为提升对时间效率要求较高的通航物流系统整体运行效能,设计和提出了一种新型变种群极搜索遗传算法(PSGA).通过在算法逻辑结构上对传统遗传算法(GA)进行重新设计,同时创新性地设计和引入一种适应度调和因子,使PSGA的算法效率较传统GA算法有了明显提升.经过2个不同复杂度的函数寻优测试显示,PSGA在效率上分别高出GA35.35%和43.50%;最后,通过实际案例应用表明,PSGA的收敛效率高出GA25代,优化精度高出GA1.46.测试与应用结果说明,PSGA算法在通航物流系统效能优化中具有较好的有效性和适用性.
In order to enhance the performance of the general aviation logistics system which requires mach higher time efficiency, this paper designs and proposes a new variable population and pole search genetic algorithm(PSGA). By redesigning the traditional genetic algorithm(GA)in the logical structure, and innovatively designing and introducing a fitness factor, the algorithm efficiency of PSGA is made higher than that of traditional GA.Through two function optimization tests with different complexity,the efficiency of PSGA is made higher than that of GA for 35.35% and 43.50%. Finally, the practical application shows that the convergence efficiency of PSGA is higher than that of GA for 25 generations and the optimization accuracy is higher than that of GA for 1.46. The tests and application results show that PSGA algorithm has better effectiveness and applicability in the performance optimization of general aviation logistics system.
[1] 钱海军.采用空间编码与正弦选择算子遗传算法求解排课问题[J].计算机与数字工程,2017,45(10):1924- 1930.DOI:10.3969/j.issn.1672-9722.2017.10.008.
[2] 赵鑫宁,喻歆,吴锡.一种基于混合概率选择算子的改进遗传算法[J].成都信息工程大学学报,2016,31(3): 247-254.DOI:10.16836/j.cnki.jcuit.2016.03.003.
[3] 陈灿辉,朱红,詹景坤,等.一种新的遗传算法交叉算子及其在GNSS星座选择中的应用[J].计算机测量与控制,2015,23(10):3452-3454.DOI:10.16526/j.cnki.11-4762/tp.2015.10.059.
[4] 董妍汝.基于选择算子的遗传算法改进[J].办公自动化,2015(16):59-61.DOI:10.3969/j.issn.1007-001X.2015.08.030.
[5] 王金敏,朱丽苹,甄士刚.一种基于蜜蜂进化选择算子的布局遗传算法[J].图学学报,2014,35(5):690-696.DOI:CNKI:SUN:GCTX.0.2014-05-006.
[6] 谢盛嘉.一种改进选择算子的遗传算法在泊松曲线沉降预测模型中的应用[J].计算机与现代化,2012(7): 6-8.DOI:10.3969/j.issn.1006-2475.2012.07.002.
[7] 杨侃,郑姣,郝永怀,等.三角函数选择算子的遗传算法在梯级水库优化调度中的应用[J].天津大学学报, 2012,45(2):167-172.DOI:CNKI:SUN:TJDX.0.2012-02-015.
[8] ZHANG J Y,MENG Q X,FENG X G,et al. A 6-DOF robot-time optimal trajectory planning based on an improved genetic algorithm[J]. Robotics and Biomimetics,2018,5(1):1-7.DOI:10.1186/s40638-018-0085-7.
[9] SENTHILKUMAR V,JAYAPRAKASH G.Hybrid neural network-particle swarm optimization algorithm and neural network-genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2019,41(8):1-15.DOI:10.1007/s40430-019-1830-8.
[10] MUSTAFI D,SAHOO G.A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the k-means algorithm with applications in text clustering[J]. Soft Computing, 2019,23(15):6361-6378.DOI:10.1007/s00500-018-3289-4.
[11] 范媛,李文锋,贺利军.基于改进遗传算法的智能仓储多移动机器人协同调度[J].武汉理工大学学报(信息与管理工程版),2019,41(3):293-298.DOI:10.3963/j.issn.2095-3852.2019.03.012.
