A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

Wu, Hongguang; Gao, Yuelin; Wang, Wanting; Zhang, Ziyu

doi:10.1007/s40747-021-00401-1

Cited by 21 publications

(16 citation statements)

References 44 publications

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“…GA-LNS reduced the vehicle number effectively, an average of five fewer vehicles compared with the saving algorithm, it is basically the same as the fleet size solved by [44]. The performance of GA-LNS is slightly lower than that in [44] in terms of traveling distance, but higher than that in [40][41][42][43]. However, the solution results of most instances confirm the viability of GA-LNS approaches for traveled distance minimization in the VRPTW and reduction of distribution cost.…”

Section: Instances and Results Comparisonmentioning

confidence: 88%

“…In Table 6, the saving algorithm, LNS, simulated annealing algorithm (SA), GA, ant colony optimization (ACO) as well as simulated annealing algorithm based on large neighborhood search (SA-LNS) and improved ant colony algorithm (HACO) that can represent hybrid optimization algorithms were adopted, respectively. References [40][41][42][43][44] use the same as numerical instances this study which can used for an indirect comparison with the algorithm performance. The details of these proposed algorithms can be found in the relevant references, and the results of a comparison of these algorithms are given in Table 6 together.…”

Section: Instances and Results Comparisonmentioning

confidence: 99%

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Heterogeneous Multitype Fleet Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in Flexible Manufacturing System

et al. 2022

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In this study, we present and discuss a variant of the classical vehicle routing problem (VRP), namely the heterogeneous multitype fleet green automated guided vehicle (AGV) routing problem with time windows (HFGVRPTW) applied in the workshops of flexible manufacturing systems (FMS). Specifically, based on the analysis of AGV body structure and motion state, transport distance and energy consumption are selected as two optimization objectives. According to the characteristics and application context of the problem, this paper designs a hybrid genetic algorithm with large neighborhood search (GA-LNS) considering the farthest insertion heuristic. GA-LNS is improved by increasing the local search ability of genetic algorithm to enhance the solution optimal quality. Extensive computational experiments which are generated from Solomon’s benchmark instances and a real case of FMS are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results reveal that compared with using the traditional homogeneous fleet, the heterogeneous multitype AGV fleet transportation mode has a huge energy-saving potential in workshop intralogistics.

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Section: Instances and Results Comparisonmentioning

confidence: 88%

Section: Instances and Results Comparisonmentioning

confidence: 99%

Heterogeneous Multitype Fleet Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in Flexible Manufacturing System

et al. 2022

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“…In 2020, Mirjalili was inspired by the changes in the sine and cosine functions in mathematics and proposed the sine-cosine algorithm (SCA) [17]. These algorithms are used to solve various problems in life [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network

Zheng

Liu

Chai

et al. 2023

Complex Intell. Syst.

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Sensor technology is developing rapidly and up to date. The lifetime of the Wireless Sensor Network (WSN) has also attracted many researchers, and the location of the Base Station (BS) plays a crucial role in prolonging the lifetime. The energy consumption in the WSN occurs during transmission of data and selection of cluster-head nodes. A reasonable location of the BS prolongs the lifetime of the WSN. This study proposes a Levy Flight Edge Regeneration Black Hole algorithm (LEBH) to speed up convergence and enhance optimization capabilities. The performance of LEBH and other heuristic algorithms are compared on CEC 2013. The result shows that the LEBH outperforms other heuristics in most cases. In this study, the energy consumption and WSN models are simulated. Subsequently, the proposed LEBH is combined with relocation technology to change the location of the BS to prolong the lifetime. Simulation results show LEBH-BS prolongs the lifetime of the WSN better than random-base and static-base stations and other heuristic algorithms in most cases.

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“…However, most of these methods suffer from local optima and expensive computational costs due to the use of greedy search methods [35,36]. Over the past three decades, Evolutionary Algorithms (EA) have been very reliable in solving various optimization problems, such as image processing [37], intrusion detection [38], path planning [39], particle filtering [40], production scheduling [22], support vector machines [41], home healthcare [42], wireless sensors [43], and neural network models [44].…”

Section: Introductionmentioning

confidence: 99%

A Tent Lévy Flying Sparrow Search Algorithm for Wrapper-Based Feature Selection: A COVID-19 Case Study

2023

Self Cite

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The “Curse of Dimensionality” induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big data and improve accuracy. As a recently proposed algorithm, the Sparrow Search Algorithm (SSA) shows its advantages in the FS tasks because of its superior performance. However, SSA is more subject to the population’s poor diversity and falls into a local optimum. Regarding this issue, we propose a variant of the SSA called the Tent Lévy Flying Sparrow Search Algorithm (TFSSA) to select the best subset of features in the wrapper-based method for classification purposes. After the performance results are evaluated on the CEC2020 test suite, TFSSA is used to select the best feature combination to maximize classification accuracy and simultaneously minimize the number of selected features. To evaluate the proposed TFSSA, we have conducted experiments on twenty-one datasets from the UCI repository to compare with nine algorithms in the literature. Nine metrics are used to evaluate and compare these algorithms’ performance properly. Furthermore, the method is also used on the coronavirus disease (COVID-19) dataset, and its classification accuracy and the average number of feature selections are 93.47% and 2.1, respectively, reaching the best. The experimental results and comparison in all datasets demonstrate the effectiveness of our new algorithm, TFSSA, compared with other wrapper-based algorithms.

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A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

Cited by 21 publications

References 44 publications

Heterogeneous Multitype Fleet Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in Flexible Manufacturing System

Heterogeneous Multitype Fleet Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in Flexible Manufacturing System

Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network

A Tent Lévy Flying Sparrow Search Algorithm for Wrapper-Based Feature Selection: A COVID-19 Case Study

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