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A tri-objective ant colony optimization based model for planning safe construction site layout

AUTOMATION IN CONSTRUCTION 2018年 89卷 1-12页

作者： Ning, Xin Qi, Jingyan Wu, Chunlin Wang, Wenjuan Dongbei Univ Finance & Econ Sch Investment & Construct Management Dalian Liaoning Peoples R China Beihang Univ Sch Econ & Management Beijing Peoples R China Beihang Univ Beijing Key Lab Emergency Support Simulat Technol Beijing Peoples R China Dongbei Univ Finance & Econ Sch Managanent Sci & Engn Dalian Liaoning Peoples R China

Realizing safety improvements in construction site layout planning (CSLP) is vitally important to construction project safety management. Unlike previous studies in which the safety objective is built without detailed risk factors analysis, this study transforms CSLP into a multi-objective optimization (MOO) problem with designing two safety objective functions due to facility safety relationships (potential risks arising from interaction flows) and geographic safety relationship (potential risks arising from hazardous sources) from the holistic interpretation of interaction relationship connecting temporary facilities. Besides, a supplementary cost reduction objective function was also derived as cost is a critical barrier against safety improvement. Subsequently, a tri-objective ant colony optimization based model was developed to solve MOO problem. Finally, a case study is used to verify the proposed model. The study enriches safety implications by considering onsite safety issues from interaction relationship and enhances site safety of CSLP in the pre-construction stage.

关键词： Construction site layout planning ant colony optimization algorithm optimization model Interaction relationship Safety management

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Parameters identification and trajectory control for a hydraulic system

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ISA TRANSACTIONS 2019年 92卷 228-240页

作者： Feng, Hao Yin, Chenbo Ma, Wei Yu, Hongfu Cao, Donghui Nanjing Tech Univ United Inst Excavator Key Technol Nanjing 211816 Jiangsu Peoples R China Nanjing Tech Univ Inst Automobile & Construct Machinery Nanjing 211816 Jiangsu Peoples R China SANY Grp Co Ltd Suzhou 215300 Peoples R China

In order to improve the tracking accuracy of a hydraulic system, an improved ant colony optimization algorithm (IACO) is proposed to optimize the values of proportional-integral-derivative (PID) controller. In addition, this paper presents an experimental study on the parameters identification to deduce accurate numerical values of the hydraulic system, which also determines the relationship between control signal and output displacement. Firstly, the basic principle of the hydraulic system and the mathematical model of the electro-hydraulic proportional control system are analyzed. Based on the theoretical models, the transfer function of the control system is obtained by recursive least square identification method (RLS). Then, the key parameters of the control system model are obtained. Some improvements are proposed to avoid premature convergence and slow convergence rate of ACO: the transition probability is revised based adjacent search mechanism, dynamic pheromone evaporation coefficient adjustment strategy is adopted, pheromone update rule and parameters optimization range are also improved. Then the proposed IACO tuning based PID controller and the identification parameters are modeled and simulated using MATLAB/Simulink and AMESim co-simulation platform. Comparisons of IACO, standard ACO and Ziegler-Nichols (Z-N) PID controllers are carried out with different references as step signal and sinusoidal wave using the co-simulation platform. The simulation results of the bucket system using the proposed controller demonstrates improved settling time, rise time and the convergence speed with a new objective function J. Finally, experiments with leveling operations are performed on a 23 ton robotic excavator. The experimental results show that the proposed controller improves the trajectory accuracy of the leveling operation by 28% in comparison to the standard ACO-PID controller. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Parameters identification Trajectory control ant colony optimization algorithm PID Recursive least square method Robotic excavator

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A method for indoor Wi-Fi location based on improved back propagation neural network

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第4期27卷 2511-2525页

作者： Chen, Jinghui Dong, Chen He, Guorong Zhang, Xiaoyu Fuzhou Univ Coll Math & Comp Sci Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou Fujian Peoples R China Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou Fujian Peoples R China Fujian Prov Key Lab Informat Secur Network Syst Fuzhou Fujian Peoples R China

In order to achieve high precision on indoor location, a Wi-Fi indoor location method based on improved back propagation (BP) neural network is proposed. The classical BP neural network is optimized in real time by the ant colony optimization algorithm. Meanwhile, the momentum term is introduced to construct an improved four-layer BP neural network model. The model uses the Wi-Fi signal feature as the input of the BP neural network and succeeds in the area classification under multiple Wi-Fi signal features. The experimental results demonstrate that the improved BP neural network can increase the classification accuracy of the classifier effectively, and achieve a high-precision indoor area location. Furthermore, it performs better practical results while ensuring the time complexity. The advantages of this method are high practicability, low cost, high prediction classification accuracy, and robust stability, which can achieve the efficient classification of the short-range area.

关键词： Indoor Wi-Fi location back propagation neural network ant colony optimization algorithm momentum term

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Integrated production and transportation on parallel batch machines to minimize total weighted delivery time

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COMPUTERS & OPERATIONS RESEARCH 2019年 102卷 39-51页

作者： Jia, Zhao-hong Zhuo, Xue-xue Leung, Joseph Y-T Li, Kai Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230039 Anhui Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China

This paper considers a production-distribution scheduling problem on parallel batch processing machines (BPMs) with multiple vehicles. In the production stage, the jobs with non-identical sizes and equal processing time are grouped into batches, which are processed on BPMs. In the distribution stage, there are vehicles with identical capacity arriving regularly to transport the batches to the customers. The objective is to minimize the total weighted delivery time of the jobs. A method of computing a lower bound is given to evaluate the proposed algorithms. To tackle this NP-hard problem, a deterministic heuristic (algorithm H) and two hybrid meta-heuristic algorithms based on ant colony optimization (HACO, MMAS) are proposed, respectively. Through analyzing the property of the investigated problem, the heuristic information and the pheromone trails are defined. Incorporated with a local optimization strategy, the ant colony constructs the schedule first. Then, a heuristic is designed to transport the batches that have been processed. The performance of the proposed algorithms are compared with each other through testing on randomly generated problem instances. It is shown that the proposed MMAS algorithm slightly beats the HACO algorithm, which can find the better solutions than the H algorithm in a reasonable amount of time. (C) 2018 Published by Elsevier Ltd.

关键词： Batch processing machines Non-identical job sizes Integrated production and transportation Local optimization ant colony optimization algorithm

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A Load Balancing algorithm for 5G Vehicular Cloud Computing Systems 2020

A Load Balancing Algorithm for 5G Vehicular Cloud Computing ...

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24th Pan-Hellenic Conference on Informatics

作者： Eirini Zoumi Emmanouil Skondras David Veliu Angelos Michalas Dimitrios D. Vergados University of Piraeus Greece University of Western Macedonia Greece

ISBN: (纸本)9781450388979

5G Vehicular Cloud Computing (5G-VCC) infrastructures are evolving rapidly. In a 5G-VCC system, Cloud resources should be efficiently distributed to provide satisfactory Quality of Service (QoS) for modern services with increased requirements. Furthermore, the workload should be fairly distributed to the available Virtual Machines (VMs). This paper proposes an algorithm for performing load balancing in Cloud infrastructures that exist in 5G-VCC systems. The algorithm is called Modified ant colony optimization (MACO), as its functionality is influenced by the natural behaviour of ants. Specifically, the MACO algorithm assigns a pheromone (weight) value to each VM. Subsequently, for each service request the VM with the highest pheromone is selected in a way similar to the one that ants apply to select optimal routes. The selection of each VM results in the decrement its pheromone value, considering the workload of the assigned service. Evaluation results show that the proposed algorithm outperforms existing load balancing algorithms in terms of the processing time required for serving the user requests.

关键词： 5G-VCC systems cloud services ant colony optimization algorithm load balancing

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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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An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 127卷 240-252页

作者： Rubaiee, Saeed Yildirim, Mehmet Bayram Univ Jeddah Dept Ind Engn POB 80327 Jeddah 21589 Saudi Arabia Wichita State Univ Dept Ind & Mfg Engn 1845 Fairmount Wichita KS 67260 USA

Energy-aware scheduling in manufacturing operations with time-of-use electricity tariffs is a challenging problem. Sustainable manufacturing is gaining significant momentum: companies are not only improving their product quality, but also optimizing the production processes to improve energy consumption (i.e., minimizing energy cost) in order to manage environmental challenges which contribute to global climate change. The purpose of this paper is to study a preemptive scheduling problem on a single-machine to minimize the total completion time and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To solve these objectives, we develop several new holistic ant colony optimization algorithms. The proposed model is solved via several methods including weighted sum method (WSM) using CPLEX, and multiobjective ant colony optimization based on a dominance ranking (ACO-DR) or based on a dominance ranking procedure and crowding distance comparison (ACO-DRC) or based on a heuristic approach to obtain an approximate Pareto-front and also provide information on when to start and resume each job for any solution on the Pareto-front. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we demonstrate how the results of the multiobjective model could be utilized in decision making using the multiobjective optimization on the basis of ratio analysis (MOORA) method. This proposed model and heuristics allows decision makers to operate in challenging-data enabled environments in industrial interne of things ecosystem, and assists in optimizing production planning to improve energy cost.

关键词： Single-machine preemptive scheduling Energy-aware scheduling Time-of-use electricity tariffs Multiobjective optimization Internet of things (IoT) ant colony optimization algorithm Green and sustainable manufacturing

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STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICLE SWARM ant colony optimization algorithm

STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICL...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Zhengqiu Yang Meiling Liu Jiapeng Xiu Chen Liu School Of Software Engineering Beijing University of Posts and Telecommunications School Of Computer Science Beijing University of Posts and Telecommunications

Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particle swarm to solve cloud computing environment resources allocation problem.

关键词： Particle swarm optimization algorithm ant colony optimization algorithm Cloud resource allocation

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A Novel Strategy of Initializing the Population Size for ant colony optimization algorithms in TSP 13

A Novel Strategy of Initializing the Population Size for Ant...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Fanzhen Zhong, Jiaqi Liu, Chen Gao, Chao Li, Xianghua Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Pangang Grp Xichang Steel & Vanadium Co Ltd Panzhihua Sichuan Peoples R China Potsdam Inst Climate Impact Res PIK D-14473 Potsdam Germany

ISBN: (纸本)9781538621653

The ant colony optimization (ACO) algorithm belonging to swarm intelligence methods has been used to solve quantities of optimization problems. Among those problem, the travelling salesman problem (TSP) is a very essential application of ACO algorithm, which displays the great ability of ACO algorithm to find short paths through graphs. However, the existing ant colony optimization algorithms still perform a low efficiency in solving TSP within a limited time. In order to overcome these shortcomings, a hypothesis about initializing the population size for ACO algorithms is put forward, based on the analysis of the relationship among the initial number of ant, the average optimal solution and the computational cost. Furthermore, some experiments are implemented in six datasets, and the results prove that the hypothesis is reasonable and reveal that the initial population size is relevant to the number of cities in a dataset. Based on the hypothesis, this paper proposes a novel strategy of initializing the number of ants for ACO algorithms in TSP, so that the relative high-quality optimal solutions can be obtained within a short time.

关键词： ant colony optimization algorithm TSP Initial population size

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An adaptive population control framework for ACO-based community detection

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CHAOS SOLITONS & FRACTALS 2020年 138卷 109886-109886页

作者： Wang, Chunyu Zhang, Fan Deng, Yue Gao, Chao Li, Xianghua Wang, Zhen Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Ctr OPTIMAL Xian 710072 Peoples R China

The community structure is one of the most important features of complex networks and has wide research and application prospects. To find the community structure, many researchers currently focus on natural heuristic methods, where an extraordinary swarm intelligence algorithm (i.e., the ant colony algorithm) is widely adopted to detect the potential community structures. However, the computational cost of such an algorithm is so high that it restricts the property and range of application. In this paper, we present a novel adaptive population control framework for ACO-based community discovery approaches to overcome the mentioned shortcomings. Specifically, this framework dynamically controls the number of ants based on the slope of the modularity and iterations. Such a framework is adopted in two different algorithms and we make corresponding comparison between this one and traditional ACO-based algorithms in six classical real networks and five synthetic datasets. Experiments show that ant colony algorithms with our proposed framework have evidently reduced time complexity and maintained the quality of community structure simultaneously. (c) 2020 Published by Elsevier Ltd.

关键词： Community detections ant colony optimization algorithm Adaptive population control framework

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