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A hybrid ant colony optimization algorithm for drone-based cooperative EV routing problem with simultaneously delivery and pickup

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ANNALS OF OPERATIONS RESEARCH 2025年 1-36页

作者： Yang, Yongjian Feng, Tianjiao Luo, Chenyu Li, Chenglong Wang, Jun Xiong, Yuning Southwestern Univ Finance & Econ Sch Management Sci & Engn Chengdu 610064 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610064 Peoples R China Xihua Univ Sch Econ Chengdu 610039 Peoples R China

With the rapid development of the logistics industry and the enhancement of human environmental protection consciousness, the economic, environmental, and social values of reverse logistics are gradually being highlighted. To improve the efficiency of delivery and pickup in reverse logistics, this paper integrates drones into the transportation system to form a new type of drone-based cooperative EV transport mode, where each EV carries only one drone and the drone can only take off and land on the corresponding EV. In this transport mode, each EV and the drone can perform both delivery and pickup tasks, but when the drone takes off from either customer point, the EV needs to wait in place for the return of the drone. During transportation, the energy consumption of both EVs and drones is taken into account. In case an EV runs out of power, it can be recharged at recharging stations. The objective is to find a synthetic route for the drone-based cooperative EV combination, aiming to minimize the aggregate costs of distribution and transportation while deducting the total revenue of pickups. To solve this problem, we design an efficient hybrid ant colony optimization (Haco) algorithm containing several mutation operators and a large neighborhood search (LNS) algorithm. Extensive numerical studies are conducted to clarify the accuracy and efficiency of the developed algorithms, to analyze the advantages of the drone-based cooperative EV transport mode and the hybrid recharging strategy at recharging stations, and to discuss the impact of some of the key parameters on the model, which in turn provides effective management insights for managers. Additionally, we demonstrated the performance of our model with a case study of delivery and pickup of medical supplies in Shenzhen, China. We also provide effective management insights for decision makers based on the experimental results.

关键词： aco algorithm Delivery and pickup Transportation Drone-based cooperative EV routing problem

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A Priori Constrained aco Method Applied to Three-Dimensional Imaging of Subsurface Electrical Resistivity

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2023年第1期41卷 15-25页

作者： Guo, Qian Wang, Hai Bai, Jian Liu, Benchao Wu, Honglin Wu, Zhenyu He, Zhou Univ Jinan Sch Water Conservancy & Environm 336 Nanxinzhuang Rd Jinan 250022 Peoples R China Shandong Univ Geotech & Struct Engn Res Ctr Jinan 250061 Peoples R China

Electrical resistivity imaging seeks to reconstruct conductivity model of subsurface water-bearing body for disaster reduction and resource protection. Since its high non-linearity, the inverse problem relying mainly on linear least-square method may inclined to consider much deeper adoption of non-linear algorithm. Ant colony optimization (aco) is a standard probabilistic algorithm used to find optimal paths. Since the direct application of the aco is unstable, there are many false anomalies, and prior information needs to be applied according to the laws of physical properties. This study proposed a priori constrained approach to improving ant colony optimization method wherein prior information is included as regularization term in search process. First, we introduce foraging path smoothness into objective function to reduce non-uniqueness. On this basis, smooth constraint of resistivity is added into visibility and inequality constraint is added into pheromone intensity, by which some unnecessary nodes of foraging paths are avoided form selecting by ants. Finally, the strategy of global minimum is introduced in the process of updating pheromone. Forward modeling of each ant selected is parallel calculated via shared-memory computing strategy. Taking into account of those, the direction of global minimum is maintained and the search efficiency is improved greatly, which makes the constrained aco feasible for the 3-D resistivity inversion. Both numerical experiments and field application is conducted to evaluate this method and illustrate that we can obtain the improved inversion results of subsurface water-bearing body.

关键词： Electrical resistivity imaging aco algorithm Priori constraint Subsurface water-bearing body Field test

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Metaheuristic (Ant Colony Optimization) algorithm-Based Optimization of a Circular Shaped Patch Antenna for Medical Purposes

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SN Computer Science 2024年第7期5卷 918页

作者： Mahesh, D.S. Naveen, K.B. Electronics and Communication Engineering Department BGS Institute of Technology Adichunchanagiri University Karnataka Mandya India

This paper presents the design, simulation, optimization, fabrication and testing of a circular shape based antenna, ant colony optimization (aco) algorithm was chosen for antenna optimization. The antenna considered here was fed by a 1.59 mm diameter coaxial cable, the location of feeding and the radius of circular shape was varied by ant colony optimization algorithm (aco). Finding a set of settings that would enhance the antenna's performance was the optimization's goal. We have made use of MATLAB software to implement aco algorithm. The Ansys HFSS software which was used to design antenna allows MATLAB to be interfaced with it for optimization purpose. The parameters considered were return loss (reflection coefficient S₁₁) and the voltage standing wave ratio (VSWR). The aco yielded a design with return loss (S₁₁) of − 25.85 dB and VSWR of 1.24 at 2.48 GHz. The entire antenna dimensions will be approximately (5 cm × 5 cm). The substrate used is FR4 (flame retardant) epoxy. There was strong good agreement between the measurements from the antenna's (fabrication) manufacturing and simulation for the operational frequencies and desirable performance in gain, bandwidth and VSWR (voltage standing wave ratio) parameters. The VSWR achieved values was lower than 1.4 for the frequencies used. Additionally, the simulations portray a broad radiation pattern and shows good gain and directivity. The antenna which was fabricated was tested using Anechoic chamber and network analyser. This tested antenna was used in wearable health care application due to its small size. At the transmitter side we used temperature and pulse reading sensors to collect physiological information like body temperature and heart rate from the patient, these sensors were connected to Arduino microcontroller then to transmit this physiological information, NRF24L01 transreceiver module along with our fabricated aco optimised antenna was used, which is connected to Arduino microco

关键词： aco algorithm Return loss VSWR

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Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第1期25卷 579-595页

作者： Belgacem, Ali Beghdad-Bey, Kadda Mhamed Bougara Univ Boumerdes Algeria Ecole Mil Polytech Algiers Algeria

Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-aco approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (aco) to minimize them. Experimental simulations are performed on three types of real-world science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic aco, PEFT-aco, and FR-MOS.

关键词： Cloud computing Workflow scheduling Resource allocation Makespan Cost aco algorithm

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Fair Allocation Game Approach for Cloud-Hosted Applications by Ant Colony Optimization algorithm 2nd

Fair Allocation Game Approach for Cloud-Hosted Applications ...

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2nd International Conference on From Smart City to Smart Factory for a Sustainable Future, SCFF 2024

作者： Bui, Thanh-Khiet Thu Dau Mot University Binh Duong Province Thu Dau Mot City Viet Nam

ISBN: (纸本)9783031656552

Service providers and end-users have significant challenges in managing resources in cloud computing. Resource allocation will be developed by leveraging the user’s resource needs and the level of service quality. The available physical resources in a cloud computing environment are diverse, and the resource demands from users likewise vary in terms of resource types. Allocating resources can be challenging, especially in situations when resources are limited. This study involved the construction of allocation model that aimed to achieve fairness across physical servers and maximize resource utilization using game theory principles. An Ant Colony Optimization algorithm will be utilized to develop a fair resource allocation approach. The simulation results demonstrate the utilization of parameters to achieve equitable virtual machine allocation and benchmarking using Round-Robin, Ant system, and Max-Min ant system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： aco algorithm Cloud-hosted applications Fair allocation

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Optimization analysis of node energy consumption in wireless sensor networks based on improved ant colony algorithm

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2024年 64卷

作者： Wang, Long Luo, Yiqun Yan, Hongyan Woosuk Univ Grad Sch Dept Energy Elect Engn Jincheon Gun 27841 Chungbuk Do South Korea Xiangnan Univ Coll Phys & Elect & Elect Engn Chenzhou 423000 Hunan Peoples R China Hebei Univ Engn Sch Water Conservancy & Hydroelect Power Handan 056038 Hebei Peoples R China

Wireless sensor network (WSN) originated from the traditional sensor system "Tropical Tree" sensor used in Vietnam War. "Tropical Tree" will automatically transmit the target information sensed by the detector to the server center through the wireless network, so as to monitor and direct the information. Aiming at the above phenomenon, this paper proposes an optimized Ant colony (aco) algorithm based on the traditional aco algorithm. By introducing the improved pseudo-random proportional rule to optimize the state transition formula, the phenomenon of premature stagnation of the algorithm is effectively avoided. For the research done, the details of different algorithms are described, including comparing the time of dead nodes after several rounds of simulation. When the number of rounds reaches 100, the data mining algorithm with the highest number of dead nodes is 284. Secondly, data mining algorithm, the number of nodes is 201. Finally, the method in this paper has 180 nodes. In WSN, node failures and low energy sometimes occur, which lead to topology changes and path failures. Ant colony algorithm has great advantages in solving dynamics. Even if one of the individuals can't work normally, other individuals can continue to work according to certain rules. It can be seen from the experimental results that the introduction of energy factor at the same time enables the ant colony algorithm to select the optimal path with shorter length and higher energy, thus prolonging the network life. The search speed will change as the search time changes. It increased by about 52 %.

关键词： aco algorithm Wireless sensor Network energy Routing algorithm

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River water quality management using a fuzzy optimization model and the NSFWQI Index

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WATER SA 2021年第1期47卷 45-53页

作者： Ghorbani, Mohammad Kazem Afshar, Abbas Hamidifar, Hossein Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Shiraz Univ Water Engn Dept Shiraz Iran

In this study, a novel multiple-pollutant waste load allocation (WLA) model for a river system is presented based on the National Sanitation Foundation Water Quality Index (NSFWQI). This study aims to determine the value of the quality index as the objective function integrated into the fuzzy set theory so that it could decrease the uncertainties associated with water quality goals as well as specify the river's water quality status rapidly. The simulation-optimization (S-O) approach is used for solving the proposed model. The QUAL2K model is used for simulating water quality in different parts of the river system and ant colony optimization (aco) algorithm is applied as an optimizer of the model. The model performance was examined on a hypothetical river system with a length of 30 km and 17 checkpoints. The results show that for a given number of both the simulator model runs and the artificial ants, the maximum objective function will be obtained when the regulatory parameter of the aco algorithm (i.e., q(0)) is considered equal to 0.6 and 0.7 (instead of 0.8 and 0.9). Also, the results do not depend on the exponent of the membership function (i.e.,gamma). Furthermore, the proposed methodology can find optimum solutions in a shorter time.

关键词： aco algorithm fuzzy set theory multiple pollutant NSFWQI QUAL2K model waste load allocation model

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Application of Improved Multi-Objective Ant Colony Optimization algorithm in Ship Weather Routing

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Journal of Ocean University of China 2021年第1期20卷 45-55页

作者： ZHANG Guangyu WANG Hongbo ZHAO Wei GUAN Zhiying LI Pengfei State Key Laboratory on Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012China CRRC Changchun Railway Vehicles Co. Ltd.Changchun 130000China

This paper presents a novel intelligent and effective method based on an improved ant colony optimization(aco)algorithm to solve the multi-objective ship weather routing optimization problem,considering the navigation safety,fuel consumption,and sailing *** the improvement of the aco algorithm is mainly reflected in two ***,to make the classical aco algorithm more suitable for long-distance ship weather routing and plan a smoother route,the basic parameters of the algorithm are improved,and new control factors are ***,to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems,the related operations of crossover,recombination,and mutation in the genetic algorithm are introduced in the improved aco *** final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization *** addition,the black-box model method was used to study the ship fuel consumption during a voyage;the model was constructed based on an artificial neural *** parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship,and then the trained black-box model was used to predict the future fuel consumption of the test *** with other fuel consumption calculation methods,the black-box model method showed higher accuracy and applicability.

关键词： multi-objective optimization weather routing aco algorithm fuel consumption

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A new approach for solving the flow-shop scheduling problem using a parallel optimization algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第12期12卷 10723-10732页

作者： Nazif, Habibeh Payame Noor Univ PNU Dept Math POB 19395-4697 Tehran Iran

In any organization and business, efficient scheduling cause increased efficiency, reducing the time required to complete jobs and increasing an organization's profitability in a competitive environment. Also, the flow-shop scheduling problem is a vital type of scheduling problem with many real-world applications. Flow-shop scheduling has numerous exciting applications in various manufacturing and industrial domains. During the past eras, the growing interests in the arrangement of flow shops with diverse objective functions (for example, minimizing the makespan and flow-time) were observed. The permutation flow-shop is formulated as mixed-integer programming, and it is an NP-Hard problem. Therefore, in this paper, a novel method is provided to decrease the makespan and completion time. Since parallel algorithms use some computing elements to accelerate the search and present a new exploration pattern that is frequently suitable to enhance the quality of the results, in this research, a parallel ant colony optimization algorithm is employed to solve the mentioned problem. The Matlab simulation setting in Net Beans IDE 8.0.2 and Java to simulate the introduced method is applied. According to the obtained results, the suggested procedure has more efficiency than the previous methods. The Matlab simulator outcomes have indicated that the average response time has been improved compared to the PSO-SA and HBC algorithms. Also, the makespan is improved in comparison to GA and MOACSA.

关键词： Flow‐ shop scheduling Parallel aco algorithm Optimization algorithm

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Ant colony optimization algorithm and its application 2

Ant colony optimization algorithm and its application

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2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC)

作者： Chen, Aoran Tan, Hao Zhu, Yiyue Univ Minnesota Twin Cities Dept Math Minneapolis MN 55455 USA Southwest Univ Dept Westa Chongqing 400000 Peoples R China Chongqing Univ Technol Chongqing Dept Mech Engn Chongqing 400000 Peoples R China

ISBN: (纸本)9781510655201;9781510655195

Ant Colony Optimization (aco) algorithm is a bionic algorithm simulating ant colony behavior. Ant colony can find the shortest path by sensing pheromone when looking for food. Ant colony algorithm plays an important role in solving a variety of planning problems such as traveling salesman problem (TSP). This paper mainly introduces the background knowledge of aco algorithm in entomology and computer and uses python implementation to solve an example of traveling salesman problem. This work focuses on the aco algorithm, which was inspired by the ant colony system. Starting with pheromone direction, this study concentrates on the properties and principles of the aco algorithm, and then applies the aco algorithm to solve the TSP issue successfully. Pheromone is used by the entire ant colony as an indirect communication method to address difficult challenges.

关键词： Ant colony aco algorithm TSP Problem Python

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