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Metaheuristic approach to optimal pathfinding in wireless charging of sensor nodes using autonomous bots

Procedia Computer Science 2020年 171卷 2147-2156页

作者： Dinesh Kumar G Sarath. R. Menon M Pradeep Ramanathan R

Wireless Sensor Nodes play a prominent role in many military and civilian applications. They are used to collect data for various disciplines from diverse environments. The main drawback of this technology is the decrease in energy of the wireless sensor nodes as time progresses. A significant amount of works has been done and justified in using an autonomous bot to charge the wireless sensor nodes. There are a lot of difficulties to overcome to implement the concept of using an autonomous bot to charge the independent wireless sensor nodes. One such cumbersome issue is the scheming of an optimal path for the autonomous bot to charge these wireless sensor nodes efficiently. In this paper, we present a comparative study of various methods ant colony optimization algorithm, Simulated Annealing and Tabu search to compute the tour of the Autonomous bot to recharge the nodes, considering the tour length and the time taken to compute the tour length. The result of this study gives which algorithm is best suited for a given network with a given number of nodes with respect to tour length and time taken to compute the tour.

关键词： Wireless Sensor Nodes Wireless charging Mobile charger Autonomous bot Travelling Salesman Problem Metaheuristic Kruskal’s algorithm ant colony optimization algorithm Simulated Annealing Tabu search

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AUTOMATIC BOILING WATER REACTOR CONTROL ROD PATTERN DESIGN USING ant colony optimization algorithm

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NUCLEAR TECHNOLOGY 2011年第1期176卷 72-82页

作者： Wang, Cheng-Der Lin, Chaung Natl Tsing Hua Univ Dept Engn & Syst Sci Hsinchu 30013 Taiwan Inst Nucl Energy Res Nucl Engn Div Longtan Township 32546 Taoyuan County Taiwan

A method for the automatic design of a boiling water reactor (BWR) control rod (CR) pattern (CRP) was developed using the rank-based ant system, which is an effective optimization algorithm for a combinatorial optimization problem. The designed BWR CRP followed either the A2-B1-A1-B2 or the A1-B2-A2-B1 sequence in this study. After the CRP was determined, the SIMULATE-3 code was used to calculate the axial power distribution, the effective multiplication factor k(eff), the shutdown margin, and three thermal limits which were then used to evaluate the CRP and update the pheromone concentration. The developed methodology was demonstrated using design CRPs for several fuel loading patterns, showing that the designed patterns can be obtained within a reasonable computation time and with an acceptable cycle length.

关键词： boiling water reactor control rod pattern rank-based ant system ant colony optimization algorithm

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Doctor-Patient Combined Matching Problem and Its Solving algorithms

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IEEE ACCESS 2019年 7卷 177723-177733页

作者： Yu, Jing Xing, Lining Tan, Xu Ren, Teng Li, Zhenping Natl Univ Def Technol Coll Syst Engn Changsha 400073 Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518172 Peoples R China Cent South Univ Forestry & Technol Sch Transportat & Logist Changsha 410018 Peoples R China Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China

"Difficult to see a doctor" and prominent contradictions between doctors and patients in large public hospitals have become an urgent research topic in China. In this paper, we propose a multi-patient treatment mode (MTM) to improve medical efficiency and patient satisfaction. Based on the MTM, a problem named the doctor-patient combined matching problem (DPCMP) is proposed and can be described by a two-stage process: (1) The patients are grouped according to similar disease symptoms. (2) How to capture an optimal matching scheme form the grouped patients? Therefore, to solve the aforementioned problem, a mathematical model of the DPCMP is constructed, and several improved ant colony optimization algorithms are designed. Finally, certain examples verify the effectiveness and good performances of the proposed methods.

关键词： Doctor-patient contradictions patient satisfaction multi-patient treatment mode doctor-patient combined matching problem medical efficiency ant colony optimization algorithm local search operator

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A meta-heuristic for minimizing total weighted flow time on parallel batch machines

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 298-308页

作者： Jia, Zhao-hong Zhang, Han Long, Wen-tao Leung, Joseph Y-T. Li, Kai Li, Wei Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230039 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

To address the problem of minimizing the total weighted completion time on parallel batch processing machines with identical machine capacities, non-identical job sizes and unequal weights, an effective meta-heuristic based on ant colony optimization is proposed. After presenting a mathematic model of the problem, we provide an algorithm to calculate the lower bound. Then, a meta-heuristic is proposed to solve the problem. The heuristic information is defined with consideration of job weights and job sizes. Meanwhile, a candidate set for constructing the solution is used to narrow the search space. Additionally, to improve the solution quality, a local optimization strategy is incorporated. Simulation results show that the proposed algorithm is able to obtain a high-quality solution within a reasonable time, and outperforms the compared algorithms.

关键词： Parallel scheduling Patch processing machines Non-identical job sizes Total weighted completion time ant colony optimization algorithm

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UAV path path planning method for digital terrain model reconstruction - A debris fan example

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AUTOMATION IN CONSTRUCTION 2018年 93卷 214-230页

作者： Yang, Cheng-Hsuan Tsai, Meng-Han Kang, Shih-Chung Hung, Chi-Yao Natl Taiwan Univ Dept Civil Engn Taipei Taiwan Natl Taiwan Univ Ctr Weather Climate & Disaster Res Taipei Taiwan Natl Chung Hsing Univ Dept Soil & Water Conservat Taichung Taiwan

This research develops an unmanned aerial vehicle (UAV) path-planning method that aims to ensure the required image overlap and optimize the flying routes when applying the UAV for digital terrain model's (DTM) reconstruction. To collect images on a terrain for image modeling, enough overlap between each collected image must be ensured. In addition, when planning the optimized flying routes for collecting images on a debris fan, the specifications of the debris fan and the limitations of the UAV should both be taken into consideration. The path planning method takes a debris fan as an example and refers to the specifications of a debris fan and the limitations of the UAV. The developed method can help the operators to ensure the image overlap through dividing the debris fan into cells by the UAV's maximum image collection distance interval. The near-optimized UAV flying paths are calculated though applying a modified ant colony optimization algorithm (ACO). The developed method is validated to be able to help operators to sufficiently use the limited UAV batteries and evaluate the efficiency of the image collection process. A site experiment was also conducted for validating the workability of the developed method. The result of the comparison shows that the path-planning method can reduce 18.5% of the image collection time. It also confirms that applying the method on an actual debris fan can guarantee the required image overlapping and generate a complete DTM without model breaking.

关键词： Debris fan Digital terrain model Unmanned aerial vehicle Path planning ant colony optimization algorithm

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Solutions to VRP in Home Delivery Based on ant colony optimization algorithm

Solutions to VRP in Home Delivery Based on Ant Colony Optimi...

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International Conference on Micro Nano Devices, Structure and Computing Systems

作者： Hu Meng-Jie Wang Jian-Ping Li Xiao-Min Henan Inst Sci & Technol Sch Econ & Management Luoyang Peoples R China Henan Inst Sci &Technol Sch Informat Engineer Luoyang Peoples R China Henan Inst Sci & Technol Sch Mech &Elect Luoyang Peoples R China

ISBN: (纸本)9783037850091

Home delivery is a new trend in logistics at present. The distribution path planning has a great impact on customer's satisfaction and the total cost of operation in home delivery industry. In This paper, we construct the distribution path planning problems in the industry of logistics and home delivery based on ant colony optimization algorithm, the optimal vehicle's number and the best distribution path can be found in the shortest time by using the model advised in the paper. It is found that there is no obvious correlation between the service and the total costs of delivery after the analysis. So, home delivery companies can select the optimal path planning, i.e. a lower cost of delivery and higher level of service, according to their service policies.

关键词： ant colony optimization algorithm Industry of Logistics and Home Delivery Path Planning VRP

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Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第8期28卷 1801-1815页

作者： Luo, Yabo Wuhan Univ Technol Sch Mech & Elect Engn Dept Ind Engn Wuhan 430070 Peoples R China

Job Shop Scheduling Problem (JSSP) is one of classic combinatorial optimization problems and has a long research history. Modern job shop has following characteristics: increasingly complicated processes, small batch and personalized requirement, which lead to complex correlations among processes. Complex correlations of processes, involving nested correlations besides serial and parallel correlations, propose a new task for JSSP research. Decomposing JSSP into two nested sub problems of order of arranging processes and machine arrangement, this research integrates the traditional thought of complex method into the ant colony optimization (ACO) to develop a nested optimization method in order to solve the new task. This paper is divided into four parts: first, the model of JSSP with complex associated processes is constructed and the difficulties to solve which are analyzed and listed;second, the definition of "order of arranging processes" is originally proposed, based on which the mathematical model available for the complex method is developed, taking process starting time as design variables of the first level optimization. The steps of the first level optimization and the secondary nested flow chart are detailed with the demonstration of the effectiveness of the complex method's iteration mechanism;third, based on the representation of features the order of arranging processes obtained by the first level optimization combined with the first-in first-out rule owns, the corresponding modified ACO algorithm, involving pheromone positive perception and reverse spreading mechanism, is put forward to realize the second level optimization, which result is taken as the objective function value of the complex vertex to realize the secondary nested optimization strategy;finally, taking plentiful JSSP with complex associated processes as study cases, a serial of comparative experiments are done respectively adopting the genetic algorithm, ACO algorithm, particle swarm opt

关键词： Job Shop Scheduling Problem Complex method ant colony optimization algorithm Heuristic algorithm

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Proposing a new model for location - routing problem of perishable raw material suppliers with using meta-heuristic algorithms

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HELIYON 2019年第12期5卷 e03020-e03020页

作者： Yaghoubi, Ali Akrami, Farideh Raja Univ Dept Engn Qazvin Iran Ghazali Higher Educ Inst Dept Ind Management Qazvin Iran

In the last three decades, an integrated approach to optimize logistics system is considered as one of the most important aspects of optimizing supply chain management. This approach involves the ties between locations of facility, allocation of suppliers/customers, structure of transportation routes and inventory control. The aim of this paper is to investigate the ordering planning of a supply chain with multi supplier, multi distribution center, multi customer and one perishable raw material. This paper provides a mathematical model taking in consideration the limitation of raw material corruptibility (perishable material) which belongs to the category of NP-hard problems. To solve the proposed model, the ant colony optimization algorithm (ACO) and Particle Swarm optimization algorithm (PSO) are employed. In order to improve performances of ACO and PSO parameters, a Taguchi experimental design method was applied to set their proper values. Besides, to evaluate the performance of the proposed model, an example of the dairy industry is analyzed by using MATLAB R 2015a. To validate the proposed meta-heuristic algorithms, the results of them were compared with together. The results of the comparison show that ACO is greater than PSO in speed convergence rate and the number of solutions iterations.

关键词： Systems engineering Industrial engineering Mathematical modeling Computational intelligence Process modeling Industry management Supply chain management ant colony optimization algorithm Particles swarm optimization algorithm Perishable raw material

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QoS-based Semantic Micro Services Discovery and Composition using ACO algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第6期10卷 159-168页

作者： Essayah, Ahmed Youssfi, Mohamed Bouattane, Omar Mansouri, Khalifa Illoussamen, Elhocein Hassan II Univ Casablanca ENSET Mohammedia Lab SSDIA Mohammadia 28999 Morocco

In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths. In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective. The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system. Learners' requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph. Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs. In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination. Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.

关键词： Semantic micro service quality of service learning path e-learning platform service discovery and composition ant colony optimization algorithm

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ACO-based Parallel Machine Scheduling Considering Both Setup Time and Run-based Preventive Maintenance with Reliability Constraints

ACO-based Parallel Machine Scheduling Considering Both Setup...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Chen, Siqi Wang, Liya Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9781538667866

This paper studied the problem of parallel processing machine scheduling, taking both set up time and run-based preventive maintenance with reliability constraints into consideration. The objective is to minimize makespan. For this NP-hard problem, an ant colony optimization (ACO) algorithm is proposed. The node selecting probability equation is set based on characteristics of this problem. The objective value obtained by the proposed algorithm is compared to that of the classical LPT rule through numerical experiments. The experiment results imply that the proposed ACO algorithm has better performance than the LPT rule.

关键词： Parallel machine scheduling ant colony optimization algorithm preventive maintenance setup time

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