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Chaos enhanced intelligent optimization-based novel deception detection system

CHAOS SOLITONS & FRACTALS 2023年 166卷

作者： Bingol, Harun Alatas, Bilal Malatya Turgut Ozal Univ Dept Software Engn TR-44200 Malatya Turkey Firat Univ Dept Software Engn TR-23119 Elazig Turkey

Today, with the developing technology, access methods to information have also changed. Internet blogs and news sites, social media, etc. replace the traditional information access tools such as TV, radio, newspaper, and magazines. Cheaper and faster access than traditional methods and easy access from anywhere where the internet is available are the main factors in changing the access method to information. Besides, information spreads rapidly on the internet without proving the accuracy. There may be many reasons for the distribution of misinformation in this way, such as commercial, political, and economic. Information with fake content and deception purposes negatively affects both the person and the society. It is extremely important to detect deceptive information in textual data and researchers continue to propose new models in order to obtain efficient results in terms of many metrics. This paper proposes a new approach for deception detection problems. The deception detection problem was considered an optimization problem for the first time. Optics Inspired Opti-mization (OIO), Grey Wolf optimization (GWO), and Chaos Based Optics Inspired optimization (CBOIOs) are adapted and modeled for the first time in the deception detection problem. Deception detection models proposed in this article include data preprocessing, adapting optimization methods, and testing stages. An experimental evaluation and comparison of the proposed model and seven supervised machine learning algorithms are per-formed within two different data sets depending on four evaluation metrics (Accuracy, Recall, Precision, and F -Measure). Results show that CBOIOs are more effective than OIO, GWO, and other machine learning algorithms for this problem.

关键词： Deception detection intelligent optimization algorithms Chaos Machine learning

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Analysis of Credit Risk Assessment Model for Financial Data Based on intelligent optimization Algorithm

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Procedia Computer Science 2023年 228卷 421-428页

作者： Xiaolan Qu Yunnan University of Finance and Economics Kunming 650221 Yunnan China

With the rapid development of China's social economy, the financial industry accounts for an increasing proportion of the national economy, and the financial industry is one of the key factors of a country's modernization level and comprehensive national strength. Therefore, how to effectively evaluate financial institutions is particularly important. intelligent optimization algorithms can provide investors with more accurate, timely and comprehensive information, which can help them make accurate investment decisions, and this can also help improve the financial institutions' ability to control and manage risks in the financial market. Based on the analysis of intelligent optimization algorithm, this paper constructs a set of effective assessment model for financial data credit risk, which can reduce the default rate of financial institutions through the assessment of financial data credit risk, thus improving the efficiency of capital use and thus promoting the healthy development of China's financial industry.

关键词： intelligent optimization algorithms Financial Data Credit Risk Evaluation Model Analysis

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A fast fault location method based on detecting the minimum number of phasor measurement units using a novel adaptive binary differential evolution optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第11期25卷 2933-2947页

作者： Ghaffarzadeh, Navid Parpaei, Mohammad Tayar, Mirza Zuhaib Imam Khomeini Int Univ Tech & Engn Fac Qazvin Iran

This paper proposes a novel adaptive binary differential evolution optimization algorithm based on optimal placement of phasor measurement units (PMUs) and an intelligent strategy for fault location in power systems. The novelties of the proposed adaptive binary differential evolution optimization algorithm are that the proposed approach presents two new factors, namely, "weight factor" and "reduction factor", in order to improve convergence speed and enhance the capability of the novel modified binary differential evolution optimization algorithm for detecting optimal placement of PMUs in large-scale problems. To investigate the capability of the proposed approach, it is applied to several IEEE test systems (IEEE 14 bus, IEEE 24 bus, IEEE 30 bus, IEEE 39 bus, and IEEE 57 bus test systems). In addition, a comparison with conventional optimization algorithms has been carried out. Then, according to the PMUs data, an intelligent fault location strategy will be proposed. The main advantages of the proposed intelligent fault location strategy are higher accuracy and speed than conventional fault location approaches. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： binary differential evolution optimal PMU placement fault location intelligent optimization algorithms

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Review: Multi-objective optimization methods and application in energy saving

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ENERGY 2017年 125卷 681-704页

作者： Cui, Yunfei Geng, Zhiqiang Zhu, Qunxiong Han, Yongming Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China

Multi-objective optimization problems are difficult to solve in that the optimized objectives are usually conflicting with each other. It is usually hard to find an optimal solution that satisfies all objectives from the mathematical point of view. Unlike analytical methods and classical numerical methods, which require strict mathematical calculation or defined initial search values, intelligent optimization algorithms are heuristic algorithms able to find global optimal solutions. In this paper, we make a brief introduction of multi-objective optimization problems and some state-of-the-art intelligent algorithms. In order to get the final optimal solution in the real-world multi-objective optimization problems, trade-off methods including a priori methods, interactive methods, Pareto-dominated methods and new dominance methods are utilized. Moreover, we give a review of multi-objective optimization methods application in the environmental protection fields, for optimization objectives of energy saving, emissions reduction and cost reduction, etc. At last, a whole summary about current difficulties existed in the multi-objective optimization problem is given out, serving as suggestions or guidance for future researches. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization intelligent optimization algorithms Trade-off solution Energy saving Emissions reduction

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Development of a hydrate formation prediction model for sub-sea pipeline

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PETROLEUM SCIENCE AND TECHNOLOGY 2017年第5期35卷 443-450页

作者： Abbasi, Aijaz Hashim, Fakhruldin Mohd Univ Teknol PETRONAS Dept Mech Engn Seri Iskandar Malaysia

In this research work, a novel model is developed to the hydrate formation pressure and hydrate formation temperature for a single component of methane (CH4) gas. This research model holds at a temperature and pressure of real-time data for pure water, methane, and other mixtures of gases, respectively. Furthermore, the modelling and statistical analysis conducted in this research were divided into three major segments;the assessment of the computability of the mathematical models, the performance of the proposed optimization techniques, and the comparison of the proposed techniques with real-time data of gas pipeline. Results showed that the improved optimization algorithms are in admirable compliance with the real-time data. These correlations are providing the capability to predict gas-hydrate-forming conditions for a wide range of hydrate formation data.

关键词： Deepwater pipeline empirical equations hydrate formation prediction intelligent optimization algorithms statistical analysis

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A Comparative Study of Parameter Identification Fusion Methods With a High-Precision Supercapacitor Model for Electric Vehicles

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JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE 2024年第1期21卷 011013页

作者： Wang, Chun Shen, Chao Tang, Aihua Liu, Pu Sichuan Univ Sci & Engn Sch Mech Engn 519Huixing Rd Zigong 643000 Sichuan Peoples R China Chongqing Univ Technol Sch Vehicle Engn 69Hongguang Rd Chongqing 400054 Peoples R China

To ensure energy storage system operates reliably for electric vehicles, it is vital to accurately identify supercapacitor model parameters in applications. In recent years, most of the algorithms focus on lithium-ion batteries, but few are reported to be used for supercapacitors. To fill this research gap, many algorithms and corresponding fusion methods for supercapacitors are designed in this study. First, seven popular intelligent optimization algorithms are selected to identify the supercapacitor model parameters, and the identification results are discussed in detail. Then, considering a single algorithm cannot guarantee convergence to all global optimal model parameters over state-of-charge (SOC) intervals, five fusion methods for supercapacitor parameter identification have been developed by combining information fusion technology. Finally, voltage errors are statistically analyzed to validate the effectiveness of the five proposed fusion methods. The results show that the five fusion methods can further enhance the global prediction performance of the supercapacitor model, particularly the reverse search-based parameter identification fusion (PIF-RS) method, which has better accuracy and reliability with respect to the maximum (Max) error, mean error, and root mean square (RMS) error decreasing by at least 10.1191%, 17.0024%, and 17.0989%, respectively.

关键词： supercapacitor model parameter identification intelligent optimization algorithms fusion methods electrochemical capacitors electrochemical storage

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Evolution of Controllers Under a Generalized Structure Encoding/Decoding Scheme With Application to Magnetic Levitation System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第9期69卷 9655-9666页

作者： Xin, Bin Wang, Yipeng Xue, Wenchao Cai, Tao Fan, Zhun Zhan, Jiaoyang Chen, Jie Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Shantou Univ Minist Educ Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China

Evolutionary search has been widely implemented for the adjustment of controllers' parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimize both structures and parameters simultaneously in this article. A controller is made up of a combination of some basic controller components and relevant parameters. The design of controllers can be transformed into an optimization problem involving the structure (represented by discrete vectors) and parameters (represented by real numbers). A generalized structure encoding/decoding scheme is developed. Guided by the performance indicators, intelligent algorithms for both combinatorial and numerical optimization are employed to iteratively and cooperatively evolve the controller structure and parameters, respectively. In order to effectively reduce some redundant or infeasible solutions, a set of generation rules for the controller structure are put forward, which also ensures the feasibility of the structure. Furthermore, this method is applied to a magnetic levitation ball system with nonlinear dynamics and external disturbance. Both simulation and experiment results demonstrate the effectiveness and practicability of the proposed method.

关键词： optimization Design methodology Control systems Tuning Magnetic levitation Signal processing Periodic structures Automatic design of controllers disturbance-rejection intelligent optimization algorithms magnetic levitation system (MLS) mixed-variable optimization

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Particle swarm optimization algorithm for dynamic synchronization of smart grid

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第2期44卷 3940-3959页

作者： Zulueta, Asier Azurmendi, Iker Rey, Nerea Zulueta, Ekaitz Fernandez-Gamiz, Unai Univ Basque Country Nucl Engn & Fluid Mech Dept Vitoria Spain Univ Basque Country Syst Engn & Automat Control Dept Vitoria Spain

In recent decades, traditional energy generation and consumption have undergone a transformation to cleaner energy sources. The transition to a sustainable energy system based on efficiency and renewable energies will thus require the replacement of a complex and heavily implemented energy system with one based on concepts such as microgrids. A microgrid is a controlled small-scale power system and the elements that make up it are: distributed generation systems;energy storage systems;load management techniques;monitoring systems, etc. Consequently, since ac devices are used, synchronization criteria must be satisfied to switch operation between them. The synchronization criteria consist of making the values of the phase-angle difference, slip frequency, and voltage difference as small as possible. This is very important to ensure the correct operation of the grid. In the other hand, the use of Particle Swarm optimization (PSO) algorithm has been increasing, because of their simplicity and efficiency in engineering optimization problems. Therefore, this paper proposes an active synchronizing control scheme that allows to synchronize a generator set to the grid and a particle swarm optimization algorithm, which makes the synchronizing control as efficient as possible. With the implemented control and with the help of the synchronization algorithm, improvements in the reduction of settling time (from 9 seconds to 3 seconds) are obtained in comparison to other controls in the literature.

关键词： Smart grid microgrid synchronization genset Particle swarm optimization (PSO) genetic algorithms intelligent optimization algorithms

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An optimization algorithm applied to the class integration and test order problem

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SOFT COMPUTING 2019年第12期23卷 4239-4253页

作者： Zhang, Yanmei Jiang, Shujuan Wang, Xingya Chen, Ruoyu Zhang, Miao China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China

In recent years, many intelligent optimization algorithms are applied to the class integration and test order problem and it has been proved that they can efficiently solve this problem. In this paper, the particle swarm optimization algorithm is applied to the class integration and test order problem. First, the initial population (test orders) is generated randomly and each test order is taken as a particle. Here, we map particles to one-dimensional space;then, a particle (an integration test order) can be represented by a position in the one-dimensional space;finally, the optimal particle (integration test order) is selected by particle swarm optimization algorithm. Also, whether the precedence table of dependency relations is introduced as the constraints in evolution can impact the effect of particle swarm optimization approach is validated through experiment. The experimental results show that the particle swarm optimization algorithm is encouraging in solving class integration and test order problem.

关键词： Particle swarm optimization intelligent optimization algorithms Integration testing Object-oriented software

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Water resource management based on wireless virtualization in wastewater reclamation and utilization in dryland botanical garden

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WATER SUPPLY 2023年第8期23卷 3140-3155页

作者： Chen, Guanyu Yulin Normal Univ Coll Polit & law Yulin 537000 Guangxi Peoples R China

The research on the management and distribution of renewable water resources mostly adopts the method of combining the intelligent optimization algorithm and the target planning model. In this paper, four water distribution users are used as categories, and the problem of renewable water resources allocation is transformed into a classification problem, which is included in the category of wireless virtualization processing problems, and wireless virtualization is selected to support the allocation of renewable water resources. Experiments show that the water shortage of the water distribution scheme obtained by the method in this paper is lower than that of the optimal water distribution scheme of the traditional intelligent optimization algorithm. From the water distribution cost and water shortage of a single user, it can also show that the water distribution effect of the method in this paper is excellent. The water distribution effect of traditional intelligent optimization algorithm (P < 0.05). It shows that the research on the management and distribution method of renewable water resources based on wireless virtualization not only provides new ideas for dealing with configuration problems but also has important significance for alleviating the shortage of water resources in our country and the management and utilization of renewable water resources.

关键词： intelligent optimization algorithms renewable water resource management shortage of water resources wireless virtualization

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