共查询到19条相似文献,搜索用时 218 毫秒
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针对考虑废物包装时间的车辆回收路径规划问题,建立问题数学模型,提出禁忌搜索算法与模因算法求解该问题,并与爬山算法、遗传算法进行对比.模因算法是爬山算法和遗传算法的结合.实验结果表明:在解的质量方面,禁忌搜索算法与模因算法所求出的解的质量要远远好于另外两种算法,但在运行时间上,禁忌搜索、爬山算法与遗传算法要远优于模因算法. 相似文献
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针对差分进化算法求解组合优化问题存在的局限性,引入计算机语言中的2种按位运算符,对差分进化算法的变异算子进行重新设计,用来求解不确定需求和旅行时间下同时取货和送货的随机车辆路径问题(SVRPSPD)。通过对车辆路径问题的benchmark问题和SVRPSPD问题进行路径优化,并同差分进化算法和遗传算法的计算结果进行比较,验证了离散差分进化算法的性能。结果表明,离散差分进化算法在解决复杂的SVRPSPD问题时,具有较好的优化性能,不仅能得到更好的优化结果,而且具有更快的收敛速度。 相似文献
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基于粒子群遗传算法的泊车系统路径规划研究 总被引:1,自引:0,他引:1
针对智能停车库自动导引运输车(automated guided vehicle,AGV)存取车路径规划问题,提出了一种基于粒子群和遗传算法的动态自适应混合算法.在标准粒子群算法和遗传算法的基础上,通过引入动态自适应调整策略分别对惯性权重系数、学习因子以及交叉变异概率公式进行了优化.在进化初期,通过在惯性权重系数和学习因子之间建立动态联动关系来实现对粒子速度和位置的实时有效更新;在进化后期,通过引入自适应遗传算法的交叉、变异操作来增强混合算法的全局搜索能力,提高算法的进化速度和收敛精度.为验证混合算法的可行性和有效性,选用MATLAB软件对其进行仿真测试.仿真测试结果显示,与禁忌搜索算法、蚁群算法以及遗传算法相比,混合算法表现出较强的全局搜索能力和较好的收敛性能,表明混合算法可行和有效. 相似文献
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提出一种密度峰值聚类 (density peak clustering, DPC)与遗传算法(genetic algorithm, GA)相结合的新型混合算法(density peak clustering with genetic algorithm, DGA),求解带时间窗的车辆路径问题。首先应用DPC对客户进行聚类以缩减问题规模,再将聚类后的客户用GA进行线路优化。结果表明:DGA在9个数据集上的平均值比模拟退火(simulated annealing, SA)和禁忌搜索(Tabu)分别提高了13.41%和4.7%,单个数据集最大提高了26.4%。这证明了该算法是求解车辆调度问题的高效算法。 相似文献
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包装物回收物流中的车辆路径优化问题 总被引:2,自引:2,他引:0
目的提高遗传算法(GA)求解包装物回收车辆路径优化问题的性能。方法通过对传统GA算法的改进,提出混合蜂群遗传算法(HBGA)。首先改进传统GA算法的初始种群生成方式,设计初始种群混合生成算子;其次,提出最大保留交叉算子,对优秀子路径进行保护;然后,在上述改进的基础上引入蜜蜂进化机制,用以保证种群多样性和优秀个体特征信息的利用程度;最后,对标准算例集进行仿真测试。结果与传统GA算法相比,HBGA算法在全局寻优能力、算法稳定性和运行速度方面均有所改善。HBGA算法的全局寻优能力和算法稳定性均优于粒子群算法(PSO)、蚁群算法(ACO)和禁忌搜索算法(TS),但运行速度稍慢于TS算法。结论对传统GA算法的改进是合理的,且HBGA算法整体求解性能优于PSO算法、ACO算法和TS算法。 相似文献
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《软包装商情》2017,(11)
目的提高遗传算法(GA)求解包装物回收车辆路径优化问题的性能。方法通过对传统GA算法的改进,提出混合蜂群遗传算法(HBGA)。首先改进传统GA算法的初始种群生成方式,设计初始种群混合生成算子;其次,提出最大保留交叉算子,对优秀子路径进行保护;然后,在上述改进的基础上引入蜜蜂进化机制,用以保证种群多样性和优秀个体特征信息的利用程度;最后,对标准算例集进行仿真测试。结果与传统GA算法相比,HBGA算法在全局寻优能力、算法稳定性和运行速度方面均有所改善。HBGA算法的全局寻优能力和算法稳定性均优于粒子群算法(PSO)、蚁群算法(ACO)和禁忌搜索算法(TS),但运行速度稍慢于TS算法。结论对传统GA算法的改进是合理的,且HBGA算法整体求解性能优于PSO算法、ACO算法和TS算法。 相似文献
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Mathematical modelling of container transfers and storage locations at seaport terminals 总被引:2,自引:2,他引:0
This paper models the seaport system with the objective of determining the optimal storage strategy and container-handling schedule. It presents an iterative search algorithm that integrates a container-transfer model with a container-location model in a cyclic fashion to determine both optimal locations and corresponding handling schedule. A genetic algorithm (GA), a tabu search (TS) and a tabu search/genetic algorithm hybrid are used to solve the problem. The implementation of these models and algorithms are capable of handling the very large problems that arise in container terminal operations. Different resource levels are analysed and a comparison with current practise at an Australian port is done. 相似文献
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With the expansion of the application scope of social computing problems,
many path problems in real life have evolved from pure path optimization problems to
social computing problems that take into account various social attributes, cultures, and
the emotional needs of customers. The actual soft time window vehicle routing problem,
speeding up the response of customer needs, improving distribution efficiency, and
reducing operating costs is the focus of current social computing problems. Therefore,
designing fast and effective algorithms to solve this problem has certain theoretical and
practical significance. In this paper, considering the time delay problem of customer
demand, the compensation problem is given, and the mathematical model of vehicle path
problem with soft time window is given. This paper proposes a hybrid tabu search (TS) &
scatter search (SS) algorithm for vehicle routing problem with soft time windows
(VRPSTW), which mainly embeds the TS dynamic tabu mechanism into the SS
algorithm framework. TS uses the scattering of SS to avoid the dependence on the quality
of the initial solution, and SS uses the climbing ability of TS improves the ability of
optimizing, so that the quality of search for the optimal solution can be significantly
improved. The hybrid algorithm is still based on the basic framework of SS. In particular,
TS is mainly used for solution improvement and combination to generate new solutions.
In the solution process, both the quality and the dispersion of the solution are considered.
A simulation experiments verify the influence of the number of vehicles and maximum
value of tabu length on solution, parameters’ control over the degree of convergence, and
the influence of the number of diverse solutions on algorithm performance. Based on the
determined parameters, simulation experiment is carried out in this paper to further prove
the algorithm feasibility and effectiveness. The results of this paper provide further ideas
for solving vehicle routing problems with time windows and improving the efficiency of
vehicle routing problems and have strong applicability. 相似文献
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This paper considers the no-wait job shop (NWJS) problem with makespan minimisation criteria. It is well known that this problem is strongly NP-hard. Most of the previous studies decompose the problem into a timetabling sub-problem and a sequencing sub-problem. Each study proposes a different sequencing and timetabling algorithm to solve the problem. In this research, this important question is aimed to be answered: is the timetabling or the sequencing algorithm more important to the effectiveness of the developed algorithm? In order to find the answer, three different sequencing algorithms are developed; a tabu search (TS), a hybrid of tabu search with variable neighbourhood search (TSVNS), and a hybrid of tabu search with particle swarm optimisation (TSPSO). Afterwards, the sequencing algorithms are combined with four different timetabling methods. All the approaches are applied to a large number of test problems available in the literature. Statistical analysis reveals that although some of the sequencing and timetabling algorithms are more complicated than the others, they are not necessarily superior to simpler algorithms. In fact, some of the simpler algorithms prove to be more effective than complicated and time-consuming methods. 相似文献
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This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions. 相似文献
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A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem
Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems. 相似文献
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The hybrid flow-shop scheduling problem (HFSP) has been of continuing interest for researchers and practitioners since its advent. This paper considers the multistage HFSP with multiprocessor tasks, a core topic for numerous industrial applications. A novel ant colony system (ACS) heuristic is proposed to solve the problem. To verify the developed heuristic, computational experiments are conducted on two well-known benchmark problem sets and the results are compared with genetic algorithm (GA) and tabu search (TS) from the relevant literature. Computational results demonstrate that the proposed ACS heuristic outperforms the existing GA and TS algorithms for the current problem. Since the proposed ACS heuristic is comprehensible and effective, this study successfully develops a near-optimal approach which will hopefully encourage practitioners to apply it to real-world problems. 相似文献
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目的为了提高蝙蝠算法(BA)求解包装废弃物逆向物流问题的性能。方法在标准BA算法的基础上提出混合蝙蝠算法(HBA)。首先,构建新型蝙蝠表达式,使BA算法适用于包装废弃物逆向物流问题的求解。其次,引入自适应惯性权重,改造蝙蝠速度更新公式;然后,引入粒子群算法(PSO),对每次迭代中任一随机蝙蝠进行粒子群操作;最后,利用HBA算法对企业实例和标准算例进行仿真测试。结果企业最优回收距离为776.63 km。与遗传算法(GA)、蚁群算法(ACO)和禁忌搜索算法(TS)相比,HBA算法能够求得已知最优解的标准算例个数最多为6个,求得最好解与已知最优解的平均误差最小为8.58%,平均运行时间最短为4.39s。结论 HBA算法的全局寻优能力、稳定性和运行速度均优于GA算法、ACO算法和TS算法。 相似文献
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为解决单车场容量约束车辆调度问题提出了一种改进禁忌算法.在传统的禁忌算法思想中,它的解受算法的唯一初始解的状态影响很大,因此优化结果的稳定性得不到保证.此改进算法使用多初始解和全局禁忌表,它能够减小解的不稳定性和扩大搜索范围.与标准禁忌算法比较,它的全局搜索能力和稳定性都大大增强.通过算例试验,取得了良好的结果. 相似文献