检索结果-内蒙古大学图书馆

第三十九届中国控制会议

作者： Yuqin Zhou Zhiyao Zhao Xiaoyi Wang Qian Sun School of Artificial Intelligence Beijing Technology and Business University China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University

Water Quality Evolution Mechanism（WQEM） modeling and Water quality estimation are important technical means for water pollution prevention and control of lakes and reservoirs. However, existing classical WQEM models usually contain unknown parameters with empirical values range, which brings difficulty of estimation water quality changes of specific lakes and reservoirs to meet the accuracy requirements. Furthermore, water quality indicator is susceptible to natural factors and human factors, which makes the water system complex and nonliner, and enhances the difficulty of water quality estimation. Therefore,combining water quality mechanism, this paper proposes a fruit fly optimization algorithm（FFOA） based WQEM modeling method and studies a method of water quality estimation based on Particle Filter（PF） algorithm. First, a more comprehensive WQEM model is established to characterize the water quality mechanism of lakes and reservoirs. Then, combining observed data of water quality indicator and WQEM, the unknown parameters of a WQEM model are estimated by using FFOA. Finally,PF algorithm is used to estimate the water quality status. Simulation results show that the method can effectively estimate the unknown parameters of the WQEM model and estimate the water quality status.

关键词： Water quality evolution mechanism fruit fly optimization algorithm particle filter modeling estimation

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Optimal riser design method based on geometric reasoning method and fruit fly optimization algorithm in CAD

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第1-4期96卷 53-65页

作者： Wang, Tong Yin, Yajun Zhou, Jianxin Shen, Xu Wang, Min Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Hubei Peoples R China

With the continuous development and improvement of computer simulation technology, computer simulation analysis has become an important means for judging the feasibility of casting process design. But computer consumption is also gradually increasing due to the more complex castings, the huge computation quantity and the long cycle of casting process design. Therefore, in order to reduce the computer consumption, a new method for optimal riser design based on geometric reasoning method and fruit fly optimization algorithm in CAD is proposed in this paper. The main focus of the method is to carry out the initial process optimization design of riser purely taking into account the casting geometry based on CAD techniques. Firstly, geometric reasoning method based on implicit surface is used to obtain every hot spot of casting. Secondly, the open source CAD application (HeeksCAD) is used to compute the volume and heat transfer area of casting which need to be compensated. Thirdly, fruit fly optimization algorithm (FOA) is used to optimize the riser geometric sizes. A cylinder sleeve casting is taken as an example to illustrate the feasibility of the methodology. Finally, numerical modeling method confirms the validity of the implementation of the new methodology for optimal riser design. The results indicate that the method could be useful in cutting down the expense and time of casting production cycle, particularly for casting process optimization stage.

关键词： Hot spots Implicit surface Geometric reasoning method fruit fly optimization algorithm Riser optimization

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Improved fruit fly optimization algorithm Incorporating Tabu Search for Optimizing the Selection of Elements in Trusses

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第12期22卷 4940-4954页

作者： Li, Yancang Lian, Sida Hebei Univ Engn Coll Water Conservancy & Hydroelect Power Handan 056038 Hebei Peoples R China Hebei Univ Engn Coll Civil Engn Handan 056038 Hebei Peoples R China

In order to find a more effective method for the structural optimization, an improved fruit fly optimization algorithm was proposed. The dynamic adjustment search, the inertia weight function and the tabu search theory were employed to overcome the premature flaw of the basic algorithm. Then, the improved algorithm was introduced to the structural optimization of the tube- type trestle. After the setup of the optimization model, the improved algorithm was used. optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.

关键词： structural optimization tube-type trestle fruit fly optimization algorithm self-adaptive tabu search

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Construct the prediction model for China agricultural output value based on the optimization neural network of fruit fly optimization algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 86卷 663-669页

作者： Han, Shi-Zhuan Pan, Wen-Tsao Zhou, Ying-Ying Liu, Zong-Li East China Jiaotong Univ Sch Econ & Management Nanchang Jiangxi Peoples R China Guangdong Univ Foreign Studies Sch Business Guangzhou Guangdong Peoples R China

Since agriculture is the foundation of a country and the industry that people depend on for life, it is particularly important for the development of national economy, and it has a higher output value than forestry, fishery and animal husbandry, so it occupies a very important position in the economic development of a country. The aim of this paper is to strengthen the capacity of prediction mode for total agricultural output value. This paper provides relevant government departments a reference and solves the problem of the lack of predictive ability of prediction mode for total agricultural output value in previous study. Different from previous literature, this paper adopts the new CFOA to optimize the parameters of GRNN, which contains innovative and reference value in some degree. Besides the way to validate this new model is to take the agricultural output value of the past years as a research sample and test it repeatedly. The study results have indicated that the total agricultural production value accounts for a higher proportion of agriculture, forestry, fishery and animal husbandry and the proportion tends to decline year by year;it can be found through 4 evaluation indexes that the prediction model that optimizes the smoothing parameters of GRNN through CFOA has a better predictive ability than the other two prediction models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Improved fruit fly optimization algorithm Chaos fruit fly optimization algorithm General Regression Neural Network Total agricultural production value

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An effective fruit fly optimization algorithm with hybrid information exchange and its applications

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2018年第10期9卷 1623-1648页

作者： Lv, Sheng-Xiang Zeng, Yu-Rong Wang, Lin Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Sch Informat Engn Wuhan 430205 Hubei Peoples R China

As a newly proposed algorithm, fruit fly optimization algorithm (FOA) has been shown to have a strong capacity for solving numerical optimization problems. However, the basic FOA is faced with the challenges of poor diversity of the swarm and weak local search ability because of the improper osphresis operation and vision operation. To overcome these limitations synthetically, we propose an improved FOA based on hybrid location information exchange mechanism (HFOA) aiming at improving the swarm diversity in a more efficient way and well balance the global search and local search abilities. First, the proposed HFOA enables flies to communicate with each other and conduct local search in a swarm based approach. Second, osphresis operation is conducted in probability to balance the global search and local search processes. Finally, a mutation strategy called cataclysm policy is designed to help the flies jump out of the local extreme points. 18 complex continuous benchmark functions are used to test the performance of HFOA. Numerical experiments results indicate that HFOA outperforms main state-of-the-art algorithms. A classical non-deterministic polynomial hard problema widely-researched joint replenishment and delivery scheduling problem with resource restrictions is also used to further verify the ability of HFOA in solving practical operation management problems. Results show that HFOA can obtain lower operation cost than other widely used methods, demonstrating its ability to solve various complex optimization problems.

关键词： fruit fly optimization algorithm Information exchange Hybrid exchange Function optimization Joint replenishment and delivery scheduling

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A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 38卷 54-63页

作者： Wang, Ling Zheng, Xiao-long Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a knowledge-guided multi-objective fruit fly optimization algorithm (MOFOA) is proposed for the multi-skill resource-constrained project scheduling problem (MSRCPSP) with the criteria of minimizing the makespan and the total cost simultaneously. First, a solution is represented by two lists, i.e. resource list and task list. Second, the minimum total cost rule is designed for the initialization according to the property of the problem. Third, the smell-based search is implemented via the neighborhood based search operators that are specially designed for the MSRCPSP, while the vision-based search adopts the technique for the order preference by similarity to an ideal solution (TOPSIS) and the non-dominated sorting collaboratively to complete the multi-objective evaluation. In addition, a knowledge-guided search procedure is introduced to enhance the exploration of the FOA. Finally, the design-of-experiment (DOE) method is used to investigate the effect of parameter setting, and numerical tests based on benchmark instances are carried out. The results compared to other algorithms demonstrate the effectiveness of the MOFOA with knowledge-guided search in solving the multi-objective MSRCPSP.

关键词： fruit fly optimization algorithm Multi-skill resource constrained project scheduling problem Knowledge Multi-objective optimization

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A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems

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KNOWLEDGE-BASED SYSTEMS 2018年第Mar.15期144卷 153-173页

作者： Wu, Lei Liu, Qi Tian, Xue Zhang, Jixu Xiao, Wensheng China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore Marine Design & Res Inst China Shanghai 200011 Peoples R China CNOOC Safety Technol Serv Co Ltd Tianjin 300456 Peoples R China

Nature-inspired algorithms are widely used in mathematical and engineering optimization. As one of the latest swarm intelligence-based methods, fruit fly optimization algorithm (FOA) was proposed inspired by the foraging behavior of fruit fly. In order to overcome the shortcomings of original FOA, a new improved fruit fly optimization algorithm called IAFOA is presented in this paper. Compared with original FOA, IAFOA includes four extra mechanisms: 1) adaptive selection mechanism for the search direction, 2) adaptive adjustment mechanism for the iteration step value, 3) adaptive crossover and mutation mechanism, and 4) multi-sub-swarm mechanism. The adaptive selection mechanism for the search direction allows the individuals to search for global optimum based on the experience of the previous iteration generations. According to the adaptive adjustment mechanism, the iteration step value can change automatically based on the iteration number and the best smell concentrations of different generations. Besides, the adaptive crossover and mutation mechanism introduces crossover and mutation operations into IAFOA, and advises that the individuals with different fitness values should be operated with different crossover and mutation probabilities. The multi-sub-swarm mechanism can spread optimization information among the individuals of the two sub-swarms, and quicken the convergence speed. In order to take an insight into the proposed IAFOA, computational complexity analysis and convergence analysis are given. Experiment results based on a group of 29 benchmark functions show that IAFOA has the best performance among several intelligent algorithms, which include five variants of FOA and five advanced intelligent optimization algorithms. Then, IAFOA is used to solve three engineering optimization problems for the purpose of verifying its practicability, and experiment results show that IAFOA can generate the best solutions compared with other ten algorithms. (C) 2017 Els

关键词： fruit fly optimization algorithm Optimal search direction Iteration step value Crossover and mutation operations Multi-sub-swarm Engineering optimization problem

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Structural damage identification using cloud model based fruit fly optimization algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2018年第3期67卷 245-254页

作者： Zheng, Tongyi Liu, Jike Luo, Weili Lu, Zhongrong Sun Yat Sen Univ Dept Appl Mech Guangzhou Guangdong Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou Guangdong Peoples R China

In this paper, a Cloud Model based fruit fly optimization algorithm (CMFOA) is presented for structural damage identification, which is a global optimization algorithm inspired by the foraging behavior of fruit fly swarm. It is assumed that damage only leads to the decrease in elementary stiffness. The differences on time-domain structural acceleration data are used to construct the objective function, which transforms the damaged identification problem of a structure into an optimization problem. The effectiveness, efficiency and accuracy of the CMFOA are demonstrated by two different numerical simulation structures, including a simply supported beam and a cantilevered plate. Numerical results show that the CMFOA has a better capacity for structural damage identification than the basic fruit fly optimization algorithm (FOA) and the CMFOA is not sensitive to measurement noise.

关键词： damage identification swarm intelligence cloud model fruit fly optimization algorithm time domain data

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Pattern synthesis of the multimode orbital angular momentum beams based on the fruit fly optimization algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2019年第10期29卷 e21876-e21876页

作者： Li, Qingwen Li, Xiuping Qi, Zihang Zhu, Hua Huang, Yuhan Akram, Zaid Jiang, Xing Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China Guilin Univ Elect Technol Guangxi Coll Key Lab Microwave & Opt Wave Applicat Technol Guilin Peoples R China

The multimode orbital angular momentum (OAM) radio waves can be used to multiplex multiple transmission channels to increase the capacity of communication system without adding additional bandwidth. However, the divergence of the OAM beams and beam inconsistency escalate by increasing OAM mode number. Moreover, the worse sidelobe level (SLL) always appears along with a better convergent beam. In this article, the fruit fly optimization algorithm (FOA) is proposed to suppress the SLL in multimode OAM scenario. Based on the concentric circular array antenna (CCAA), the feeding amplitudes and the radii of the array are synthesized simultaneously to realize less than -30 dB SLL of the multimode OAM patterns. When the main lobes with different OAM modes steered to a uniform azimuth of theta = 0 degrees, the SLLs of these OAM modes are also suppressed to less than -21 dB. The advantages of FOA used in the OAM pattern synthesis are verified by comparing it with the genetic algorithm (GA). The FOA-based synthesis has a simpler implementation flow diagram which reduces the time of synthesis to 39.5% of GA.

关键词： beam uniformity optimization fruit fly optimization algorithm orbital angular momentum pattern synthesis vortex electromagnetic wave

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Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete fruit fly optimization algorithm

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ENERGIES 2019年第17期12卷 3260页

作者： Zhang, Xiaohui Liu, Xinhua Tang, Shufeng Krolczyk, Grzegorz Li, Zhixiong China Univ Min Technol Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Inner Mongolia Univ Technol Sch Mech Engn Hohhot 010051 Peoples R China Opole Univ Technol Dept Mfg Engn & Automat Prod PL-45758 Opole Poland Tsinghua Univ Suzhou Automot Res Inst Suzhou 215134 Peoples R China Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia

This study attempts to optimize the scheduling decision to save production cost (e.g., energy consumption) in a distributed manufacturing environment that comprises multiple distributed factories and where each factory has one flow shop with blocking constraints. A new scheduling optimization model is developed based on a discrete fruit fly optimization algorithm (DFOA). In this new evolutionary optimization method, three heuristic methods were proposed to initialize the DFOA model with good quality and diversity. In the smell-based search phase of DFOA, four neighborhood structures according to factory reassignment and job sequencing adjustment were designed to help explore a larger solution space. Furthermore, two local search methods were incorporated into the framework of variable neighborhood descent (VND) to enhance exploitation. In the vision-based search phase, an effective update criterion was developed. Hence, the proposed DFOA has a large probability to find an optimal solution to the scheduling optimization problem. Experimental validation was performed to evaluate the effectiveness of the proposed initialization schemes, neighborhood strategy, and local search methods. Additionally, the proposed DFOA was compared with well-known heuristics and metaheuristics on small-scale and large-scale test instances. The analysis results demonstrate that the search and optimization ability of the proposed DFOA is superior to well-known algorithms on precision and convergence.

关键词： energy saving and efficiency distributed manufacturing system blocking constraint distributed flow shop scheduling fruit fly optimization algorithm

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