验结果与离散粒子群算法(DPSO)、混合离散粒子群算法(HDPSO)、离散布谷鸟搜索(DCS)算法、带有遗传模拟退火的蚁群粒子群(GSA-ACS-PSOT)算法的实验结果进行对比。实验数据显示,该算法在求解旅行商问题中,能较快、较准确地找到最优解,在相同实验条件下,比其他算法求解偏差百分比明显降低。研究结果表明,本文提出的算法具有较好的求解性能。
In view of the standard Flower Pollination Algorithm （ FPA） can not be used to solve combinatorial optimization prob-lem, a Discrete Flower Pollination Algorithm（DFPA） was proposed and then applied to Traveling Salesman Problem （TSP）.By redefining the concept of flowers , global search and local search , etc., and Levy flight will be segmented by a new method , ef-fectively avoiding premature falling into local optimum and enhancing the global search ability of the algorithm .Finally the per-formance of the proposed is tested against by some typical instances in the internationally commonly used library of TSP （ TSPLIB） and compared with Discrete Particle Swarm Optimization （DPSO）, Hybrid Discrete Particle Swarm Optimization（HDPSO）, Dis-crete Cuckoo Search （ DCS） , Genetic Simulated Annealing Ant Colony System with Particle Swarm Optimisation Technique （ GSA-ACS-PSOT） .The results of the tests show that DFPA can quickly , accurately find the optimal solution .Under the same experi-mental conditions , the algorithm reduces the error rate than any other algorithm .The results show that the proposed algorithm has better solving performance .
出处
《计算机与现代化》
2016年第7期37-43,共7页
Computer and Modernization
基金
陕西省自然科学基础研究计划项目(2014
JM1006)
