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A joint indoor positioning scheme exploiting pedestrian dead reckoning and radio frequency tomography

JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2018年第18期32卷 2386-2403页

作者： Zhou, Biao Jing, Changqiang Sun, Chao Kim, Youngok Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China Linyi Univ Sch Informat Linyi Peoples R China Kwangwoon Univ Dept Elect Engn Seoul South Korea

The pedestrian dead reckoning (PDR) is a popular technique for recognizing a pedestrian's trajectory. However, its inherent disadvantage of susceptibility to error accumulation limits its performance as the travelled distance is increased. Various techniques are combined with PDR to mitigate the error accumulation by regular position resetting. The radio frequency (RF) Tomography is an emerging technology for localization by sensing the received signal strength (RSS) fluctuation on a line-of-sight wireless link (LOSL). Conventional RF Tomography based schemes need specific large-scale networks and face with the problem of harsh environments and high hardware costs. Thus in this paper, we proposed the flexible and practical GF operators for PDR resetting work, in which we introduce the displacement estimation by PDR technique and the LOSL triggering sequence to build a PDR geometrical formulation (PDR-GF) model, and then this model is optimized by particle swarm optimization (PSO) algorithm for crossing point (CP) estimation on the LOSLs. Finally, the estimated CP is in turn used for regular position resetting. Practical experiments are performed to evaluate the proposed positioning scheme. According to the results, the proposed scheme can achieve a finer accuracy of less than 1 m, outperforming the existing RSS-PDR positioning scheme.

关键词： Pedestrian dead reckoning radio frequency tomography geometrical formulation position resetting particle swarm optimization algorithm

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A fuzzy logic-based method for solving the scheduling problem in the cloud environments using a non-dominated sorted algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第17期31卷 e5185-e5185页

作者： Wakil, Karzan Badfar, Arshad Dehghani, Pooyan Sadati, Seyed Mojtaba Shoja Navimipour, Nima Jafari Sulaimani Polytech Univ Res Ctr Sulaimani Iraq Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Management South Tehran Branch Tehran Iran Shahid Beheshti Univ Elect Engn Dept Tehran Iran Islamic Azad Univ Islamshahr Branch Young Researchers & Elite Club Islamshahr *** Iran

Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta-heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non-dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta-heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution. Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms.

关键词： cloud computing multi-objective optimization particle swarm optimization algorithm task scheduling

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optimization of a heliostat field layout using hybrid PSO-GA algorithm

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APPLIED THERMAL ENGINEERING 2018年 128卷 33-41页

作者： Li, Chao Zhai, Rongrong Liu, Hongtao Yang, Yongping Wu, Hao North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102206 Peoples R China

A mathematical model of the heliostat field and the PSO-GA algorithm are developed in Matlab to optimize a heliostat field and determine the highest potential daily energy collection (DEC). The heliostat field in Lhasa, China is analyzed as an example. The energy collected per unit cost (ECUC) is then calculated to evaluate the economic performance of the heliostat field. Furthermore, the effects of several important factors on the heliostat field are also explored. Results indicate that, after optimization, the DEC during the spring equinox, summer solstice, autumnal equinox, and winter solstice increase by approximately 1.1 x 10(5), 1.8 x 10(5), 1.2 x 10(5), and 0.9 x 10(5) MJ, respectively. Studies on the key parameters show that as the number of heliostats in the first row of the field (Nhel(1)) increases, the ECUC first increases to its maximum value and then decreases. Additionally, ECUC increases with tower height but decreases as the cost of the heliostat mirror collector increases. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Solar tower power system Heliostat field optimization Genetic algorithm particle swarm optimization algorithm

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A swarm Intelligence algorithm for Routing Recovery Strategy in Wireless Sensor Networks With Mobile Sink

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IEEE ACCESS 2018年 6卷 67434-67445页

作者： Yue, Yinggao Cao, Li Hang, Bo Luo, Zhongqiang Hubei Univ Arts & Sci Comp Sch Xiangyang 441053 Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Yibin 644000 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China

Considering the fault tolerance mechanism in the route optimization of the mobile wireless sensor network (MWSN), we analyze the routing fault tolerance between nodes and establish an intelligent fault-tolerant routing model for MWSN. We also propose a novel fault-tolerant routing algorithm for an MWSN based on an artificial bee colony (ABC) optimized particle swarm optimization algorithm (ABC-PSO), and this optimizes the ABC-PSO algorithm is applied to study the optimal construction strategy of an alternate route. The proposed using of the path coding, the ABC algorithm optimization, the collaborative updation, and the evolution of the principal and subordinate swarms, as well as particle selection, provide faster overall convergence performance and more accurate solutions for the network optimization. Analytical proofs and simulation experiments show that the route fault-tolerant strategy proposed in this paper can create a reliable transmission environment and an efficient route recovery mechanism for the MWSN. Moreover, it can lower the energy consumption of the network and increase the network's lifetime and improve the robustness and the reliability of MWSN.

关键词： Mobile wireless sensor networks fault tolerant swarm intelligence algorithm particle swarm optimization algorithm reliability

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optimization and performance analysis of solar hybrid CCHP systems under different operation strategies

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APPLIED THERMAL ENGINEERING 2018年 133卷 327-340页

作者： Yang, Gan Zhai, Xiaoqiang Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Shanghai 200240 Peoples R China

The hybridization between conventional combined cooling heating and power (CCHP) systems and solar systems has been considered as a good solution to the urgent energy and environment issues. This study develops the mathematical model of a CCHP system hybridized with PV panels and solar thermal collectors. The particle swarm optimization (PSO) algorithm is adopted to find the optimum values of design parameters. Based on the energy output characteristic of the solar hybrid CCHP system, five operation strategies of the conventional CCHP system are adjusted and applied for the solar hybrid CCHP system. The simulation work of the hybrid CCHP systems based upon a hotel building in Atlanta is carried out to find an appropriate design scheme. The results show that the hybrid CCHP system under the FEL-ECR mode is the best choice. Besides, its PESR, CO2ERR and ATCSR can reach 36.15%, 53.73% and 4.16%, respectively. Compared with a conventional CCHP system, the hybrid CCHP system achieves better energy-saving and CO2 reduction performance. However, the hybrid CCHP system consumes more annual total costs because of its high initial investment.

关键词： Combined cooling heating and power Solar energy Hybrid system particle swarm optimization algorithm

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Optimized radial basis function neural network for improving approximate dynamic programming in pricing high dimensional options

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NEURAL COMPUTING & APPLICATIONS 2018年第6期30卷 1783-1794页

作者： Hajizadeh, Ehsan Mahootchi, Masoud Amirkabir Univ Technol Tehran Polytech Dept Ind Engn & Management Syst 424 Hafez Ave Tehran *** Iran

Pricing American basket option is one of the essential problems in quantitative finance. The complexity of this type of option has motivated many practitioners and researchers to develop simulation-based methods. In this paper, we develop an optimized radial basis function neural network (RBFNN), which is optimally tuned by the particle swarm optimization algorithm to enhance the efficiency and accuracy of approximate dynamic programming (ADP) for pricing the American basket option. Additionally, for the scenario generation, a simulation-based technique using a copula-GARCH method and Extreme Value Theory is performed to tackle the nonlinearity of dependencies between variables. The prices obtained through the proposed approach compared with those ones achieved from pure RBFNN and ADP in different situations. This is also illustrated that the obtained prices of American basket option can outperform the results obtained through the RBFNN and ADP approaches in terms of the predefined fitness measures.

关键词： Radial basis function neural network Approximate dynamic programming particle swarm optimization algorithm American basket options

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Wavefront sensing method for diffraction optical system based on phase diversity

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INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 102980-000页

作者： Zhi, Xiyang Gong, Jinnan Yu, Di Wang, Dawei Niu, Ruize Harbin Inst Technol Res Ctr Space Opt Engn Harbin 150001 Heilongjiang Peoples R China

In this paper, we present a novel wavefront sensing method for diffraction optical system based on phase diversity. Based on the physical-imaging mechanism of diffractive optical system, the wavefront characteristics of the diffraction optical system are characterized by using diffraction efficiency. On this basis, a novel modified phase diversity (PD) wavefront sensing method is established based on the blocking idea. After that, the global optimization method of corresponding method based on particle swarm optimization algorithm is proposed. Finally, some experiment results are achieved with the experiment on a membrane diffraction optical system. Experimental results indicate that the proposed algorithm performs well on both wavefront reconstruction and image restoration and is far superior to traditional methods in diffraction optical systems. The accuracy of our proposed PD method is at least two orders of magnitude higher than the traditional method. Taking the 0.01 degrees field of view for example, our modified PD method can achieve the accuracy of 2.5 x 10(-4) wavelength when the diffraction efficiency is higher than 0.6. This proposed method can be applied to improve the image quality of the diffraction optical system and further support the on-orbit application of ultra-large aperture membrane imaging technology.

关键词： Wavefront sensing Phase diversity Diffraction optical system particle swarm optimization algorithm

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An optimized gene expression programming model for forecasting the national CO₂ emissions in 2030 using the metaheuristic algorithms

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APPLIED ENERGY 2018年 228卷 808-820页

作者： Hong, Taehoon Jeong, Kwangbok Koo, Choongwan Yonsei Univ Dept Architecture & Architectural Engn Seoul 03722 South Korea Kyonggi Univ Dept Architectural Engn Suwon 16227 South Korea

To cope with the approaching POST-2020 scenario, the national CO2 emission in the building sector, which accounts for 25.5% of the total CO2 emissions, should be managed effectively and efficiently. To do this, it is essential to forecast the national CO2 emissions in the building sector by region. As the South Korean government does not currently do this by region, regional characteristics are rarely taken into consideration when managing the national CO2 emissions in the building sector. Towards this end, this study developed an optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms. Compared to the forecasting performance of the gene expression programming model, the forecasting performance of the optimized gene expression programming harmony search optimization model has improved by 7.11, 2.05, and 2.06% in terms of the mean absolute error, root mean square error, and mean absolute percentage error, respectively. Various national CO2 emissions scenarios in the building sector were established in order to better analyze the variation range of the national CO2 emissions in the building sector. Compared to the national CO2 emissions in 2016 (i.e., scenario 1: 41,337 ktCO(2);scenario 2: 45,373 ktCO(2);scenario 3: 46,024 ktCO(2)) in multi-family housing complexes, the national CO2 emissions in 2030 (i.e., scenario 1: 37,579 ktCO(2);scenario 2: 37,736 ktCO(2);scenario 3: 37,754 ktCO(2)) in multi-family housing complexes are forecasted to increase by 10.00-21.91%. The developed optimized gene expression programming harmony search optimization model will potentially be able to assist policymakers in central and local governments forecast the national CO2 emissions in 2030. Through this, national CO2 emission management that more closely reflects the characteristics at the regional or national level can be supported.

关键词： National CO2 emissions Gene expression programming Harmony search optimization algorithm particle swarm optimization algorithm

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Bicriterion total flowtime and maximum tardiness minimization for an order scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 117卷 152-163页

作者： Wu, Chin-Chia Liu, Shang-Chia Lin, Tzu-Yun Yang, Tzu-Hsuan Chung, I-Hong Lin, Win-Chin Feng Chia Univ Dept Stat Taichung 40724 Taiwan Fujen Catholic Univ Dept Business Adm Hsinpei City Taiwan

An order scheduling problem arises in numerous production scheduling environments. Makespan, mean flow time, and mean tardiness are the most commonly discussed and studied measurable criteria in the research community. Although the order scheduling model with a single objective has been widely studied, it is at odds with real-life scheduling practices. In practice, a typical manager must optimize multiple objectives. A search of the literature revealed that no articles had addressed the issue of optimizing an order scheduling problem with multiple objectives. Therefore, an order scheduling model to minimize the linear sum of the total flowtime and the maximum tardiness is introduced in this study. Specifically, several dominance relations and a lower bound are derived to expedite the search for the optimal solution. Three modified heuristics are proposed for finding near-optimal solutions. A hybrid iterated greedy algorithm and a particle swarm colony algorithm are proposed to solve this problem. Finally, a computational experiment is conducted to evaluate the performances of all proposed algorithms.

关键词： Order scheduling Iterated greedy algorithm particle swarm optimization algorithm Total completion time Maximum tardiness

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A multi-criterion partner selection problem for virtual manufacturing enterprises under uncertainty

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2018年 196卷 68-81页

作者： Huang, Bin Bai, Lihui Roy, Arnab Ma, Ningning Fuzhou Univ Coll Mech Engn & Automat Fuzhou 350108 Fujian Peoples R China Univ Louisville Dept Ind Engn Louisville KY 40292 USA

We study a partner selection problem for virtual manufacturing enterprises facing an uncertain environment. We propose a new method of using the grey system theory to account for uncertainties in a project's start time, completion time, transportation time, as well as cost. We first develop the concepts of delivery time agreement index and cost agreement index, and then formulate a multi-objective partner selection problem that maximizes the minimum delivery time agreement index, the average delivery time agreement index and the cost agreement index. A chaotic particle swarm optimization (CPSO) algorithm is developed. Extensive computational results show that the proposed CPSO method outperforms the standard PSO in providing quality solutions more reliably.

关键词： Partner selection Virtual enterprise particle swarm optimization algorithm Grey environment

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