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An Efficient Modeling and Optimization Approach for Pressure Testing During Reentry Operation of Deepwater Drilling Riser System

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IEEE ACCESS 2020年 8卷 74759-74770页

作者： Liu, Kang Chen, Guoming Shao, Weiming China Univ Petr East China Ctr Offshore Engn & Safety Technol Qingdao 266580 Peoples R China China Univ Petr East China Coll New Energy Qingdao 266580 Peoples R China

Pressure testing should be conducted on auxiliary lines during riser reentry to ensure the seal security of connection positions. The proper setting of the number and positions of pressure testing is essential because it can significantly reduce the high costs of offshore drilling. However, the analysis and optimization models for pressure testing during riser reentry were not resolved satisfactorily in engineering practice. This study proposes an efficient optimization procedure for pressure testing during riser reentry. A time-efficiency evaluation model and a risk assessment model are established based on the pressure testing process and entropy theory. The modified artificial bee colony (ABC) algorithm is used to optimize the pressure testing process by incorporating the above proposed models. The optimization procedure is illustrated by a case study. The optimal results from the proposed procedure are compared with those from the recommended practices. The resulting variabilities due to seal reliability, water depth, and pressure testing time are also discussed. The study shows that the optimization procedure can improve the operation efficiency of the riser reentry process by 20 & x0025;and 38 & x0025;compared to two recommended practices. The proposed procedure is verified to be an efficient alternative for decision making in pressure testing to improve the economic efficiency and ensure safety during riser reentry.

关键词： Testing Optimization Seals Reliability Entropy Electron tubes Pressure testing riser time-efficiency evaluation risk assessment artificial bee colony algorithm optimization

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Multi-objective configuration optimization method for a diesel-based hybrid energy system

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ENERGY REPORTS 2020年 6卷 2146-2152页

作者： Wang Rongjie Jimei Univ Marine Engn Inst Xiamen 361021 Peoples R China Fujian Prov Key Lab Naval Architecture & Ocean En Xiamen 361021 Peoples R China

In this paper, a configuration optimization method for a diesel hybrid energy system has been proposed. The method is based on a fuzzy artificial bee colony optimization mechanism that considers the power supply characteristics of an isolated hybrid energy system. First, a mathematical model based on a diesel hybrid energy system and a configuration optimization objective function that considers cost and load shortages is constructed. Then, an objective function is optimized in combination with fuzzy rules and artificial swarming algorithms to obtain a system configuration that meets multiple objective requirements. Finally, the system configurations of three hybrid modes of photovoltaic (PV)/energy storage/diesel, wind power/energy storage/diesel, and PV/wind power/energy storage/diesel are compared, and the pollutant emissions of the different systems are analyzed. Experimental results show that the new method exhibits good configuration optimization and performance, and it is a practical approach for solving the problem of microgrid system configuration. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Fuzzy artificial bee colony algorithm Diesel engine Hybrid energy system Load power shortage

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A comparative study by separating and combining swarm-based algorithms into modules

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Procedia Computer Science 2022年 207卷 956-965页

作者： Ryo Takano College of Information Science and Engineering Ritsumeikan University Shiga Japan

The focus of this paper is to determine how the mechanisms of each algorithm contribute to search performance. swarm-based algorithms are composed of several mechanisms. This paper regards these independent mechanisms as modules. The modules constituting each algorithm are classified into three categories: (1) the common module: a collection of mechanisms that all swarm-based algorithms have in common; (2) the main module: the unique mechanisms of each algorithm that updates the solution by interacting with other solutions; and (3) the sub module: the unique mechanisms of each algorithm that do not directly improve the quality of the solution. Testing the algorithms combined into these modules reveals the contribution of each module to performance. The experiments are conducted for the three algorithms PSO, ABC, and CS to test the usefulness of this concept. The results of these experiments revealed the following implications: (a) the main module of ABC alone can have good performance; (b) the main module of CS does not perform well as a stand-alone; (c) the combination algorithm with the main module of PSO and the sub module of ABC can improve applicability.

关键词： A comparative study Particle Swarm Optimization artificial bee colony algorithm Cuckoo Search

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Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human-robot collaboration

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APPLIED SOFT COMPUTING 2020年 93卷 106394-106394页

作者： Cil, Zeynel Abidin Li, Zixiang Mete, Suleyman Ozceylan, Eren Izmir Democracy Univ Dept Ind Engn Izmir Turkey Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Gaziantep Univ Dept Ind Engn Gaziantep Turkey

The collaboration of human workers and robots draws increasing attention from the manufacturing enterprises to embrace the Industry 4.0 paradigm in a competitive way. Motivated by the requirements of collaboration between human workers and robots in assembly lines, this study investigates the mixed-model assembly line balancing (MMALB) problem with the collaboration between human workers and robots. A mixed-integer linear programming (MILP) model is formulated to tackle the small-size problems optimally to minimize the sum of cycle times of models. Also, bee algorithm (BA) and artificial bee colony (ABC) algorithm are implemented and improved to solve the large-size problems due to the NP-hardness of this problem. The proposed BA algorithm utilizes a new employed bee phase to accelerate the evolution of the swarm and new scout phase to escape from being trapped into local optima and produce a high-quality and diverse population. The developed ABC proposes a new onlooker phase to accelerate the evolution of the whole swarm by removing the poor-quality solutions, new scout phase to achieve high-quality solutions while preserving the diversity of the swarm, and local search to enhance exploitation capacity. Computational study on a set of generated instances indicates that the improvements enhance the BA and ABC algorithm by a significant margin, and the proposed BA and ABC algorithm achieve competing performance in comparison with nine other algorithms, including the late acceptance hill-climbing algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, discrete cuckoo search algorithm, the original bee algorithm, and three artificial bee colony algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mixed-model assembly line Human-robot collaboration bee algorithm artificial bee colony algorithm Mixed-integer linear programming

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artificial bee colony-based Routing for Mobile Agents on the Internet of Things

Artificial Bee Colony-based Routing for Mobile Agents on the...

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IEEE Canada Electric Power Conference, EPEC

作者： Shamim Yousefi Farnaz Derakhshan Hadis Karimipour University of Tabriz Tabriz EA Iran School of Engineering University of Guelph Guelph ON Canada

ISBN: (数字)9781728164892

ISBN: (纸本)9781728164908

In the critical infrastructure of the Internet of Things (IoT), the most efficient approach for data collection is to employ the software Mobile Agents (MAs). In MA-based data collection, the energy exhaustion of wireless communications is much more than the data processing costs. Hence, a crucial challenge in IoT is to offer optimal routing for MAs to save the energy of devices. In this paper, we present a new routing method for MAs on IoT using the artificial bee colony (ABC) algorithm. In our approach, the Euclidean distance between sensor/devices, residual energy, and data priority are considered to measure the appropriateness of the generated routing. Simulation analysis demonstrates that our proposed method improves the energy exhaustion, lifetime, transmission delay, and reliability of IoT.

关键词： Routing Internet of Things Data collection Mobile agents Euclidean distance artificial bee colony algorithm Performance evaluation

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Sensor Localization in Wireless Sensor Networks Using Cultural algorithm

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2020年第4期11卷 106-122页

作者： Kulkarni, Vaishali Raghavendra Desai, Veena MS Ramaiah Univ Appl Sci Bangalore Karnataka India KLS Gogte Inst Technol Belgaum Karnataka India

Evolutionary computing-based cultural algorithm (CA) has been developed for anchor-assisted, range-based, multi-stage localization of sensor nodes of wireless sensor networks (WSNs). The results of CA-based localization have been compared with those of swarm intelligence-based algorithms, namely the artificial bee colony algorithm and the particle swarm optimization algorithm. The algorithms have been compared in terms of mean localization error and computing time. The simulation results show that the CA performs the localization in a more accurate manner and at a higher speed than the other two algorithms.

关键词： artificial bee colony algorithm Cultural algorithm Localization Particle Swarm Optimization Wireless Sensor Networks

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Dynamic modelling and optimal design of a clutch actuator for heavy-duty automatic transmission considering flow force

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MECHANISM AND MACHINE THEORY 2020年 145卷

作者： Ouyang, Tiancheng Huang, Guicong Li, Shuoyu Chen, Jingxian Chen, Nan Guangxi Univ Coll Mech Engn Nanning Peoples R China Southeast Univ Sch Mech Engn Nanjing Peoples R China

With the rapid development of heavy-duty vehicles, increasing attention has been paid to the performance of high-power automatic transmission. An electro-hydraulic actuator, as a key component in automatic transmission, is crucial to the shifting quality. In this study, we develop a dynamic model of an electro-hydraulic actuator for heavy-duty vehicles, and adopt the artificial bee colony (ABC) algorithm to optimize the structural parameters to improve the ride comfort and fuel economy. First, a detailed model considering the flow force is developed to describe the motion inside the actuator based on the theory of fluid dynamics. Then the key design parameters, which affect the shifting performance significantly, are identified by combining the Bode plots and sensitivity analysis. Finally, the electro-hydraulic actuator is optimized to ensure the actual curve approximates the ideal trajectory while shortening the clutch pressure response time maximally by using the ABC algorithm. In comparison with the original parameters, the optimized design shows a major improvement in the rapid response and precise control of gear shifting. This investigation provides an efficient approach to design the structure parameters of clutch actuators to increase their shifting performance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Heavy-duty automatic transmission Clutch actuator Dynamic modelling artificial bee colony algorithm Optimal design

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Research on artificial bee colony algorithm with social cognition search strategy

Research on artificial bee colony algorithm with social cogn...

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24th Chinese Control and Decision Conference (CCDC)

作者： Wu Bin Qian Cun-hua Cui Zhi-yong Nanjing Univ Technol Dept Ind Engn Nanjing 210009 Jiangsu Peoples R China

ISBN: (纸本)9781457720727

artificial bee colony (ABC) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions, the modified algorithms based on social cognition thinking are proposed. The new candidate solutions are generated through mimicking the search mechanism of particle swarm optimization. To compare and analyze the performance of our proposed modified algorithms, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the proposed algorithms can effectively enhance the searching efficiency and greatly improve the searching quality.

关键词： artificial bee colony algorithm Social cognitive strategies Global optimization

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ABC-DE-WOA: A New Hybrid algorithm for Optimization Problems

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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International Conference on Circuits, Communication, Control and Computing

作者： Pradeep Kumar D Sowmya BJ Anita Kanavalli Annvin Vincent Dept. of Computer Science and Engineering M S Ramaiah Institute of Technology (Affliaited to VTU) Bangalore Dept. of Artificial Intelligence and Data Science M S Ramaiah Institute of Technology (Affliaited to VTU) Bangalore

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the artificial bee colony (ABC) strategy. Despite the fact that there are many artificial bee colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard artificial bee colony (ABC) algorithm has been combined with the Whale Optimization algorithm (WOA) and Differential Evolution algorithm (DE) to generate a novel Hybrid artificial bee colony algorithm (ABC), artificial bee colony-Differential Evolution-Whale Optimization algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with artificial bee colony (ABC) variants like artificial bee colony-Whale Optimization algorithm (ABC-WOA), artificial bee colony-Differential Evolution algorithm (ABC-DE), and original artificial bee colony algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed.

关键词： Benchmark testing artificial bee colony algorithm Whale optimization algorithms Hybrid power systems Optimization Standards Convergence

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The estimation of flood quantiles in ungauged sites using teaching-learning based optimization and artificial bee colony algorithms

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SCIENTIA IRANICA 2018年第2期25卷 632-645页

作者： Anilan, T. Uzlu, E. Kankal, M. Yuksek, O. Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey

In this study, a Regional Flood Frequency Analysis (RFFA) was applied to 33 stream gauging stations in the Eastern Black Sea Basin, Turkey. Homogeneity of the region was determined by discordancy (D-i) and heterogeneity measures (H-i) based on L-moments. Generalized extreme-value, lognormal, Pearson type III, and generalized logistic distributions were fitted to the flood data of the homogeneous region. Based on the appropriate distribution for the region, flood quantiles were estimated for return periods of T = 5,10, 25,50,100, and 500 years. A non-linear regression model was then developed to determine the relationship between flood discharges and meteorological and hydrological characteristics of the catchment. In order to compare regression analysis with other models, artificial bee colony algorithm (ABC) and Teaching-Learning Based Optimization (TLBO) models were developed. The equations were obtained using the ABC and TLBO algorithms to estimate flood discharges for different return periods. The analysis showed that the TLBO and ABC results were superior to the regression analysis. Error values indicated that the TLBO method yielded better results for the estimation of flood quantiles for different independent variables. (C) 2018 Sharif University of Technology. All rights reserved.

关键词： Regional flood frequency analysis L-moments Teaching-learning based optimization artificial bee colony algorithm Turkey

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