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第33届中国过程控制会议

作者： Ning Guo Qiu-Yi Shen Bin Qian Jing Na Rong Hu Academy of Information Engineering and Automation Kunming University of Science and Technology Academy of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology

In this paper,a multi-dimensional ant colony optimization(MDACO) algorithm is proposed to solve the multi-objective multi-compartment vehicle routing problem(MOMCVRP) with time windows(MOMCVRPTW).Minimizing the total travel distance and maximizing the customer satisfaction are the objectives that the MOMCVRPTW needs to *** the global search phase of MDACO,first,a multi-dimensional pheromone concentration matrix(MDPCM) is presented to preserve the information of excellent individuals,and second,two heuristic rules based on problem characteristics are developed to initialize the *** are helpful to enhance the ability of the algorithm to find high-quality regions in the solution *** the local search phase of MDACO,several types of neighborhood operations combined with the speed-up evaluation method and the variable neighborhood descent strategy are designed to deeply search the found high-quality ***,the simulation experiments and algorithm comparisons verify the effectiveness of the proposed MDACO.

关键词： ant colony optimization algorithm multi-objective optimization multi-compartment vehicle routing problem with time windows multi-dimensional pheromone concentration matrix speed-up evaluation variable neighborhood descent

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Resource utilization enhancemnet through live virtual machine migration in cloud using ant colony optimization algorithm

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2020年第1期23卷 79-85页

作者： Sutar, Sandeep G. Mali, Pallavi J. More, Amruta Y. Annasaheb Dange Coll Engn & Technol Dept Comp Sci & Engn Ashta India OOPRA IT Solut PVT Ltd Pune Maharashtra India

Cloud computing offers unlimited computational resources which are ready to use from anywhere, anytime on request. The achievement of maximized utilization of computational resources (physical and virtual) and minimized energy consumption of resources are goals of proposed system. The proposed system provides dynamic and energy efficient live VM (virtual machine) migration approach. This system reduces wastage of power by initiating sleep mode of idle physical machines results into energy saving. We propose a system consist with seven modules. (1) Resource monitor analyses energy consumption of resources. (2) Capacity distributor distributes maximum and minimum capacity for the physical machines. (3) Task allocator determines overloaded servers. (4) Optimizer analyses load on physical machine using ant colony optimization algorithm (5) Local Migration Agent calculates load of VMs to be migrated and select appropriate physical server. (6) Migration Orchestrator migrates the VM cosidering load. (7) Energy Manager initiates sleep mode for idle physical machine(PM)

关键词： Cloud computing Energy management Virtualization Live virtual machine migration ant colony optimization algorithm

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Research on Path Planning of AGV Based on Improved ant colony optimization algorithm

Research on Path Planning of AGV Based on Improved Ant Colon...

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第33届中国控制与决策会议

作者： Jiuxiang Sun Ya'nan Yu Ling Xin College of Automation and Electronic Engineering Qingdao University of Science and Technology

Path planning is a key problem in the motion control of mobile *** order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency,this paper puts forward a matrix yard storage mode,which is transformed into grid map model,and then uses ant colony optimization algorithm to plan the path of *** at the shortcomings of traditional ant colony optimization algorithm(ACO) in global path planning,such as slow convergence speed and weak optimization ability,an improved ant colony path planning algorithm is ***,the grid map is established,and the fruit fly optimization algorithm(FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm,and then the ant colony optimization algorithm is used for global path *** the same time,in order to solve the problem of many path turning angles and large cumulative turning angles in the planning,the path smoothing is carried *** simulation results show that the improved algorithm has fewer turns and smoother path,and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.

关键词： ant colony optimization algorithm Fruit fly optimization algorithm matrix yard storage mode Smoothing

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A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows

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INFORMATION SCIENCES 2019年 490卷 166-190页

作者： Zhang, Huizhen Zhang, Qinwan Ma, Liang Zhang, Ziying Liu, Yun Univ Shanghai Sci & Technol Sch Management Mail Box 459516 Jungong Rd Shanghai 200093 Peoples R China Shanghai Univ Engn Sci Sch Mat Engn Shanghai Peoples R China

In this paper, we present a multi-objective vehicle routing problem with flexible time windows (MOVRPFIexTW). In this problem, a fleet of vehicles can service a set of customers earlier and later than the required time with a given tolerance. This flexibility enables a logistics company to save distribution costs at the expense of customer satisfaction. This paper uses a direct interpretation of the vehicle routing problem with flexible time windows (VRPFIexTW) as a multi-objective problem, where the total distribution costs (including travel costs and fixed vehicle costs) are minimized and the overall customer satisfaction is maximized. We propose a solution strategy based on ant colony optimization and three mutation operators, which incorporates the concept of Pareto optimality for multi-objective optimization. The performance of the proposed approach was evaluated using the well-known benchmark Solomon's problems. Our experimental results show that the suggested approach is effective, because it provides solutions that are comparative to the best known results in the literatures. Finally, we not only highlight the advantages of MOVRPFIexTW when compared with the single objective VRPFIexTW, but also illustrate its applicability and validation by analyzing a real case. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Vehicle routing problem Flexible time windows ant colony optimization algorithm Multi-objective optimization Mutation operator

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Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

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WATER ENVIRONMENT RESEARCH 2019年第4期91卷 300-321页

作者： Moeini, Ramtin Afshar, Mohammad Hadi Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Iran Univ Sci & Technol Envirohydroinformat Ctr Excellence Tehran Iran

The ant colony optimization algorithm (ACOA) is hybridized with nonlinear programming (NLP) for the optimal design of sewer networks. The resulting problem is a highly constrained mixed integer nonlinear problem (MINLP) presenting a challenge even to the modern heuristic search methods. In the proposed hybrid method, The ACOA is used to determine pipe diameters while the NLP is used to determine the pipe slopes of the network by proposing two different formulations. In the first formulation, named ACOA-NLP1 a penalty method is used to satisfy the problem constraints while in the second one, named ACOA-NLP2, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily satisfied as box constraint of the NLP solver leading to a considerable reduction of the search space size. In addition, the assumption of minimum cover depth at the network inlets is used to calculate the nodal cover depths and the pump and drop heights at the network nodes, if required, leading to a complete solution. The total cost of the constructed solution is used as the objective function of the ACOA, guiding the ant toward minimum cost solutions. Proposed hybrid methods are used to solve three test examples, and the results are presented and compared with those produced by a conventional application of ACOA. The results indicate the effectiveness and efficiency of the proposed formulations and in particular the ACOA-NLP2 to optimally solve the sewer network design optimization problems. (C) 2018 Water Environment Federation

关键词： ant colony optimization algorithm explicit constraints nonlinear programming optimal design sewer network

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Determination of optimum cross-section of earth dams using ant colony optimization algorithm

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SCIENTIA IRANICA 2019年第3期26卷 1104-1121页

作者： Rezaeeian, A. Davoodi, M. Jafari, M. K. Islamic Azad Univ Sci & Res Branch Dept Civil Engn Tehran Iran IIEES POB 19537-14476 Tehran Iran

Earth dams are one of the most important and expensive civil engineering structures to which a considerable amount of budget is allocated. Their construction costs mainly correspond to the size of embankments, which in turn depends on their cross sectional area. Therefore, reductions in cross-sectional areas of earth dams may cause a decrease in embankment volumes, leading to a significant reduction in the construction costs of these structures. On the other hand, it is almost impossible to obtain optimum cross section in earth dams with desired stability and acceptable operational dimensions using traditional design methods. In this paper, ant colony optimization algorithm (ACO), a well-known and powerful metaheuristic method for tackling problems in geotechnical engineering, was used to solve this complicated problem. The results showed that applying the ideal and optimum slope and berm arrangements resulting from ACO in designing embankments and earth dams with different heights could lead to a decrease in embankment volumes, compared to those without any berms or those with berms resulting from usual designs with trial and error. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Earth dams Slope stability Artificial intelligence ant colony optimization algorithm Control variable

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Improved ant colony optimization algorithm and its application to solve pipe routing design

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ASSEMBLY AUTOMATION 2019年第1期39卷 45-57页

作者： Wu, Lei Tian, Xue Wang, Hongyan Liu, Qi Xiao, Wensheng China Univ Petr Coll Mech & Elect Engn Qingdao Shandong Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Maritime Inst Singapore Singapore Marine Design & Res Inst China Shanghai Peoples R China Qingdao Univ Sci & Technol Coll Elect Engn Qingdao Shandong Peoples R China

Purpose As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible production platform is an important equipment for oil exploitation and production. PRD is one of the most key parts of the design of semi-submersible platform. This study aims to present an improved ant colony algorithm (IACO) to address PRD for the oil and gas treatment system when designing a semi-submersible production platform. Design/methodology/approach First, to simplify PRD problem, a novel mathematical model is built according to real constraints and rules. Then, IACO, which combines modified heuristic function, mutation mechanism and dynamical parameter mechanism, is introduced. Findings Based on a set of specific instances, experiments are carried out, and the experimental results show that the performance of IACO is better than that of two variants of ACO, especially in terms of the convergence speed and swarm diversity. Finally, IACO is used to solve PRD for the oil and gas treatment system of semi-submersible production platform. The simulation results, which include nine pipe paths, demonstrate the practicality and high-efficiency of IACO. Originality/value The main contribution of this study is the development of method for solving PRD of a semi-submersible production platform based on the novel mathematical model and the proposed IACO.

关键词： ant colony optimization algorithm Pipe layout space Pipe path Pipe routing design Semi-submersible production platform

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A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm

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APPLIED SOFT COMPUTING 2020年 96卷 106621-106621页

作者： Khelifa, Boudjemaa Laouar, Mohamed Ridda Larbi Tebessi Univ Tebessa Lab Math Informat & Syst LAMIS Tebessa Algeria

Hitherto, urbanization reached unprecedented spreading, various problems in the field increase from day to day, and makes the urban phenomena more dynamic and more complex. Therefore, it is important to call in experts and provide all stuff to establish urban projects' plans, which often need to be achieved in a brief time. Actually, decision-makers need more and more updated plans and even sustainable solutions to convey eventual urban changes with maintaining intrinsic features of urban areas, such as coverage, inter-dependency, and coherency. Due to decision-makers yearnings and the short time allocated to planners, urban project planning remains an exhausting task;it resorts to arbitrary choices to find a good match of projects according to the intended situations. On the other hand, it should take care of the available resources like funds, land, water, energy, underground, and raw materials, which ought to be rationally exploited, and preserved for future generations. In this paper, the proposed intelligent decision support system (IDSS) aims to find out the best urban plans that fit urban projects to appropriate areas. It also employs the holonic approach to model complex and large-scale urban systems, where agents of each level apply a new multi-objective ant colony optimization algorithm called BKPACS for the urban project planning problem, which is viewed as a bounded knapsack problem (BKP). To produce global optimal urban plans, the main algorithm called H-MACO coordinates between the different levels of this holonic system. The experimental results on a set of urban projects about a province of Algeria show good quality plans produced in less time. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Intelligent decision support system Holonic multi-agent system Urban project planning ant colony optimization algorithm Bounded knapsack problem

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Adaptive Fuzzy Control Applied to Seven-Link Biped Robot Using ant colony optimization algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2019年第4期43卷 797-811页

作者： Aldair, Ammar A. Rashid, Abdulmuttalib T. Rashid, Mofeed T. Alsaedee, Eman Badee Univ Basrah Elect Engn Dept Basrah Iraq Univ Dhi Qar Dept Elect & Elect Engn Nasiriyah Iraq

Referring to the fact that the nth links bipedal walking robot has high nonlinearity and uncertainty parameters;therefore robust controllers for walking robot should be properly designed. This paper proposes a new robust control scheme based on fuzzy system and ant colony optimization algorithm. Adaptive fuzzy controllers (AFCs) based on ant colony optimization (ACO) algorithm are proposed to eliminate the chattering phenomenon that occurs when the walking robot moves on rough surfaces. Six rotational joints are used to connect the seven links of the bipedal walking robot. Those joints are assumed frictionless and actuated by six independent servomotors. Therefore, six adaptive fuzzy controllers are designed in this work (one for each joint). To design robust fuzzy controllers, the ant colony optimization algorithm is utilized to tune and find the best parameters of the output membership function of the fuzzy controller. For comparison purposes, optimal PID controllers (OPIDCs) are designed with optimal parameters that are chosen by utilizing the ant colony algorithm. The performances of the two proposed controllers (AFCs and OPIDCs) are tested under significant disturbances situations such as carrying different weighted things by the walking biped robot. In addition, the stability of the adaptive fuzzy controller is studied and proved by applying Lyapunov theory.

关键词： Seven-link biped robot Adaptive fuzzy control Optimal PID control ant colony optimization algorithm Nonlinear system

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A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2022年第5期19卷

作者： Chen, Tao Chen, Suifan Zhang, Kuoran Qiu, Guoting Li, Qipeng Chen, Xinmin Zhejiang Univ Sci & Technol Coll Mech & Energy Engn 318 Liuhe Rd Hangzhou 310023 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn 1219 Zhongguan West Rd Ningbo 315201 Peoples R China

To improve the finding path accuracy of the ant colony algorithm and reduce the number of turns, a jump point search improved ant colony optimization hybrid algorithm has been proposed in this article. Firstly, the initial pheromone concentration distribution gets from the jump points has been introduced to guide the algorithm in finding the way, thus accelerating the early iteration speed. The turning cost factor in the heuristic function has been designed to improve the smoothness of the path. Finally, the adaptive reward and punishment factor, and the Max-Min ant system have been introduced to improve the iterative speed and global search ability of the algorithm. Simulation through three maps of different scales is carried out. Furthermore, the results prove that the jump point search improved ant colony optimization hybrid algorithm performs effectively in finding path accuracy and reducing the number of turns.

关键词： Mobile robot path planning jump point search algorithm ant colony optimization algorithm turn cost factor

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