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Multiobjective ant colony system algorithm for Component-Level Construction Schedule Optimization

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2025年第3期151卷

作者： Shen, Zhaozheng Wu, Jie Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China

Automatic generation of construction schedules has emerged as a key solution to address the inefficiencies and instabilities arising from the over-reliance on empirical judgment. However, traditional construction scheduling has been predominantly limited to regional levels, inadequately addressing the lean construction requirements of component-based prefabricated steel frame (PSF) structures. To bridge this gap, this study formulates an optimization model for the component-level resource-constrained project scheduling problem for PSF structures (C-RCPSP-PSF), which realizes the automatic extraction of precedence relationships from building information modeling three-dimensional (BIM 3D) models and the minimization of construction duration, costs, and carbon emissions. To address the C-RCPSP-PSF model, a novel multiobjective ant colony system (MOACS) algorithm is developed that utilizes three distinct colonies to individually tackle the objectives and combines taboo lists and global archives to enhance the search. Experimental results show the superior convergence and diversity of the MOACS over that of other competitive multiobjective optimization algorithms in solving the proposed model.

关键词： Component-level Resource-constrained project scheduling problem ant colony system algorithm Multiobjective optimization Carbon emission

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A Multi-Objective ant colony system algorithm for Virtual Machine Placement in Traffic Intense Data Centers

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IEEE ACCESS 2018年 6卷 58912-58923页

作者： Qin, Yao Wang, Hua Zhu, Fangjin Zhai, Linbo Shandong Univ Sch Comp Sci & Technol Jinan 250101 Shandong Peoples R China Shandong Univ Sch Software Jinan 250101 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China

Virtual machine (VM) placement can meet different kinds of performance targets in data centers. As a result, it has become one of the most critical operations in data centers. In this paper, we investigate the VM placement problem for cloud applications, which have intense bandwidth requirements. In this kind of applications, all VMs communicate with a single designated point. The work of predecessors focuses on the revenue of communications in the network, and tries to find a good solution composed of the best fitted VMs. However, in their work, where to place the selected VMs is not important and has no effect on their objective, and this may cause high power consumption. We formulate the problem as a bin packing problem, which is strictly NP-hard. Then, we propose a multi-objective ant colony system (ACS) algorithm which is called ACS-BVMP. The goal is to obtain Pareto optimal solution set, which can simultaneously maximize the revenue of communications and minimize the power consumption of PMs. The proposed algorithm is tested with some instances from the literature. Its solution set is compared with two existing multi-objective algorithms and three single-objective algorithms. The results show that our proposed algorithm outperforms the above algorithms.

关键词： Virtual machine placement ant colony system algorithm Pareto optimization traffic intense data center

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The Research of path-oriented test data generation based on a mixed ant colony system algorithm and genetic algorithm

The Research of path-oriented test data generation based on ...

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8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM)

作者： Yi, Minjie Jiangsu Automat Res Inst Lianyungang Peoples R China

ISBN: (纸本)9781612846835

It is very practical significance to seek an effective path-oriented test data automatic generation method. The genetic algorithm, ant colony algorithm is commonly used to generate test data, and the both can improve the efficiency of test data generation. But, for both algorithms, there was a little limitation to target path in path testing for being prone to local optimal solution. Some researchers have combined the genetic algorithm and ant colony algorithm to generate the test data path, in which the result was better. At the same time, they found hybrid ant colony algorithm was still subject to the limitation of global search ability of ant colony algorithm. The ant colony system algorithm is improved based on the ant colony algorithm. It is proved that it is more suitable for global search. In the present study, we propose to combine the ant colony system algorithm and genetic algorithm (ACSGA) to generate path-oriented software testing data. Classical triangle discrimination problem in path-oriented software testing is chose as a simulation experiment to verify ACSGA. The results show that the generation efficiency of target path has been improved apparently.

关键词： path-oriented software test ant colony system algorithm genetic algorithm

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Using ant colony system algorithm to solve dynamic transmission network expansion planning

Using ant colony system algorithm to solve dynamic transmiss...

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6th International Power Engineering Conference (IPEC 2003)

作者： Zhai, HB Cheng, HZ Wang, X Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9810487908

Dynamic transmission network expansion planning problem is a complex combinatorial optimization problem. Although this problem has been extensively studied, it is still not well solved. In recent years, ant algorithm, a new metaheuristic method, is emerging. It is highly efficient for finding high quality solutions to combinatorial optimization problems. In this paper, ant algorithm is briefly introduced and firstly used to solve the dynamic transmission network expansion planning problem. The corresponding mathematical model, together with its solution algorithm, is also presented. The simulation (1)results of a sample system, although preliminary, have verified its feasibility and efficiency. The directions of future research in improving ant algorithm to solve dynamic transmission network expansion planning problems are also proposed.

关键词： transmission network dynamic expansion planning ant algorithm ant colony system algorithm

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An ant colony system-based hybrid algorithm for square root concave cost transhipment problems

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ENGINEERING OPTIMIZATION 2010年第11期42卷 983-1001页

作者： Yan, S. Shih, Y. L. Wang, C. L. Natl Cent Univ Dept Civil Engn Chungli 32001 Taiwan

Concave cost transhipment problems are difficult to optimally solve for large-scale problems within a limited period of time. Recently, some modern meta-heuristics have been employed for the development of advanced local search based or population-based stochastic search algorithms that can improve the conventional heuristics. Besides these meta-heuristics, the ant colony system algorithm is a population-based stochastic search algorithm which has been used to obtain good results in many applications. This study employs the ant colony system algorithm, coupled with some genetic algorithm and threshold accepting algorithm techniques, to develop a population based stochastic search algorithm for efficiently solving square root concave cost transhipment problems. The developed algorithms are evaluated with a number of problem instances. The results indicate that the proposed algorithm is more effective for solving square root concave cost transhipment problems than other recently designed local search based algorithms and genetic algorithm.

关键词： ant colony system algorithm transhipment problem genetic algorithm population-based stochastic search

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ant colony system with characterization-based heuristics for a bottled-products distribution logistics system

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2014年第partb期259卷 965-977页

作者： Gomez S, Claudia G. Cruz-Reyes, Laura Gonzalez B, Juan J. Fraire H, Hector J. Pazos R, Rodolfo A. Martinez P, Juan J. Inst Tecnol Ciudad Madero Cd Madero Tamaulipas Mexico

The aim of this paper is to show the solution of the Vehicle Routing Problem with Time Windows (VRPTW) as a key factor to solve a logistics system for the distribution of bottled products. We made a hybridization between an ant colony system algorithm (ACS) and a set of heuristics focused on instance characterization and performance learning. We mainly propose a method to make a constrained list of candidate customers called Extended Constrained List (ECL) heuristics. Such a list is built based on the characterization of the time-window and the geographical distribution of customers. This list gives priority to the nearest customers with a smaller time window. The ECL heuristics is complemented by the Learning Levels (LL) heuristics, that allows the ants to use the pheromone matrix in two phases: local and global. In order to validate the benefits of each heuristics, a series of computational experiments were conducted using the standard Solomon's benchmark. The experimental results show that, when the ECL heuristics is incorporated in the basic ACS algorithm, the number of required vehicles is reduced by 28.16%. When the LL heuristics is incorporated, this reduction increases to 36.83%. The experimentation reveals that, by a suitable characterization, preexisting conditions in the instances are identified in order to take advantage of both of the ECL and LL. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Heuristics hybridization Vehicle routing problem ant colony system algorithm

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system optimal route choice strategy based on ant colony system

System optimal route choice strategy based on ant colony sys...

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IEEE International Conference on systems, Man, and Cybernetics (SMC)

作者： An, Yisheng Yang, Linjian Mu, Chen Zhao, Xiangmo Changan Univ Sch Informat Engn Xian 710064 Peoples R China

ISBN: (纸本)9781509018970

The research presents in this paper develops an ant colony system (ACS) based system optimal route choice strategy to ensure the rational traffic flow assignment for urban traffic network. In this work, the traffic flow and impedance function of each road section are calculated firstly, and then the individual traveler's route choice behaviors on network nodes are simulated based on applying the pseudo-random state transition rule, route and road section pheromone update formula, which implement the synthesizing of static prior knowledge, dynamic traffic state and the randomness of route choice. This paper's findings reveal that the designed strategy is in a position to reflecting the overlay and delay effect of route choice under different Origin-Destination (OD) demands. In addition, the findings can obtain better network equilibrium comparing with the incremental assignment method, and will benefit for achieving the route guidance system with time varying traffic conditions.

关键词： intelligent transportation route choice strategy ant colony system algorithm traffic network transition probability

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UAV Dispatch Planning for a Wireless Rechargeable Sensor Network for Bridge Monitoring

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2023年第2期8卷 293-309页

作者： Zhao, Chuanxin Wang, Yang Zhang, Xin Chen, Siguang Wu, Changzhi Teo, Kok Lay Anhui Normal Univ Sch Comp & Informat Wuhu 241000 Peoples R China Anhui Normal Univ Anhui Prov Key Lab Intelligent Bldg & Bldg Energy Hefei 230061 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Broadband Wireless Commun & Intern Nanjing 210049 Peoples R China Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China Sunway Univ Petaling Jaya 47500 Selangor Malaysia

Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.

关键词： Wireless rechargeable sensor network unmanned aerial vehicles trajectory planning ant colony system algorithm sensor energy assignment bridge monitoring

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A hybrid ACS-VTM algorithm for the vehicle routing problem with simultaneous delivery & pickup and real-time traffic condition

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷

作者： Liu, Wenjie Zhou, Yutong Liu, Wei Qiu, Jing Xie, Naiming Chang, Xiangyun Chen, Jian Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Harbin Inst Technol Sch Mechatron Engn Dept Ind Engn Harbin 150006 Peoples R China East China Univ Sci & Technol Dept Management Sci & Engn Shanghai 200237 Peoples R China

The vehicle routing problem with simultaneous delivery & pickup and real-time traffic information (VRPSDPTI) is a dynamic problem of combinatorial network optimisation in logistics and supply chain management. It is also a typical NP-hard problem that has plagued enterprises with reverse logistics operations for many years. The main objective of this research is to determine an optimal vehicle routing plan for the VRPSDPTI problem. To achieve this goal, a mixed integer programming (MIP) model is constructed, with the objective of minimising the total travel cycle for the VRPSDPTI problem. A hybrid algorithm of the ant colony system and virtual transformation method (ACS-VTM) is then designed to explore the vehicle routing plan for the VRPSDPTI problem;it adopts a flexible split of the time period for re-optimisation and an improved partial service policy for customer demand. Based on it, a practical case of the VRPSDPTI problem is used to verify the proposed model and algorithm. One major finding is proposed through the study of the practical case that adopting the hybrid ACSVTM algorithm can effectively reduce the total travel cycle for the VRPSDPTI problem. The main contributions of this study are that it can provide an efficient decision tool to solve the VRPSDPTI, and can not only reduce the total travel cycle, but also improve the efficiency of vehicle utilisation.

关键词： Vehicle routing problem with simultaneous delivery & pickup Real-time traffic conditions Hybrid algorithm ant colony system algorithm Virtual transformation method Vehicle routing plan

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Facilitating an ant colony algorithm for multi-objective data-intensive service provision

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JOURNAL OF COMPUTER AND system SCIENCES 2015年第4期81卷 734-746页

作者： Wang, Lijuan Shen, Jun Luo, Junzhou Univ Wollongong Sch Informat Syst & Technol Wollongong NSW 2522 Australia Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

The explosion of enormous sources of digital data has led to greater dependence on dataintensive services. Applications based on data-intensive services have become one of the most challenging applications in cloud computing. The service provision, and in particular service composition, will face new challenges as the services and data grow. In this paper, we will evaluate an ant colony system to resolve the multi-objective data-intensive service composition problem. The algorithm for a multi-objective context will get a set of Paretooptimal solutions considering two objectives at the same time: the total cost and the total execution time of a composite service. (C) 2014 Elsevier Inc. All rights reserved.

关键词： ant colony system algorithm Genetic algorithm Multi-objective optimization Data-intensive service composition Cloud computing Quality of service

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