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IEEE International Conference on Bioinformatics and Biomedicine

作者： Ruoyu Liu Xin Lai Xiao Xiao Xuanping Zhang Xiaoyan Zhu Jiayin Wang School of Computer Science and Technology Xi'an Jiaotong University School of Public Policy and Administration Xi'an Jiaotong University

ISBN: (纸本)9781728118680

Identifying associations between genomic variants and phenotypes has always been an interesting research field of population genetics, which is of great significance for studying the pathogenesis of complex diseases and supporting clinical assistant decision making. Nowadays, many identification methods have been proposed to find the associations between variants and phenotypes, such as GWAS and pheWAS, and have made excellent achievements in pathological research and clinical practice. However, the existing methods only focus on single phenotype-multiple variants or single variant-multiple phenotypes, but not on multiple variants-multiple phenotypes. In the view of the fact that complex diseases often have several subtypes which differ greatly in variants and phenotypes, focusing only on single variant or single phenotype is far from enough and limits the ability of identification of those methods. Therefore, we propose a heuristic method with an AFSA framework on the solution space to identify associations between multiple variants and multiple phenotypes. In our method, each fish carries two logic trees that respectively represent the associations between variants and the associations between phenotypes. The logic trees will be iteratively updated to find a better solution according to the preset update strategies. When the iteration stop condition is reached, the algorithm will stop and output the optimal fish. The logical expression represented by the logic trees carried by the optimal fish is the associations we find. We validated the proposed method on the simulation data generated by hapgen2 and PhenotypeSimulator, and took the ratio of the number of people that can be explained by the found logical expression as the index to evaluate the performance, which was called Coverage. We conducted 9 groups of experiments, each of which was different in the number of variants and phenotypes. The best Coverage of was from the group including 500 variants and 10 pheno

关键词： Association Analysis artificial fish swarm algorithm Multiple Variants-Multiple Phenotypes Heuristic algorithm variants Association (Psychology) Heuristic algorithms Association analysis simulated data Phenotype LOGIC TREES associations Solution space

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Unmanned wave glider heading model identification and control by artificial fish swarm algorithm

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Journal of Central South University 2018年第9期25卷 2131-2142页

作者： WANG Lei-feng LIAO Yu-lei LI Ye ZHANG Wei-xin PAN Kai-wen Science and Technology on Underwater Vehicle Laboratory Harbin Engineering University

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was ***,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical *** view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the *** on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm *** motion comparison and maritime control experiments of the“Ocean Rambler”UWG were *** test results show high precision of heading motion prediction including heading angle and yawing angular *** UWG shows good control performance in tank tests and sea *** efficiency of the proposed method is verified.

关键词： unmanned wave glider artificial fish swarm algorithm heading model parameters identification control parameters optimization

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Application of an artificial fish swarm algorithm in Solving Multiobjective Trajectory Optimization Problems

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CHEMISTRY AND TECHNOLOGY OF FUELS AND OILS 2017年第4期53卷 541-547页

作者： Sun, Tengfei Zhang, Hui Liu, Shujie Cao, Yanfeng CNOOC Res Inst Beijing Peoples R China China Univ Petr Beijing Peoples R China

Drilling faces many complex design and multiobjective optimization problems. Solving these problems is also a critical and complicated part of drilling optimization as part of well trajectory design and optimization. Many researchers have developed many algorithms, but they have some disadvantages. We take the shortest total borehole length, the highest target shooting accuracy, the lowest cost, and the minimum friction as the multiobjective function, and we use a fish swarm algorithm for trajectory optimization. In this paper, we present the idea of using a nondominant relation for sorting in the algorithm and we also use an optimization program in the Matlab software to obtain all numerical solutions satisfying the constraints. Therefore it is quite adaptable for introducing the idea of nondominant sorting into appropriate multiobjective optimization problems based on a fish swarm algorithm. We give an example of the calculation, and also show that the algorithm and the calculation procedure are accurate and reliable. The algorithm has a simple structure, a small number of calculations, and good convergence.

关键词： artificial fish swarm algorithm sorting trajectory multiobjective optimization

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Structural multi-damage identification based on strain energy and micro-search artificial fish swarm algorithm

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JOURNAL OF VIBROENGINEERING 2017年第5期19卷 3255-3270页

作者： Guo, Huiyong Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China

A two-stage damage detection method based on strain energy and micro-search artificial fish swarm algorithm (MSAFSA) is presented for solving structural multi-damage problem. First, structural modal strain energy and energy dissipation process are analyzed and an improved modal strain energy dissipation ration index (IMSEDRI) is proposed to preliminarily detect suspected damage elements. Then, artificial fish swarm algorithm (AFSA) is used to identify damage extent of suspected damage elements. In general, the search efficiency of a basic AFSA isn't very efficient for the search procedure. So, a micro-search artificial fish swarm algorithm is presented in this paper. The simulation results demonstrate that the damage detection method can estimate the damage locations and extent with good accuracy, and the calculated results of the proposed MSAFSA are obviously superior to those of both the basic AFSA and the AFSA with visual-step change strategy.

关键词： damage identification artificial fish swarm algorithm modal strain energy visual distance step length

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Parameter estimation of a composite production function model based on improved artificial fish swarm algorithm and model application

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第10期46卷 8218-8232页

作者： Cheng, Maolin Xiang, Minyin Suzhou Univ Sci & Technol Sch Math & Phys Suzhou 215009 Peoples R China Huangshan Univ Sch Math & Stat Huangshan Peoples R China

The traditional Cobb-Douglas production function uses the compact mathematical form to describe the relationship between the production results and production factors in the production technology process. However, in macro-economic production, multi-structured production exists universally. In order to better demonstrate such input-output relation, a composite production function model is proposed in this article. In aspect of model parameter estimation, artificial fish swarm algorithm is applied. The algorithm has satisfactory performance in overcoming local extreme value and acquiring global extreme value. Moreover, realization of the algorithm does not need the gradient value of the objective function. For this reason, it is adaptive to searching space. Through the improved artificial fish swarm algorithm, convergence rate and precision are both considerably improved. In aspect of model application, the composite production function model is mainly used to calculate economic growth factor contribution rate. In this article, a relatively more accurate calculating method is proposed. In the end, empirical analysis on economic growth contribution rate of China is implemented.

关键词： artificial fish swarm algorithm Empirical analysis Composite production function Contribution rate

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A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 86卷 165-176页

作者： Zhang, Zeqiang Wang, Kaipu Zhu, Lixia Wang, Yi Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China Auburn Univ Dept Math & Comp Sci Montgomery AL USA

To better reflect the uncertainty existing in the actual disassembly environment, the multi-objective disassembly line balancing problem with fuzzy disassembly times is investigated in this paper. First, a mathematical model of the multi-objective fuzzy disassembly line balancing problem (MFDLBP) is presented, in which task disassembly times are assumed as triangular fuzzy numbers (TFNs). Then a Pareto improved artificial fish swarm algorithm (IAFSA) is proposed to solve the problem. The proposed algorithm is inspired from the food searching behaviors of fish including prey, swarm and follow behaviors. An order crossover operator of the traditional genetic algorithm is employed in the prey stage. The Pareto optimal solutions filter mechanism is adopted to filter non-inferior solutions. The proposed model after the defuzzification is validated by the LINGO solver. And the validity and the superiority of the proposed algorithm are proved by comparing with a kind of hybrid discrete artificial bee colony (HDABC) algorithm using two test problems. Finally, the proposed algorithm is applied to a printer disassembly instance including 55 disassembly tasks, for which the computational results containing 12 non-inferior solutions further confirm the practicality of the proposed Pareto IAFSA in solving the MFDLBP. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Disassembly line balancing problem Triangular fuzzy number Multi-objective optimization artificial fish swarm algorithm Pareto optimal solutions

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Energy Efficient Hybrid Routing Protocol Based on the artificial fish swarm algorithm and Ant Colony Optimisation for WSNs

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SENSORS 2018年第10期18卷 3351-3351页

作者： Li, Xinlu Keegan, Brian Mtenzi, Fredrick Hefei Univ Dept Comp Sci Hefei 230601 Anhui Peoples R China Dublin Inst Technol Sch Comp Dublin 8 Ireland

Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the artificial fish swarm algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime.

关键词： wireless sensor networks hybrid routing protocol ant colony optimisation artificial fish swarm algorithm

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A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm

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SOFT COMPUTING 2018年第12期22卷 3907-3917页

作者： Xian, Sidong Zhang, Jianfeng Xiao, Yue Pang, Jia Chongqing Univ Posts & Telecommun Sch Sci Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China Inha Univ Sch Comp Engn Incheon 22212 South Korea

Recently, many forecasting methods have been proposed for the analysis of fuzzy time series. The main factors that affect the results of the forecasting of these models are partition universe of discourse and determination of fuzzy relations. In this paper, a novel fuzzy time series forecasting method which uses a hybrid artificial fish swarm optimization algorithm for the determination of interval lengths is proposed. Firstly, we introduce the chemotactic behavior of Bacterial foraging optimization into foraging behavior. Secondly, the Levy flight is used as the mutation operator for a mutation strategy. Finally, the new proposed method is applied to a fuzzy time series forecasting and the experimental results show that the proposed model obtain better forecasting results than those of other existing models. It proves the feasibility and validity of above-mentioned approaches.

关键词： Fuzzy time series Forecasting artificial fish swarm algorithm Levy flight HAFSA

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A Power Allocation algorithm Based on artificial fish swarm algorithm for Cognitive Radio Networks Based on Interference Alignment 4

A Power Allocation Algorithm Based on Artificial Fish Swarm ...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Guo, Kai Qiu, Runhe Cai, Caixia Donghua Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781538611074

In order to improve the overall quality of service (QoS) of cognitive radio network based on interference alignment (IA), an optimal power allocation algorithm based on artificial fish swarm algorithm is proposed in this paper. The algorithm uses artificial fish swarm algorithm to allocate the power of each user reasonably, so as to achieve the minimum average interruption probability of secondary user (SU). In order to ensure the communication quality of primary user (PU), this paper also proposes a minimum power threshold, the setting of this threshold ensures both the communication rate of PU and the outage probability of PU, and effectively guarantee the requirement for communication quality of PU. The simulation results also show that the proposed algorithm can effectively improve the overall communication quality of the system and minimize the average outage probability of the SU.

关键词： interference alignment cognitive wireless networks power allocation outage probability artificial fish swarm algorithm

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Development and Analysis of a Modified artificial fish swarm algorithm 13

Development and Analysis of a Modified Artificial Fish Swarm...

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13th International Conference on Electronics, Computer and Computation (ICECCO)

作者： Baba, Yachilla Ugweje, Okechukwu C. Koyunlu, Gokhan Nile Univ Nigeria Dept Elect & Elect Engn Abuja Nigeria

ISBN: (纸本)9781538625019

The analytical development and simulation of a modified artificial fish swarm algorithm is presented in this paper. It addresses the problem of the conventional AFSA migration into the local minima due to the constant effects of its control parameters such as the visual distance and step size. An iterative behavior was introduced such that each generation can select its guiding parameters adaptively. The performance of the modified algorithm was evaluated using a subset of applied mathematical optimization test functions namely Ackley, Cosine Mixture, Neumaier, Rosenbrock and Rastragin. Results show that the modified algorithm performed better in four (4) out of the 5 test functions.

关键词： artificial fish swarm algorithm modified artificial fish swarm algorithm test functions adaptive algorithm

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