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A Hybrid Metaheuristic Method in Training Artificial Neural Network for Bankruptcy Prediction

IEEE ACCESS 2020年 8卷 176640-176650页

作者： Ansari, Abdollah Ahmad, Ibrahim Said Abu Bakar, Azuraliza Yaakub, Mohd Ridzwan Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Fac Informat Sci & Technol Bangi 43600 Malaysia Inst Intelligent Nano & Informat INI Tehran *** Iran Bayero Univ Kano Fac Comp Sci & Informat Technol Dept Informat Technol Kano 700241 Nigeria

Corporate bankruptcy prediction is an important task in the determination of corporate solvency, that is, whether a company can meet up to its financial obligations or not. It is widely studied as it has a significant effect on employees, customers, management, stockholders, bank lending assessments, and profitability. In recent years, machine learning techniques, particularly Artificial Neural Network (ANN), have widely been studied for bankruptcy prediction since they have proven to be a good predictor, especially in financial applications. A critical process in learning a network is weight training. Although the ANN is mathematically efficient, it has a complex weight training process, especially in computation time when involving a large training data. Many studies improved ANN's weight training using metaheuristic algorithms such as evolutionary algorithms (EA), and Swarm Intelligence (SI) approaches for bankruptcy prediction. In this study, two metaheuristics algorithms, Magnetic optimization Algorithm (MOA) and Particle Swarm optimization (PSO), have been enhanced through hybridization to propose a new method MOA-PSO. Hybrid algorithms have been proven to be capable of solving optimization problems faster, with better accuracy. The MOA-PSO was used in training ANN to improve the performance of the ANN in bankruptcy prediction. The performance of the hybrid MOA-PSO was compared with that of four existing algorithms. The proposed hybrid MOA-PSO algorithm exhibits promising results with a faster and more accurate prediction, with 99.7% accuracy.

关键词： Bankruptcy Prediction algorithms Artificial neural networks Training optimization Sociology Statistics evolutionary optimization algorithms bankruptcy prediction artificial neural network magnetic optimization algorithm particle swarm optimization metaheuristic

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Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding

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APPLIED SOFT COMPUTING 2014年 23卷 128-143页

作者： Kurban, Tuba Civicioglu, Pinar Kurban, Rifat Besdok, Erkan Erciyes Univ Fac Engn Dept Geomat Engn TR-38010 Kayseri Turkey Erciyes Univ Civil Aviat Collage Dept Aircraft Elect & Elect TR-38010 Kayseri Turkey Erciyes Univ Fac Engn Dept Comp Engn TR-38010 Kayseri Turkey

This paper introduces the comparison of evolutionary and swarm-based optimization algorithms for multilevel color image thresholding problem which is a process used for segmentation of an image into different regions. Thresholding has various applications such as video image compression, geovideo and document processing, particle counting, and object recognition. evolutionary and swarm-based computation techniques are widely used to reduce the computational complexity of the multilevel thresholding problem. In this study, well-known evolutionary algorithms such as Evolution Strategy, Genetic Algorithm, Differential Evolution, Adaptive Differential Evolution and swarm-based algorithms such as Particle Swarm optimization, Artificial Bee Colony, Cuckoo Search and Differential Search Algorithm have been used for solving multilevel thresholding problem. Kapur's entropy is used as the fitness function to be maximized. Experiments are conducted on 20 different test images to compare the algorithms in terms of quality, running CPU times and compression ratios. According to the statistical analysis of objective values, swarm based algorithms are more accurate and robust than evolutionary algorithms in general. However, experimental results exposed that evolutionary algorithms are faster than swarm based algorithms in terms of CPU running times. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Color image thresholding evolutionary optimization algorithms Swarm-based optimization algorithms

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Assembly sequence optimization using a flower pollination algorithm-based approach

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第2期30卷 461-482页

作者： Mishra, Atul Deb, Sankha Indian Inst Technol Kharagpur Dept Mech Engn FMS & CIM Lab Kharagpur 721302 W Bengal India

One of the important decisions in assembly process planning is determination of assembly sequence. Choice of the optimum sequence is made difficult due to various reasons. There are various precedence constraints and optimization criteria. Moreover, a product may be possible to assemble in many alternative ways following different sequences, thus making assembly sequence optimization a multi-modal optimization problem with multiple optimum solutions. It is necessary to generate as many unique optimum solutions as possible in order to allow the process planner to take a decision. Moreover, with increase in part count, the number of feasible sequences rises staggeringly, thereby making assembly sequence optimization laborious and time consuming. Most conventional mathematical algorithms are known to perform poorly when used to obtain multiple optimum solutions. On the other hand, soft computing based evolutionary optimization algorithms are good candidates for multi-modal optimization. Another challenge is to develop an algorithm that can automatically maintain diversity in the optimum solutions found over the generations (i.e. optimum solutions having the same fitness but unique). Keeping the above in mind, in the present paper, an intelligent assembly sequence optimization methodology based on application of flower pollination algorithm (FPA) has been developed to automatically generate multiple unique optimal assembly sequences, subject to various precedence constraints, based on minimisation of number of orientation changes and tool changes. Since in the present paper, FPA has been applied for the first time to a discrete optimization problem like assembly sequence optimization, the main challenge before us in applying FPA was the continuous nature of the original FPA. Therefore, modifications have been made by us in the rules for local and global pollination of FPA to make it suited for solving the given discrete optimization problem. In order to evaluate the per

关键词： Assembly sequence optimization Computer-aided process planning evolutionary optimization algorithms Flower pollination algorithm (FPA)

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Non-uniform embedding applications for synchronization analysis

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JOURNAL OF VIBROENGINEERING 2016年第8期18卷 5481-5491页

作者： Drulyte, Migle Lukoseviciute, Kristina Ragulskiene, Jurate Kaunas Univ Technol Dept Math Modeling Kaunas Lithuania

Iterative polynomial model for synchronization of vibrations was proposed. A polynomial was constructed using time delay coordinates. For time delay kernel estimation, a non-uniform embedding was used. evolutionary optimization algorithms were introduced for non-uniform time delay identification. Performance investigation of proposed method was done for two different vibrations.

关键词： synchronization of vibration non-uniform embedding evolutionary optimization algorithms

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MPSoCs and Multicore Microcontrollers for Embedded PID Control: A Detailed Study

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2014年第4期10卷 2122-2134页

作者： Youness, Hassan Moness, Mohamed Khaled, Mahmoud Menia Univ Comp & Syst Engn Dept Fac Engn El Minia 61111 Egypt

This paper presents different multiprocessor implementations of the proportional-integral-derivative (PID) controller using two technologies: 1) field programmable gate array (FPGA)-based multiprocessor system-on-chip (MPSoC);and 2) multicore microcontrollers (MCUs). Techniques to implement a parallelized PID controller, a multi-PID controller, and a self-tuning PID controller are proposed. These techniques are verified using hardware (HW) in the loop (HIL) simulations. Then, the paper presents a detailed case study of an embedded real-time (RT) self-tuning PID controller for a 1-degree-of-freedom (1-DOF) aerodynamical system. This includes controller design, parameters tuning, and implementation using a multiprocessor system. Results proved the effectiveness of the proposed techniques to improve performance and functionality. It is shown that customizing HW and software (SW) within MPSoCs provides higher RT performance. Moreover, using multicore MCUs can reduce design time, implementation time, and cost, while keeping adequate performance. Therefore, it is possible to realize and implement complex RT embedded controllers that employ advanced control algorithms in rapid, effective, and cost-efficient fashion.

关键词： evolutionary optimization algorithms field programmable gate array (FPGA) fuzzy logic (FL) tuning hardware in the loop (HIL) simulation multicore microcontroller (MCU) multiprocessor systems-on-a-chip (MPSoC) PID control 1-Degree-of-freedom (DOF) helicopter

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Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2019年第2期9卷 1355-1373页

作者： Ghorbani, Hamzeh Wood, David A. Moghadasi, Jamshid Choubineh, Abouzar Abdizadeh, Peyman Mohamadian, Nima Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran DWA Energy Ltd Lincoln England Petr Ind Univ Petr Engn Dept Ahvaz Iran Petr Univ Technol Petr Dept Ahvaz Iran Iranian Assoc Chem Engineers North Tehran Branch Tehran Iran Azad Univ Omidiyeh Branch Petr Dept Young Researchers Club Omidiyeh Iran

None of the various published models used to predict oil production rates through wellhead chokes from fluid composition and pressures can be considered as a universal model for all regions. Here, a model is provided to predict liquid production-flow rates for the Reshadat oil field offshore southwest Iran, applying a customized genetic optimization algorithm (GA) and standard Excel Solver non-linear and evolutionary optimization algorithms. The dataset of 182 records of wellhead choke measurements spans liquid flow rates from <100 to 30,000 stock tank barrels/day. Each data record includes measurements of five variables: liquid production rate (QL), wellhead pressure, choke size, basic sediment and water, and gas-liquid ratio. 70% of the dataset (127 data records) was used for training purposes to establish the prediction relationships, and 30% of the dataset (55 data records) was utilized for independently testing the accuracy of the derived relationships as predictive tools. The methodology applying either the customized GA or standard Solver optimization algorithms, demonstrates significant improvements in QL-prediction accuracy with the lowest APD (-7.72 to -2.89), AAPD (7.33-8.51), SD (288.77-563.85), MSE (91,871-316,429), and RMSE (303.1-562.52);and the highest R-2 (greater than 0.997) compared to six previously published liquid flow-rate prediction models. As a general result, the novel methodology is easily applied to other field/reservoir datasets, to achieve rapid practical flow prediction applications, and is consequently of worldwide significance.

关键词： Flow-rate prediction evolutionary optimization algorithms Non-linear optimization Choke size Liquid production rate Wellhead flow-rate variables

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Automated design architectures for co-orbiting spacecraft swarms for planetary moon mapping

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ADVANCES IN SPACE RESEARCH 2021年第11期67卷 3559-3582页

作者： Nallapu, Ravi Teja Thangavelautham, Jekan Univ Arizona Space & Terr Robot Explorat SpaceTREx Lab Asteroid Sci Technol & Explorat Res Dept Aerosp & Mech EngnInclus eDuc ASTEROID Lab 1130 N Mt Ave Tucson AZ 85721 USA

This work describes the design and optimization of spacecraft swarm missions to meet spatial and temporal visual mapping requirements of missions to planetary moons, using resonant co-orbits. The algorithms described here are a part of Integrated Design Engineering and Automation of Swarms (IDEAS), a spacecraft swarm mission design software that automates the design trajectories, swarm, and spacecraft behaviors in the mission. In the current work, we focus on the swarm design and optimization features of IDEAS, while showing the interaction between the different design modules. In the design segment, we consider the coverage requirements of two general planetary moon mapping missions: global surface mapping and region of interest observation. The configuration of the swarm co-orbits for the two missions is described, where the participating spacecraft have resonant encounters with the moon on their orbital apoapsis. We relate the swarm design to trajectory design through the orbit insertion maneuver performed on the interplanetary trajectory using aero-braking. We then present algorithms to model visual coverage, and collision avoidance in the swarm. To demonstrate the interaction between different design modules, we relate the trajectory and swarm to spacecraft design through fuel mass, and mission cost estimations using preliminary models. In the optimization segment, we formulate the trajectory and swarm design optimizations for the two missions as Mixed Integer Nonlinear Programming (MINLP) problems. In the current work, we use Genetic Algorithm as the primary optimization solver. However, we also use the Particle Swarm Optimizer to compare the optimizer performance. Finally, the algorithms described here are demonstrated through numerical case studies, where the two visual mapping missions are designed to explore the Martian moon Deimos. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Spacecraft swarms Automated mission design Planetary moon exploration Resonant co-orbits evolutionary optimization algorithms

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Bacterial Foraging Based Optimal Design of Transverse Flux Linear Motor for Thrust Force Improvement

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第1期43卷 95-104页

作者： Hasanien, Hany M. Ali, Syed Qaseem Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo Egypt McGill Univ Dept Elect & Comp Engn Montreal PQ Canada

Abstract-This article presents a novel optimal design for a permanent magnet excitation transverse flux linear motor with an inner mover using bacterial foraging optimization. The target is maximizing the motor thrust force, which is the most important quantity in linear electric drives. The stator pole length, air-gap length, winding window width, and stator pole width define the search space for the optimization problem. The response surface methodology is used to build the mathematical model of the motor thrust force in terms of the design variables. It can create an objective function easily, and great computational time is saved. Finite-element computations are used for numerical experiments on the geometrical design variables to determine the coefficients of a second-order analytical model for the response surface methodology. The bacterial foraging optimization technique is used as a searching tool under the constraints of design variables for design optimization of the transverse flux linear motor to improve the motor thrust force. The effectiveness of the proposed bacterial foraging optimization model is then compared with that of both genetic algorithm and particle swarm optimization models. With this proposed bacterial foraging optimization technique, the thrust force of the initially designed transverse flux linear motor can be increased.

关键词： design optimization permanent magnet motors evolutionary optimization algorithms three-dimensional finite-element analysis linear motors transverse flux linear motor thrust force optimization response surface methodology bacterial foraging finite-element method

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An optimization-based methodology for the definition of amplitude thresholds of the ground penetrating radar

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SOFT COMPUTING 2019年第22期23卷 12063-12086页

作者： Abdelkader, Eslam Mohammed Marzouk, Mohamed Zayed, Tarek Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ Canada Cairo Univ Struct Engn Dept Fac Engn Giza Egypt Cairo Univ Struct Engn Dept Construct Engn & Management Fac Engn Giza Egypt Hong Kong Polytech Univ Construct & Real Estate Dept Hung Hom Hong Kong Peoples R China

Existing infrastructure is aging, while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges are subjected to severe deterioration agents such as variable traffic loading, deferred maintenance, cycles of freeze and thaw. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays due to the huge variance between the need for maintenance actions and the available funds to perform such actions. Condition assessment is regarded as one of the most critical and vital components of BMSs. Ground penetrating radar (GPR) is one of the nondestructive techniques that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. There is a major issue associated with the GPR which is the absence of a scale for the amplitude values. The objective of the proposed model is to compute standardized amplitude thresholds for corrosion maps. The proposed model considers eight un-supervised clustering algorithms to obtain the thresholds. The proposed model incorporates a multi-objective optimization-based methodology that employs three evolutionary optimization algorithms to calculate the optimum thresholds which are: (1) genetic algorithm, (2) particle swarm optimization algorithm, and (3) shuffled frog-leaping algorithm. Five multi-criteria decision-making techniques are used to provide a ranking for the solutions. Finally, group decision-making is performed to aggregate the results and obtain a consensus and compromise solution. The standardized thresholds obtained from the proposed methodology are: - 16.7619, - 8.8161, and - 2.9744 dB.

关键词： Bridge Management System Ground penetrating radar Nondestructive techniques Corrosion Amplitude thresholds evolutionary optimization algorithms Multi-criteria decision-making

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Drowsiness Detection Using Adaptive Hermite Decomposition and Extreme Learning Machine for Electroencephalogram Signals

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IEEE SENSORS JOURNAL 2018年第21期18卷 8855-8862页

作者： Taran, Sachin Bajaj, Varun Indian Inst Informat Technol Design & Mfg Elect & Commun Discipline Jabalpur 482005 India

Automatic drowsiness detection system plays a vital role to prevent the road accidents caused by drowsiness. In this regard, the electroencephalogram (EEG) signal provides valuable information of brain physiology for detection of drowsiness. EEG signals exhibit non-stationary nature which is tough to explore by prior defined and fixed number of basis functions. In this paper, an adaptive Hermite decomposition is proposed for the detection of drowsiness EEG signals. In the proposed decomposition, Hermite functions are used as basis functions, which are adaptively selected for each EEG signal by evolutionary optimization algorithms (EOAs). The mean square error of decomposition is proposed as an objective function to EOAs. The minimum decomposition error provided artificial bee colony EOA is considered to Hermite coefficients-based feature extraction for EEG signals. The extracted features are tested with the extreme learning machine (ELM), decision tree, k-nearest neighbor, least squares support vector machine, artificial neural network, and naive Bayes for detection of alertness and drowsiness EEG signals. The proposed method with ELM classifier obtained 95.45% and 87.92% detection rates for alertness and drowsiness states, respectively, which are better as compared to other existing methods.

关键词： Drowsiness detection electroencephalogram (EEG) signal Hermite functions evolutionary optimization algorithms

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