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Optimization of Text Feature Selection Process Based on Advanced Searching for News Classification

INTERNATIONAL JOURNAL OF SWARM intelligence RESEARCH 2020年第4期11卷 1-23页

作者： Kyaw, Khin Sandar Limsiroratana, Somchai Prince Songkla Univ Dept Comp Engn Hat Yai Thailand

Nowadays, the culture for accessing news around the world is changed from paper format to electronic and the rate of publication for newspapers and magazines on websites have increased dramatically. Therefore, the feature selection process from high-dimensional text feature set for an automatic news classification model is becoming the top challenge because irrelevant features can degrade the accuracy with high cost computation time for classification model. In this article, six-advanced search policies based on evolutionary, swarm intelligence, nature-inspired intelligence are observed for achieving the global optimal feature subset for optimal accuracy in news classification problem. According to the experimental results, the advanced search schemes that can provide flexibility in integrating classifier in accordance with its objective function such as optimal classification performance by adjusting the rate of modification parameters for the testing data.

关键词： computation intelligence Correlation-Based Feature Selection Feature Extraction Fitness Function J48 Classifier Meta-Heuristic Optimization Approach Swarm intelligence Text Mining

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A modified Whale Optimization Algorithm based on Chaos Initialization and Regulation Operation 38

A modified Whale Optimization Algorithm based on Chaos Initi...

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38th Chinese Control Conference (CCC)

作者： Jiang Ruiye Chao Tao Wang Songyan Yang Ming Harbin Inst Technol Harbin 150001 Peoples R China

ISBN: (纸本)9789881563972

A rising meta-heuristic swarm intelligent computation algorithm, the Whale Optimization Algorithm (WOA), which has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. To accelerate the convergence rate of WOA, this paper has proposed a new improved whale optimization algorithm, called Chaos Regulation Whale Optimization Algorithm (CRWOA). The chaotic map generator composed with three kinds of chaotic maps has been used to initialize the population. Furthermore, Cauchy distribution and Gauss distribution are introduced to regulate the position in each iteration. To analyze the superiority of CRWOA further, ten single-objective benchmark test functions are chosen. Through the comparison and analysis of the optimal results, CRWOA is demonstrated superior in general. It has faster convergence speed and better results than the other algorithms when less iteration number, less population quantity and wider ranges of variables are set.

关键词： Benchmark functions Chaotic map computation intelligence Continuous probability distribution Whale optimization algorithm

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Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 163-181页

作者： Ghanem, Waheed A. H. M. Jantan, Aman Univ Sains Malaysia Sch Comp Sci George Town Malaysia Univ Aden Fac Engn Aden Yemen Univ Aden Fac Educ Saber Aden Yemen

The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to address the issues of trapping in local optimal solution, slow convergence, and low accuracy in numerical optimization problems. This article introduces a new hybrid approach by modifying the butterfly adjusting operator in MBO algorithm and uses that as a mutation operator to replace employee phase of the ABC algorithm. The new algorithm is called Hybrid ABC/MBO (HAM). The HAM algorithm is basically employed to boost the exploration versus exploitation balance of the original algorithms, by increasing the diversity of the ABC search process using a modified operator from MBO algorithm. The resultant design contains three components: The first and third component implements global search, while the second one performs local search. The proposed algorithm was evaluated using 13 benchmark functions and compared with the performance of nine metaheuristic methods from swarm intelligence and evolutionary computing: ABC, MBO, ACO, PSO, GA, DE, ES, PBIL, and STUDGA. The experimental results show that the HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed. The proposed HAM algorithm is a promising metaheuristic technique to be added to the repertory of optimization techniques at the disposal of researchers. The next step is to look into application fields for HAM.

关键词： Artificial bee colony algorithm Monarch butterfly optimization algorithm Global optimization problem computation intelligence

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A Novel Hybrid Artificial Bee Colony with Monarch Butterfly Optimization for Global Optimization Problems 1

A Novel Hybrid Artificial Bee Colony with Monarch Butterfly ...

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1st International Conference on Computer Science and Engineering (COMPSE)

作者： Ghanem, Waheed Ali H. M. Jantan, Aman Univ Sains Malaysia Sch Comp Sci Gelugor Penang Malaysia Univ Aden Fac Educ Saber Aden Yemen Univ Aden Fac Engn Aden Yemen Univ Sains Malaysia Sch Comp Sci Gelugor Penang Malaysia

ISBN: (纸本)9783319705422;9783319705415

This article introduces a novel hybrid approach between two of the meta-heuristic algorithms to solve global optimization problems. The proposed hybrid algorithm uses the butterfly adjusting operator in Monarch Butterfly Op-timization (MBO) algorithm as a mutation operator to replace the employee phase of the Artificial Bee Colony (ABC) algorithm. The novel Hybrid ABC/MBO (HAM) algorithm addresses the issues of trapping in local optimal solutions, slow convergence, and low precision by improving the balance be-tween the characteristics of exploration and exploitation. The proposed HAM al-gorithm is validated on eight benchmark functions, and is compared with ABC and MBO algorithms. The experimental results show that the HAM algorithm is clearly superior to both the standard ABC and MBO algorithms. © Springer-Verlag Berlin Heidelberg 2017.

关键词： Artificial bee colony algorithm computation intelligence Global optimization problem Monarch butterfly optimization algorithm

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A modified Whale Optimization Algorithm based on Chaos Initialization and Regulation Operation

A modified Whale Optimization Algorithm based on Chaos Initi...

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第三十八届中国控制会议

作者： JIANG Ruiye CHAO Tao WANG Songyan YANG Ming Harbin Institute of Technology

A rising meta-heuristic swarm intelligent computation algorithm, the Whale Optimization Algorithm（WOA）, which has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. To accelerate the convergence rate of WOA, this paper has proposed a new improved whale optimization algorithm, called Chaos Regulation Whale Optimization Algorithm（CRWOA）. The chaotic map generator composed with three kinds of chaotic maps has been used to initialize the population. Furthermore, Cauchy distribution and Gauss distribution are introduced to regulate the position in each iteration. To analyze the superiority of CRWOA further, ten single-objective benchmark test functions are chosen. Through the comparison and analysis of the optimal results, CRWOA is demonstrated superior in general. It has faster convergence speed and better results than the other algorithms when less iteration number, less population quantity and wider ranges of variables are set.

关键词： Benchmark functions Chaotic map computation intelligence Continuous probability distribution Whale optimization algorithm

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Smart Experts for Network State Estimation

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2016年第3期13卷 622-635页

作者： Edalat, Yalda Ahn, Jong-Suk Obraczka, Katia Univ Calif Santa Cruz Dept Comp Engn Santa Cruz CA 95064 USA Dongguk Univ Dept Comp Engn Seoul 04620 South Korea

Several network protocols, services, and applications adjust their operation dynamically based on current network conditions. Consequently, keeping accurate estimates of the network and its performance as it fluctuates over time is critical. For example, both TCP and IEEE 802.11 periodically adapt some of their key operating parameters, namely, the retransmission timeout and the contention window size based on the average round trip time and the number of collisions, respectively. In this paper, we present a novel mechanism to estimate "nearfuture" network performance based on past network conditions. We call our approach to network performance estimation as smart experts for network state estimation (SENSE). SENSE uses a simple, yet effective, algorithm combining a machine learning method known as fixed-share with exponentially weighted moving average (EWMA). SENSE also introduces novel techniques that improve the predictability of the fixed-share framework without increasing computational complexity. SENSE is thus able to respond to network dynamics at different time scales, i. e., long-and medium-term fluctuations as well as short-lived variations. We evaluate SENSE's performance using synthetic and real datasets. Our experimental results show that, when compared to fixed-share and EWMA, SENSE yields higher estimation accuracy for all datasets due to its ability to more closely track data fluctuations.

关键词： Machine learning computation intelligence network performance network state estimation

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Pathway to Cost-Efficient State Estimation of Future Distribution Networks

Pathway to Cost-Efficient State Estimation of Future Distrib...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Liao, Huilian Milanovic, Jovica V. Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9781509041688

This paper provides a detailed overview of the techniques and applications related to distribution system state estimation (DSSE), together with the classification of various types of state estimation. The paper also provides the state-of-art techniques applied in DSSE including forecasted-aided state estimation, close-loop DSSE methods, the application of computation intelligence in DSSE and the use of smart meters and phasor measurement units in load estimation. As more and more active elements and functionalities will be integrated in future distribution network, e.g. demand-side management, the increased penetration of decentralized generation and dynamically controlled storage/devices, DSSE will be one of the critical functionalities for secure operation of future distribution networks. As a pathway to cost-efficient DSSE for future distribution networks, this paper addresses the characteristics of future distribution grids that will affect DSSE, and also discusses the techniques and data that can be used to better understand/model the network behaviors and load profiles in order to ultimately improve the accuracy of DSSE.

关键词： State estimation smart meters phasor measurement units computation intelligence load estimation

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Significance of Computional intelligence Method in Computer Networks

Significance of Computional Intelligence Method in Computer ...

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3rd International Conference on Network and System Security

作者： Lal, Reginald Chiou, Andrew CQUniv Australia Sch Comp Sci Rockhampton Qld 4701 Australia

ISBN: (纸本)9781424450879

The increase in network traffic has lead to the concept of congestion in computer networks. The problem of network congestion control remains a major issue in today's computer networks. Despite various methods and algorithms that has been proposed, however due to the dynamic nature of computer networks, no universal control method has been widely accepted. This paper reviews various conventional methods proposed to avoid network congestion and compares it to computational intelligence (CI) method. A demonstrative simulation is setup to measure congestion in a LAN environment. In addition, the methods of computational intelligence and its efficiency in network congestion management have been addressed in-depth.

关键词： computation intelligence congestion control computer networks conventional methods fuzzy logic

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Trends in computational intelligence in nuclear engineering

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PROGRESS IN NUCLEAR ENERGY 2005年第3-4期46卷 167-175页

作者： Hines, JW Uhrig, RE Univ Tennessee Dept Nucl Engn Knoxville TN 37996 USA

Several changes to the focus of computational intelligence in Nuclear Engineering have occurred in the past few years. With earlier activities focusing on the development of condition monitoring and diagnostic techniques for current nuclear power plants, recent activities have focused on the implementation of those methods and the development of methods for next generation plants and space reactors. These advanced techniques are expected to become increasingly important as current generation nuclear power plants have their licenses extended to 60 years and next generation reactors are being designed to operate for extended fuel cycles (up to 25 years), with less operator oversight, and especially for nuclear plants operating in severe environments such as space or ice-bound locations.

关键词： computation intelligence reactor noise analysis on-line monitoring efficiency improvement intelligent agents

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Application of artificial intelligence approach in the analyses of train derailment accidents

Application of artificial intelligence approach in the analy...

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3rd International Symposium on Safety Science and Technology (2002 ISSST)

作者： Zhai, WM Zhang, Z Wang, Q SW Jiaotong Univ Train & Track Res Inst Chengdu 610031 Peoples R China

ISBN: (纸本)703010787X

Train derailment is an important factor to influence train-running safety. In recent 10 years, derailment accounts for about 70% of railway heavy and major accidents in China. Because of the complexity of derailment and difficulty of its study, the causes of derailment are usually difficult to be found out. The traditional analytical methods are simplified, which are not able to consider the mutual influence of the factors and therefore cannot explain the essence of derailment. In this paper, a new method, intelligent synthesis is employed to analyze the essential reasons of derail accidents. Some new techniques of artificial intelligence are applied in the analysis of derailment, such as compute intelligence, fuzzy logic, etc. A basic structure of the intelligent derailment analysis support system is established, and a knowledge-ware for analyzing derailment is designed. Finally an example of actual derailment accident is analyzed by using the expert system based on the fuzzy logic method.

关键词： derailment railway safety artificial intelligence computation intelligence fuzzy logic

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