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Identifying a gene knockout strategy using a hybrid of the bat algorithm and flux balance analysis to enhance the production of succinate and lactate in Escherichia coli

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BIOTECHNOLOGY AND BIOPROCESS ENGINEERING 2015年第2期20卷 349-357页

作者： Chua, Pooi San Salleh, Abdul Hakim Mohamed Mohamad, Mohd Saberi Deris, Safaai Omatu, Sigeru Yoshioka, Michifumi Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Osaka Prefecture Univ Fac Engn Sakai Osaka 5998531 Japan

The current problem for metabolic engineering is how to identify a suitable set of genes for knockout that can improve the production of certain metabolites and sustain the growth rate from the thousands of metabolic networks which are complex and combinatorial. Some approaches, such as OptKnock and OptGene, are developed to enhance the production of desired metabolites. However, the performances of these approaches are suboptimal and the obtained results are unsatisfactory because of computational limitations such as local minima. In this paper, we propose a hybrid of bat algorithm and Flux Balance Analysis (batFBA) to enhance succinate and lactate production by identifying a set of genes for knock out. The bat algorithm is an optimisation algorithm, whereas Flux Balance Analysis (FBA) is a mathematical approach to analyse the flow of metabolites through a metabolic network. The Escherichia coli iJR904 dataset was used to determine optimal knockout genes, production rate, and growth rate. By applying this hybrid method to the iJR904 dataset, we found that batFBA yielded better results than existing methods, such as OptKnock and a hybrid of Artificial Bee Colony algorithms and Flux Balance Analysis (ABCFBA), at predicting succinate and lactate production.

关键词： bat algorithm flux balance analysis succinate lactate gene knockout strategy Escherichia coli bioinformatics artificial intelligence metabolic engineering

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Using Binary-coded bat algorithm to Solve Hydrophobic-Polar Model Structure

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第2期12卷 326-329页

作者： Xue, Fei Cai, Yongquan Cui, Zhihua Li, Feixiang Cao, Yang Beijing Univ Technol Coll Comp Sci & Technol Beijing 100124 Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China

Hydrophobic-Polar model structure prediction problem is one important issue in molecular biology. This problem is to determining the native two-dimensional structure of a protein when giving the sequence of amino acid residues. Recently, a novel swarm intelligent algorithm, bat algorithm is proposed. In this paper, a new binary-coded bat algorithm is designed and applied to solve it. To show the validity of proposed algorithm, four protein sequences are employed to compare, and simulation results show it is effective.

关键词： bat algorithm Hydrophobic-Polar Model Structure Protein

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The path planning for mobile robot based on bat algorithm

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2015年第1期9卷 50-60页

作者： Guo, Jinchao Gao, Yu Cui, Guangzhao Zhengzhou Univ Light Ind Dept Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China

This article presents a novel method of global optimal path planning based on the Dijkstra algorithm and bat algorithm. This method consists of three steps: the first step is establishing the working space of mobile robot by adopting the MAKLINK graph theory, the second step is utilising the Dijkstra algorithm to obtain the sub-optimal path from the start point to the goal point, and the third step is adopting the bat algorithm to optimise the sub-optimal path so as to get the global optimal path of the robot. The result of the simulation experiment shows the proposed method is effective and can meet the real-time requirements of mobile robot. At the same time, the experiment also proves the optimal path planning of mobile robot based on bat algorithm is superior to particle swarm optimisation algorithm.

关键词： MAKLINK graph theory Dijkstra algorithm path planning bat algorithm

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A Hybrid bat algorithm for Process Planning Problem 15

A Hybrid Bat Algorithm for Process Planning Problem

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15th IFAC Symposium on Information Control Problems in Manufacturing

作者： Wang, Jinfeng Fan, Xiaoliang Zhao, Ailin Yang, Mingqiang North China Elect Power Univ Sch Energy Power & Mech Engn Baoding 071003 Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250000 Peoples R China

In CAPP, proccss planning (PP) it-wolves two simultaneous tasks: operations selection and operations sequencing, which is difficult to be carried out to achieve the satisfied process plan. In this paper, a hybrid bat algorithm (BA) has been employed to solve PP problem. To apply BA in PP problem, a 2 x n matrix is proposed to represent the process plan solution. Some strategies of encoding, decoding, population initializing have been developed to adapt the BA algorithm to qualify to solve PP problem. To avoid local convergence, two local search strategics are incorporated into the standard BA. A modification for the local search of BA is executed to improve the bats' movement. Two new operators for the different parts of the solution representation have been proposed to diversify the bat population. A simulation experiment has been carried out to verify the validity of the hybrid BA. The interesting results show the hybrid BA can generate the satisfactory solutions. (C) 2015, IFAC (International Federation or Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Process planning bat algorithm Operations selection Operations sequencing

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A bat algorithm with Generalized Walk for the Two-Stage Hybrid Flow Shop Problem

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INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2015年第3期7卷 1-16页

作者： Dekhici, Latifa Belkadi, Khaled Univ Sci & Technol Oran Math & Comp Sci Fac Oran Algeria

In the last years, a set of bio-inspired metaheuristics has proved their efficiencies in combinational and continues optimization areas. This paper intends to hybrid a discrete version of bat algorithm (BA) with Generalized Evolutionary Walk algorithm (GEWA) to solve the mono-processors two stages Hybrid Flow Shop scheduling. The authors compare the modified bat algorithm with the original one, with Particle Swarm Optimization (PSO) and with others results taken from literature. Computational results on a standard two-stage hybrid flow shop benchmark of 70 cases, and about 1700 instances, indicate that the proposed algorithm finds the best makespan (Cmax) in a good processing time comparing to the original bat algorithm and other algorithms.

关键词： bat algorithm Generalized Evolutionary Walk algorithm Makespan Particle Swarm Optimization Two Stages Hybrid Flow Shop

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Favourable test sequence generation in state-based testing using bat algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2015年第4期51卷 334-343页

作者： Srivastava, Praveen Ranjan Pradyot, Kumar Sharma, Deepshikha Gouthami, K. P. Indian Inst Management Informat Technol & Syst Grp Rohtak 124001 Haryana India Birla Inst Technol & Sci Dept Comp Sci Pilani 333031 Rajasthan India

Software testing has always been a significant component of the software development life cycle. Also, in any project a good amount of time and cost is required for testing. For optimal results, maximum testing in minimum time is desired with fewest repetitions. In large and complex systems, this is possible only through use of meta-heuristic algorithms to help decide which portions to test first. A similar approach is presented in this paper which demonstrates how the bat algorithm works upon a given state chart diagram and suggests favourable test sequences keeping in mind the critical system modules, which often need to be tested first. A graphical representation and comparative results with other algorithms currently in use reflect the basis of choosing the new alternative technique that this paper presents.

关键词： state-based testing bat algorithm meta-heuristics nature inspired algorithm software testing test sequence generation

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Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2021年第3期24卷 845-855页

作者： Paryani, Sina Neshat, Aminreza Pradhan, Biswajeet Islamic Azad Univ Fac Nat Resources & Environm Dept GIS RS Sci & Res Branch Tehran Iran Univ Technol Sydney Fac Engn & IT Ctr Adv Modelling & Geospatial Informat Syst CAMG Ultimo NSW Australia King Abdulaziz Univ Ctr Excellence Climate Change Res POB 80234 Jeddah 21589 Saudi Arabia Univ Kebangsaan Malaysia Inst Climate Change Earth Observat Ctr Bangi 43600 Selangor Malaysia

Landslide is a natural phenomenon that can turn into a natural disaster. The main goal of this research was to spatial prediction of a high-risk region located in the Zagros mountains, Iran, using hybrid machine learning and metaheuristic algorithms, namely the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), the Harris hawks optimisation (HHO), and the bat algorithm (BA). The landslide occurrences were first divided into training and testing datasets with a 70/30 ratio. Fourteen landslide-related factors were considered, and the stepwise weight assessment ratio analysis (SWARA) were employed to determine the correlation between landslides and factors. After that, the hybrid models of ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were applied to generate landslide susceptibility maps (LSMs). Finally, in order to validation and comparison of the applied models, two indexes, namely mean square error (MSE) and area under the ROC curve (AUROC), were used. According to the validation results, the AUROC values for the ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were 0.849, 0.82, 0.895, and 0.865, respectively. The SVR-HHO showed the highest accuracy, with AUROC of 0.895 and lowest MSE of 0.147, and ANFIS-BA showed the least accuracy with an AUROC value of 0.82 and MSE value of 0.218. Based on the results, although four hybrid models with more than 80% accuracy can generate very good results, the SVR is superior to the ANFIS model, whereas the HHO algorithm outperformed the bat algorithm. The map generated in this study can be employed by land use planners in more efficient management.(c) 2021 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Landslide susceptibility ANFIS SVR Harris hawks optimization bat algorithm GIS

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A Multi-Swarm bat algorithm for Global Optimization

A Multi-Swarm Bat Algorithm for Global Optimization

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IEEE Congress on Evolutionary Computation (CEC)

作者： Wang, Gai-Ge Chang, Bao Zhang, Zhaojun Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun Peoples R China Jiangsu Normal Univ Kewen Coll Dept Informat Sci Xuzhou Peoples R China Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou Peoples R China

ISBN: (纸本)9781479974924

By simulating the echolocation behavior of bats in nature, bat algorithm (BA) is proposed for global optimization that is a recently developed nature-inspired algorithm. Since then, it has been widely used in various fields. bat algorithm balance the global search and local search by adjusting loudness and pulse rate. However, there is so many loudness and pulse rate combinations that it is hard to choose the most proper one for different problems. In this paper, a multi-swarm algorithm, called multi-swarm bat algorithm (MBA), is proposed for global search problem. In MBA method, immigration operator is used to exchange information between different swarms with different parameter settings. Thus, this configuration can make a good trade-off between global and local search. In addition, the best individuals of every swarm is put into the elite swarm through selection operator. The bat individuals in elite swarm pass over next generation without performing any operators, and this can ensure these best solutions cannot be damaged during optimization process. In order to evaluate the efficiency of MBA method, MBA has been bench marked by sixteen standard test functions by comparing with basic BA. The results show that the MBA method is able to search more satisfactory function values on most benchmark problems than BA.

关键词： bat algorithm multi-swarm immigration operator selection operator benchmark problems

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Modification of the bat algorithm using Fuzzy Logic for Dynamical Parameter Adaptation

Modification of the Bat Algorithm using Fuzzy Logic for Dyna...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Perez, Jonathan Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479974924

We describe in this paper the bat algorithm and a proposed enhancement using a fuzzy system to dynamically adapt its parameter, original method is compared with the proposed method and also compared with genetic algorithm, providing a more complete analysis of the effectiveness of the bat algorithm. Simulation results on a set of mathematical functions with the fuzzy bat algorithm outperform the traditional bat algorithm and genetic algorithms.

关键词： bat algorithm Genetic algorithm Mathematical Functions Fuzzy System

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A hybrid method based on neural networks and a meta-heuristic bat algorithm for stock price prediction 2

A hybrid method based on neural networks and a meta-heuristi...

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2nd International Conference on Knowledge Based Engineering and Innovation (KBEI)

作者： Golmaryami, Marjan Behzadi, Marziveh Ahmadzadeh, Marziyeh Shiraz Univ Technol Dept Comp Engn & Informat Technol Shiraz Iran

ISBN: (纸本)9781467365062

Recent developments in the stock market have created an urgent need for efficient methods to help stockholders take appropriate decisions about their stocks. Since large fluctuations occur in the stock market over time and there are many parameters which influence this, it seems difficult to make good decisions that are also well-timed. The purpose of this study is to apply artificial neural networks (ANNs), which can deal with time series data and nonlinear parameters, to predict the next day's stock price. This research has trained the proposed ANN with a meta-heuristic bat algorithm which has a fast and powerful convergence. The recommended method has been applied to stock price forecasting for the first time. This work has used a seven-year dataset of a private bank stocks in order to prove the performance of the suggested method. After data pre-processing, three types of ANNs (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) were employed to predict the stocks' closing price. Afterwards, MATLAB was used to evaluate the performance of these three methods by scoring the target of the mean absolute percentage error (MADE). This paper indicates that the bat algorithm adjusts the weight matrix of ANN more precisely than the two other algorithms. The results may be adapted to other companies' stocks.

关键词： Artificial Neural Networks Back Propagation algorithm Particle Swarm Optimization algorithm bat algorithm Stock Market Forecasting

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