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Two-Phase Hybrid Search algorithm for Time-Dependent Cold Chain Logistics Route Considering Carbon Emission and Traffic Congestion

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IEEE ACCESS 2024年 12卷 95128-95151页

作者： Yang, Lu Gao, Yuelin Sun, Ying Li, Jia North Minzu Univ Coll Comp Sci & Engn Yinchuan 750021 Peoples R China North Minzu Univ Ningxia Collaborat Innovat Ctr Sci Comp & Intellig Yinchuan 750021 Peoples R China

This paper studies the time-dependent cold chain logistics vehicle routing problem considering both traffic congestion and carbon emissions. A cold chain logistics model with time-dependent green vehicle paths with time windows (TDGVRPTW) was developed to fulfil the demands of green logistics and to take comprehensive account of consideration should be given to factors such as road congestion and carbon emissions. The objective of the model is to minimise total costs, which include carbon emission costs, penalty costs, fuel consumption costs, fixed costs, damage costs and refrigeration costs. Two-phase hybrid search algorithm was developed to solve this model. During the initial stage of the algorithm, a dual-population ant colony optimization (DACO) algorithm sharing the optimal individual is employed to optimize the distribution route of the vehicle. During the second phase, an adaptive golden section search (AGSS) algorithm is used to optimise the departure time of the vehicle from the distribution centre to avoid traffic congestion time periods. To validate the effectiveness of the suggested two-phase hybrid search algorithm, it is applied to the improved Solomon benchmark test set. The experimental findings demonstrate that the two-phase hybrid search algorithm can reasonably plan the driving routes and departure times for each vehicle, effectively avoiding peak traffic congestion periods in the city, and reducing the overall delivery cost.

关键词： Logistics Costs Carbon dioxide Heuristic algorithms Vehicle routing Green products Roads Traffic congestion Traffic control ant colony optimization Carbon emissions Time-dependent green vehicle routing problem with time windows traffic congestion cold chain logistics ant colony optimization algorithm carbon emission

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Meta-Heuristic algorithms for Learning Path Recommender at MOOC

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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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Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第4期18卷 903-921页

作者： Zhou, Fengjiao Ma, Rui Samsurijan, Mohamad Shaharudin bin Xie, Xiaoqin Univ Sains Malaysia Sch Social Sci George Town 11800 Gelugor Malaysia Chengdu Univ Tradit Chinese Med Hlth & Rehabil Coll Chengdu 611137 Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Publ Adm Chengdu Sichuan Peoples R China

Industry 5.0 puts forward higher requirements for smart cities, including low -carbon, sustainable, and people -oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

关键词： Smart City Cyber-Physical-Social Systems Smart Patrol Platform Drone Trajectory Planning ant colony optimization algorithm

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A new approach for service migration in cloud-based e-commerce using an optimization algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第14期33卷 e4457-e4457页

作者： Huang, Lixia Abnoosian, Karlo Hunan Appl Technol Univ Coll Informat Engn Changde Hunan Peoples R China Islamic Azad Univ Dept Stat Sci & Res Branch Tehran Iran

Service-oriented architecture (SOA) has a crucial role in backing productive cloud services. Also, the vast spread of the theoretical notion of diverse businesses (like e-commerce) into the actual use has been recently applied by cloud computing. The service functionality could be affected by overfilling of the network traffic because of the broadly dispersed nature of e-commerce in clouds-a key challenge for immediate jobs. Throughout the last decade, a vast range of applications or large-scale operators has increasingly attracted to migrate the services in clouds. An effective method for accessing the applications throughout standard business hours is continually moving virtual machine containers from one data center to another. Now, with the commonness of cloud computing, many applications have been moved to the cloud fully/partly. It can be handled through the migration of cloud services to diverse platforms in a way that minimizes the communication cost of e-commerce. As this issue has an NP-hard nature, in the present article, we present an automatic smart service migration outline through the ant colony optimization (ACO) algorithm on cloud-oriented e-commerce. In the presented model, we use the ACO algorithm to take the finest (near-optimal) service migration decisions. Based on the obtained results, the proposed technique has the optimal number of migrations compared to the existing models.

关键词： ant colony optimization algorithm cloud computing e-commerce service migration

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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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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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The family traveling salesman problem with incompatibility constraints

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NETWORKS 2022年第1期79卷 47-82页

作者： Bernardino, Raquel Paias, Ana Univ Lisbon DEIO Fac Ciencias C6Piso 4 P-1749016 Lisbon Portugal Univ Lisbon Ctr Matemat Aplicacoes Fundamentais & Invest Oper Fac Ciencias Lisbon Portugal

In this article, we propose a new variant of the family traveling salesman problem (FTSP). The FTSP is an NP-hard problem that generalizes the traveling salesman problem. In the FTSP, the set of cities is partitioned into several families and one wants to find the minimum cost route that visits a given number of cities in each family. A new variant arises by introducing incompatibilities between families, that is, cities from incompatible families cannot be visited in the same route, and it is called the FTSP with incompatibility constraints (FTSP-IC). We propose compact and non-compact mixed integer linear programming models for the FTSP-IC where the incompatibility constraints are modeled by defining paths in a compatibility graph for each family. Additionally, we present a set of valid inequalities motivated by the incompatible families. The non-compact models and the valid inequalities were tested using a branch-and-cut framework. To evaluate the models we generated incompatibility matrices for benchmark instances of the FTSP, which are available in the website . With the branch-and-cut algorithm, we were able to obtain the optimal value of instances with up to 127 nodes. We also developed an ant colony optimization (ACO) algorithm and an iterated local search algorithm (ILS) to address the largest sized instances. Both metaheuristics are able to provide solutions for instances that the branch-and-cut algorithm cannot address and to improve the majority of the upper bounds obtained by the branch-and-cut algorithm in less computational time.

关键词： ant colony optimization algorithm branch‐ and‐ cut algorithm family traveling salesman problem incompatibility constraints iterated local search algorithm network flows valid inequalities

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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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An optimization model for energy-efficient machining for sustainable production

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JOURNAL OF CLEANER PRODUCTION 2019年 232卷 1121-1133页

作者： Wang, Honghui Zhong, Ray Y. Liu, Guijie Mu, WeiLei Tian, Xiaojie Leng, Dingxin Ocean Univ China Dept Mech & Elect Engn Qingdao 266100 Shandong Peoples R China Ocean Univ China Key Lab Ocean Engn Shangdong Prov Qingdao 266100 Shandong Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China

Sustainable production plays an important role in product lifecycle management by considering the social sustainability. Energy-efficient machining is an efficient approach for sustainable production in current manufacturing sectors. Although many related efforts have been achieved, a comprehensive energy optimization approach oriented to manufacturing parts is still a challenge. Therefore, this paper selects Standard for the Exchange of Product model data-Numerical Control (STEP-NC) as the enabling technology to achieve energy-efficient machining. An optimization model is proposed based on the energy calculation method using the workingstep in STEP-NC. An improved ant colony optimization (ACO) solution, consisting of encoding and decoding, initialization, machining scheme generation, idea of local multiple iteration, evaluation, pheromone evaporation and update, is presented. A part with typical manufacturing features is applied to verify the effectiveness of the proposed approach. The generated solution can provide a comprehensive machining scheme for low energy demand! by improving the efficiency with 25% for solving the optimization problem. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： STEP-NC ant colony optimization algorithm Energy-efficient machining

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Definition of a New Metric With Mutual Exclusivity and Coverage for Identifying Cancer Driver Modules

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IEEE ACCESS 2020年 8卷 133767-133776页

作者： Zhang, Wei Liu, Yue Lei, Wang Lei Wang Changsha Univ Hunan Prov Key Lab Ind Internet Technol & Secur Changsha 410022 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Identification of cancer driver modules or pathways is a key step in understanding cancer pathogenesis and exploring patient-specific treatments. Numerous studies have shown that some genes with low mutation frequency are also important for the cancer progression, while previous research have focused on identifying high-frequency mutation genes. In this study, we propose a new framework with a new metric to identify driver modules with low-frequency mutation genes, called iCDModule. Inspired by the gravity model, we integrate the coverage and mutual exclusivity in mutation information, define a new metric between gene pairs, called mutation impact distance, to help identifying potential driver genes sets, including those have extremely low mutation rates but play an important role in functional networks. A genetic network is constructed by combining the defined mutation impact distance and then the driver module identification problem is formalized as the maximum clique solution problem, and an improved ant colony optimization algorithm is used to solve it. iCDModule is applied to TCGA breast cancer, glioblastoma, ovarian cancer to test performance. Experiments show that it can accurately identify known cancer driver modules and pathways, and also detect driver modules containing low-frequency mutation genes. iCDModule is significantly better than other existing methods in identifying driver modules.

关键词： Cancer Mathematical model Measurement Gravity ant colony optimization Web servers Genetics Driver modules mutual exclusivity mutation impact distance low-frequency mutation ant colony optimization algorithm

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