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Meta-Heuristic algorithms for Learning Path Recommender at MOOC

IEEE ACCESS 2021年 9卷 59093-59107页

作者： Son, Ngo Tung Jaafar, Jafreezal Aziz, Izzatdin Abdul Anh, Bui Ngoc Univ Teknol Petronas Dept Comp & Informat Sci Seri Iskandar 32610 Perak Malaysia FPT Univ Informat & Commun Technol Dept Hanoi 100000 Vietnam

Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic algorithm (GA) and ant colony optimization algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera's courses and Vietnam work's jobs. The test results show the effectiveness of the proposed method.

关键词： optimization Electronic learning Data mining Genetic algorithms Computer aided instruction Java ant colony optimization Learning path Knowledge-based recommendation Knowledge graph multi-objective optimization compromise programming genetic algorithm ant colony optimization algorithm

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Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low-Carbon

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IEEE ACCESS 2023年 11卷 26921-26931页

作者： Wang, Zhanzhong Wang, Yan Jiao, Yuling Jilin Univ Transportat Coll Changchun 130022 Peoples R China

Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi-objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO.

关键词： Hazardous materials Road traffic Resilience Transportation Path planning Carbon dioxide Accidents Routing Hazardous materials transportation uncertainty theory ant colony optimization algorithm resilience multi-objective

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Design of a sensitive air quality monitoring network using an integrated optimization approach

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2016年第3期30卷 779-793页

作者： Benis, Khaled Zoroufchi Fatehifar, Esmaeil Shafiei, Sirous Nahr, Fatemeh Keivani Purfarhadi, Yaser Sahand Univ Technol Environm Engn Res Ctr Fac Chem Engn POB 51335-1995 Tabriz Iran

A new method is developed to design a multi-objective and multi-pollutant sensitive air quality monitoring network (AQMN) for an industrial district. A dispersion model is employed to estimate the ground level concentration of the air pollutants emitted from different emission sources. The primary objective of AQMN is providing the maximum information about the pollutant with respect to (1) maximum coverage area, (2) maximum detection of violations over ambient air standards and (3) sensitivity of monitoring stations to emission sources. ant colony optimization algorithm (ACO) and Genetic algorithm (GA) are adopted as the optimization tools to identify the optimal configuration of the monitoring network. The comparison between the results of ACO and GA shows that the performance of both algorithms is acceptable in finding the optimal configuration of AQMN. The application of the method to a network of existing refinery stacks indicates that three stations are suitable to cover the study area. The sensitivity of the three optimal station locations to emission sources is investigated and a database including the sensitivity of stations to each source is created.

关键词： Air quality monitoring network ant colony optimization algorithm Genetic algorithm Sensitivity of monitoring station

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Layout and size optimization of sanitary sewer network using intelligent ants

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ADVANCES IN ENGINEERING SOFTWARE 2012年 51卷 49-62页

作者： Moeini, R. Afshar, M. H. Iran Univ Sci & Technol Sch Civil Engn Envirohydroinformat Ctr Excellence Tehran Iran

The incremental solution building capability of ant algorithm is exploited in this paper for the efficient layout and pipe size optimization of sanitary sewer network. Layout and pipe size optimization of sewer networks requires that the pipe locations, pipe diameters and pipe slopes are optimally determined. This problem is a highly constrained Mixed-Integer Nonlinear Programming (MINLP) problem presenting a challenge even to the modern heuristic search methods. In this paper, the ant colony optimization algorithm (ACOA) is equipped with a Tree Growing algorithm (TGA) to efficiently solve the sewer network layout and size optimization problem and its performance is compared with the conventional application of the ACOA. The method is based on the assumption that a base layout including all possible links of the network is available. The TGA is used to construct feasible tree-like layouts out of the base layout defined for the sewer network, while the ACOA is used to optimally determine the pipe diameters of the constructed layout. An assumption of sewer flow at maximum allowable relative depth is made allowing for the calculation of the optimal pipe slopes in the absence of any pump and drop in the network. Two different formulations are, therefore, proposed and their performances are tested against hypothetical problems. In the first formulation, ACOA is used in a conventional manner for pipe size optimization while an ad hoc engineering concept for the layout determination. In the second formulation, however. ACOA equipped with TGA is used to simultaneously determine both the layout and pipe sizes of the network. Proposed formulations are used to solve three hypothetical test examples of different scales and the results are presented and compared. The results indicate the ability of the proposed method to optimally solve the problem of layout and size determination of sewer networks. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： ant colony optimization algorithm Tree Growing algorithm Sanitary sewer network Layout Pipe size Mixed-Integer Nonlinear Programming

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An ant colony-based algorithm for integrated scheduling on batch machines with non-identical capacities

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APPLIED INTELLIGENCE 2022年第2期52卷 1752-1769页

作者： Jia, Zhao-hong Cui, Yu-fei Li, Kai Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Peoples R China Hefei Univ Technol Sch Management Hefei 230009 Peoples R China

In this paper, a production-distribution scheduling problem with non-identical batch machines and multiple vehicles is considered. In the production stage, n jobs are grouped into batches, which are processed on m parallel non-identical batch machines. In the distribution stage, there are multiple vehicles with identical capacities to deliver jobs to customers after the jobs are processed. The objective is to minimize the total weighted tardiness of the jobs. Considering the NP-hardness of the studied problem, an algorithm based on ant colony optimization is presented. A new local optimization strategy called LOC is proposed to improve the local exploitation ability of the algorithm and further search the neighborhood solution to improve the quality of the solution. Moreover, two interval candidate lists are proposed to reduce the search for the feasible solution space and improve the search speed. Furthermore, three objective-oriented heuristics are developed to accelerate the convergence of the algorithm. To verify the performance of the proposed algorithm, extensive experiments are carried out. The experimental results demonstrate that the proposed algorithm can provide better solutions than the state-of-the-art algorithms within a reasonable time.

关键词： Parallel batch machines Non-identical machine capacities Production and distribution ant colony optimization algorithm Total weighted tardiness

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Routing optimization of Two-Echelon Two-Way Logistics in Rural Areas Based on E-Commerce Environment

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TRANSPORTATION RESEARCH RECORD 2024年第2期2679卷 564-574页

作者： Hong, Wei Du, Chen Xu, Shuling Pu, Xujin Jiangnan Univ Sch Business Wuxi Peoples R China Jiangnan Univ Food Safety Res Base Jiangsu Prov Wuxi Peoples R China Jiangnan Univ Inst Food Safety Risk Management Wuxi Peoples R China

This is a study of the two-echelon two-way logistics vehicle routing problem in rural areas while accounting for government subsidies to alleviate the high distribution costs associated with "long transportation chain + low consumption density" in rural logistics. To address the issue, an improved adaptive ant colony optimization (IACO) algorithm was developed and simulations were run to validate it. The experimental findings demonstrate that, in comparison with the conventional adaptive ant colony optimization algorithm method, the IACO algorithm offers superior optimization capabilities. Finally, a case study was conducted in a rural area of China to demonstrate that the two-echelon two-way logistics network outperforms the current single-echelon logistics network by shortening the total driving distance and reducing distribution costs. This research can inform the optimization of rural logistics networks and provide a reference for the rural e-commerce logistics industry.

关键词： rural e-commerce ant colony optimization algorithm two-way logistics network two-echelon vehicle routing problem

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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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Research on shared parking allocation considering the heterogeneity of parking slot providers' temporary parking demand

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2024年第10期16卷 1305-1317页

作者： Li, Qiaoru Cheng, Juanjuan Chen, Liang Hebei Univ Technol Sch Civil & Transportat Tianjin 300401 Peoples R China

Studies on the allocation of parking demand during the sharing period primarily focus on public parking users, ignoring parking slot providers' temporary parking demand with heterogeneity. Therefore, this paper takes the allocation of parking slot providers' temporary parking demand as the research object and establishes a differentiated parking allocation (DPA) model to maximize the platform's net profit. The model is solved using the ant colony optimization (ACO) algorithm and compared with the First-Come-First-Served (FCFS) algorithm. Then, the platform adopts differentiated or undifferentiated charge measures when charging for the parking slot providers' temporary parking demand. The numerical analysis is performed to select three indicators for evaluation: 1) the utilization rate, 2) the net profit, and 3) the degree of time fragmentation. Results show that the ACO algorithm has an excellent optimization effect in allocating, and the differentiated allocation-undifferentiated charges for the parking slot providers' temporary parking demand is feasible.

关键词： Shared parking temporary parking demand heterogeneity differ-entiated parking allocation model time fragmentation ant colony optimization algorithm

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ant colony algorithm for the shortest loop design problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2006年第4期50卷 358-366页

作者： Eshghi, Kourosh Kazemi, Morteza Sharif Univ Technol Dept Ind Engn Tehran Iran

In this paper, a new algorithm for solving the shortest loop design problem is presented. The shortest loop design problem is to find the shortest loop for an automated guided vehicle covering at least one edge of each department of a block layout. In this paper, first it is shown that this problem can be represented as a graph model. The properties of the presented model enable us to design a meta-heuristic based on ant colony system algorithm for solving the shortest loop design problem. Computational results show the. efficiency of our algorithm in compare to the other techniques. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： shortest loop design problem ant colony optimization algorithm meta-heuristic algorithms

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A novel collaborative optimization algorithm in solving complex optimization problems

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SOFT COMPUTING 2017年第15期21卷 4387-4398页

作者： Deng, Wu Zhao, Huimin Zou, Li Li, Guangyu Yang, Xinhua Wu, Daqing Dalian Jiaotong Univ Software Inst Dalian 116028 Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Southwest Jiaotong Univ Tract Power State Key Lab Chengdu 610031 Peoples R China Guangxi Univ Nationalities Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Univ South China Dept Comp Sci & Technol Hengyang 421001 Peoples R China Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

关键词： Genetic algorithm ant colony optimization algorithm Chaotic optimization method Multi-strategy Collaborative optimization Complex optimization problem

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