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

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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Integrated fault location method for distribution networks based on IACO-PS

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JOURNAL OF POWER ELECTRONICS 2023年第1期23卷 112-126页

作者： Zhang, Shuqing Zhang, Xiaowen Jiang, Anqi Zhang, Liguo Li, Mingliang Yanshan Univ Inst Elect Engn Qinhuangdao Hebei Peoples R China

This paper develops a new hybrid method based on an improved ant colony optimization algorithm that incorporates pattern search (IACO-PS) for determining the location of faults in a distribution network. The performance of the conventional ant colony optimization (ACO) algorithm is improved using the opposite-based learning strategy to generate the initial population and adding a weight coefficient into the pheromone update mechanism to dynamically adjust the pheromone volatilization factor. The hybrid IACO-PS algorithm combines the individual strengths of ACO and PS. In addition, the fitness function is constructed by counting the false and missing fault information into the fault variable. In optimizing benchmark function experiments, the proposed hybrid IACO-PS presents a superior performance when compared to other improved versions of ACO. The effectiveness of the proposed approach is corroborated by tests performed on an IEEE 134-bus network. Simulation results show that the proposed hybrid IACO-PS method can determine the location of a fault even in the presence of fault distortion. In addition, it is immune to noise and data loss errors. Finally, the method proposed in this paper significantly outperforms other published fault location methods, and it can accurately locate faults and identify the type of distortion.

关键词： Fault location Distribution network ant colony optimization algorithm Pattern search

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A hybrid multi-path routing algorithm for industrial wireless mesh networks

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2013年第1期2013卷 1-12页

作者： Zuo, Yun Ling, Zhihao Yuan, Yifeng E China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Multi-path routing, a routing technique that enables data transmission over multiple paths, is an effective strategy in achieving reliability in wireless sensor networks. However, multi-path routing does not guarantee deterministic transmission. This is because more than one path is available for transferring data from the source node to the destination node. A hybrid multi-path routing algorithm is proposed for industrial wireless mesh networks for improving reliability and determinacy of data transmission, as well as to effectively deal with link failures. The proposed algorithm adopts the enhanced Dijkstra's algorithm for searching the shortest route from the gateway to each end node for first route setup. A virtual pheromone distinct from the regular pheromone is introduced to realize pheromone diffusion and updating. In this way, multiple routes are searched based on the ant colony optimization algorithm. The routes used for data transmission are selected based on their regular pheromone values, facilitating the delivery of data through better routes. Link failures are then handled using route maintenance mechanism. Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of average end-to-end delay, packet delivery ratio, and routing overhead;moreover, it has a strong capacity to cope with topological changes, thereby making it more suitable for industrial wireless mesh networks.

关键词： Multi-path routing Industrial wireless mesh networks Enhanced Dijkstra's algorithm ant colony optimization algorithm

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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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Meta-Heuristic algorithms for the Generalized Extensible Bin Packing Problem With Overload Cost

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IEEE ACCESS 2022年 10卷 124858-124873页

作者： Ding, Ran Deng, Bin Li, Weidong Yunnan Univ Operat Res & Cybernet Kunming Peoples R China Yunnan Univ Sch Math & Stat Kunming 650504 Peoples R China

In this paper, we consider a generalized extensible bin packing problem with overload cost, first proposed by Denton et al. in 2010, in which the total size of items packed into a bin is allowed to exceed its capacity, and the cost incurred each bin is equal to the fixed cost plus the overload cost, the objective is to minimize the total cost of all bins. According to the characteristics of the problem, we first propose an improved ant colony optimization algorithm (IACO), which enhances the positive feedback effect of ACO by improving the update method of pheromone and the adaptive adjustment parameters. We also introduce a variable neighborhood search method in ACO to improve the convergence of the algorithm and get rid of the phenomenon of local extrema. Then, we present a discrete particle swarm optimization algorithm (DPSO) to solve the problem. In order to ensure the uniform distribution and high quality of the initial particle swarm, we use some heuristic methods in the initialization process of the swarm, so that the initial particle can cover the entire search space with a large probability, which effectively improves the performance of DPSO algorithm. Finally, we compare and analyze the performance of these proposed algorithms through two sets of computational experimental frameworks. Compared with some algorithms in the literature, computational results signify that the improved ACO algorithm and MDPSO algorithm are more competitive than some other metaheuristic algorithms.

关键词： ant colony optimization Costs Uncertainty Heuristic algorithms Search methods Metaheuristics Servers Extensible bin packing regular working time scheduling ant colony optimization algorithm discrete particle swarm optimization

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A max-min ant colony system for assembly sequence planning

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第9-12期67卷 2819-2835页

作者： Yu, Jiapeng Wang, Chengen Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Northeastern Univ Liaoning Prov Key Lab Multidisciplinary Optimal D Shenyang 110819 Liaoning Peoples R China

An improved ant colony optimization (ACO)-based assembly sequence planning (ASP) method for complex products that combines the advantages of ant colony system (ACS) and max-min ant system (MMAS) and integrates some optimization measures is proposed. The optimization criteria, assembly information models, and components number in case study that reported in the literatures of ACO-based ASP during the past 10 years are reviewed and compared. To reduce tedious manual input of parameters and identify the best sequence easily, the optimization criteria such as directionality, parallelism, continuity, stability, and auxiliary stroke are automatically quantified and integrated into the multi-objective heuristic and fitness functions. On the precondition of geometric feasibility based on interference matrix, several strategies of ACS and MMAS are combined in a max-min ant colony system (MMACS) to improve the convergence speed and sequence quality. Several optimization measures are integrated into the system, among which the performance appraisal method transfers the computing resource from the worst ant to the better one, and the group method makes up the deficiency of solely depending on heuristic searching for all parallel parts in each group. An assembly planning system "AutoAssem" is developed based on Siemens NX, and the effectiveness of each optimization measure is testified through case study. Compared with the methods of priority rules screening, genetic algorithm, and particle swarm optimization, MMACS is verified to have superiority in efficiency and sequence performance.

关键词： Assembly sequence planning ant colony optimization algorithm Max-min ant system Extended interference matrix

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A meta-heuristic for minimizing total weighted flow time on parallel batch machines

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 298-308页

作者： Jia, Zhao-hong Zhang, Han Long, Wen-tao Leung, Joseph Y-T. Li, Kai Li, Wei Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230039 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

To address the problem of minimizing the total weighted completion time on parallel batch processing machines with identical machine capacities, non-identical job sizes and unequal weights, an effective meta-heuristic based on ant colony optimization is proposed. After presenting a mathematic model of the problem, we provide an algorithm to calculate the lower bound. Then, a meta-heuristic is proposed to solve the problem. The heuristic information is defined with consideration of job weights and job sizes. Meanwhile, a candidate set for constructing the solution is used to narrow the search space. Additionally, to improve the solution quality, a local optimization strategy is incorporated. Simulation results show that the proposed algorithm is able to obtain a high-quality solution within a reasonable time, and outperforms the compared algorithms.

关键词： Parallel scheduling Patch processing machines Non-identical job sizes Total weighted completion time ant colony optimization algorithm

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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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Simultaneous estimation of thermal conductivity and specific heat of thermal protective fabrics using experimental data of high heat flux exposure

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APPLIED THERMAL ENGINEERING 2016年第0期107卷 785-796页

作者： Udayraj Talukdar, Prabal Das, Apurba Alagirusamy, Ramasamy Indian Inst Technol Delhi Dept Mech Engn New Delhi 110016 India Indian Inst Technol Delhi Dept Text Technol New Delhi 110016 India

In the present study, ant colony optimization algorithm is used for an inverse heat transfer problem of parameter estimation. Temperature dependent thermal conductivity and specific heat are estimated simultaneously. Performance of the present algorithm is first analyzed by using the temperature data obtained from the solution of direct problem. Effect of measurement error on the property estimation is then analyzed. Accuracy of estimated properties is found to be good in both the cases. Experiments are then conducted on a cotton duck fabric exposed to 40 kW/m(2) radiant heat flux using a bench top test according to ISO 6942. Temporal variation of sensor temperature is used to estimate the thermal conductivity and specific heat of the fabric sample. The estimated values are in agreement with the results available in the literature. Property values estimated in the study are included in a coupled conduction-radiation heat transfer model for fabrics. Performance of the fabric sample is further analyzed with the numerical model for high heat fluxes. The simulation results compare quite well with experimental data at 80 kW/m(2) radiant heat and 70 kW/m(2) flame exposures. Flame exposure experiment is conducted according to ISO 9151. Good agreement between the numerical and experimental results are found for both the exposures. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Coupled conduction-radiation Parameter estimation ant colony optimization algorithm High heat exposure Thermal protective fabric Thermal conductivity Specific heat

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Research on Two-Level Equalization Strategy of Lithium-Ion Battery Based on Graph Theory

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JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE 2024年第2期21卷 021013页

作者： Wu, Tiezhou Li, Houjia Li, Hongguang Zhao, Rui Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Hubei Peoples R China

To solve the problem of inconsistency in the use of series-connected lithium-ion battery packs, this paper proposed a topological structure of dual-layer equalization based on a flying capacitor circuit and Cuk circuit, as well as a control strategy seeking the shortest equalization path. In this structure, batteries are divided into two forms: intra-group and inter-group;the intra-group equalization is the lower-level equalization while the flying capacitor circuit is used as an equalization circuit to achieve equalization between individual battery cells;and the inter-group equalization is the upper-level equalization while Cuk circuit is used as equalization circuit to achieve equalization between battery packs;each battery pack shares a battery cell, thus to obtain more options on equalization path. The proposed strategy, with state of charge as the balancing variable, represents the topological structure of the circuit in the form of graph by adopting graph theory control, seeks the optimal equalization path via ant colony optimization algorithm with global search, thus to improve the equalization speed and efficiency. At last, the structure and the strategy proposed in this paper were simulated in matlab/simulink to compare with the maximum value equalization method in the condition of static, charging, and discharging. The result of the simulation experiments shows that the equalization method based on graph theory control reduces the equalization duration by approximately 17%, and improves the equalization efficiency by approximately 2%, which verifies the superiority and effectiveness of the structure and strategy proposed in this paper.

关键词： graph theory lithium-ion battery dual-level equalization Cuk circuit ant colony optimization algorithm batteries

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