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A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network

NEURAL COMPUTING & APPLICATIONS 2016年第8期27卷 2551-2565页

作者： Beheshti, Zahra Firouzi, Morteza Shamsuddin, Siti Mariyam Zibarzani, Masoumeh Yusop, Zulkifli Univ Teknol Malaysia UTM Big Data Ctr Skudai 81310 Johor Malaysia Univ Teknol Malaysia Ctr Environm Sustainabil & Water Secur IPASA Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Comp Dept Informat Syst Skudai 81310 Johor Malaysia

Artificial neural networks (ANNs) are being used increasingly to forecast rainfall. In this study, several meta-heuristic algorithms are applied to train an ANN in order to improve the accuracy of rainfall forecasting. Centripetal accelerated particle swarm optimization (CAPSO), a gravitational search algorithm and an imperialist competitive algorithm train a multilayer perceptron (MLP) network as a feed-forward ANN for rainfall forecasting in Johor State, Malaysia. They are employed to forecast the average monthly rainfall in the next 5 and 10 years using the two modes of original (without data preprocessing) and data preprocessing with singular spectrum analysis. Additionally, for each month, the average monthly rainfall during the last 5 years is computed and a month with less rainfall than the average is classified as 0 (light rainfall month), otherwise as 1 (heavy rainfall month). The attributes used in the classification can be applied to forecast the monthly rainfall. The proposed methods integrate the accuracy and structure of ANN simultaneously. The result showed that the hybrid learning of MLP with the CAPSO algorithm provided higher rainfall forecasting accuracy, lower error and higher classification accuracy. One of the main advantages of CAPSO compared with the other algorithms to train MLP includes the following: The algorithm has no need to tune any algorithmic parameter and it shows good performance on unseen data (testing data).

关键词： Rainfall Forecasting Classification Artificial neural network Centripetal accelerated particle swarm optimization gravitational search algorithm Imperialist competitive algorithm Data processing

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Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic algorithms

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INFORMATION 2017年第1期8卷 31页

作者： Chen, Jeng-Fung Lo, Shih-Kuei Do, Quang Hung Feng Chia Univ Dept Ind Engn & Syst Management Taichung 40724 Taiwan Univ Transport Technol Fac Informat Technol Hanoi 100000 Vietnam

Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including gravitational search algorithm (GSA) and Cuckoo Optimization algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.

关键词： Cuckoo Optimization algorithm gravitational search algorithm neural networks forecasting monthly electricity demand

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CLASSIFICATION OF SONAR DATA SET USING NEURAL NETWORK TRAINED BY GRAY WOLF OPTIMIZATION

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NEURAL NETWORK WORLD 2016年第4期26卷 393-415页

作者： Mosavi, M. R. Khishe, M. Ghamgosar, A. Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

Multi-Layer Perceptron Neural Networks (MLP NNs) are the commonly used NNs for target classification. They purposes not only in simulated environments, but also in actual situations. Training such NNs has significant importance in a way that many researchers have been attracted to this field recently. Conventional gradient descent and recursive method has long been used to train NNs. Improper classification accuracy, slow convergence speed and trapping in local minimums are disadvantages of the traditional methods. In order to overcome these issues, in recent years heuristic and meta-heuristic algorithms are widely used. This paper uses Gray Wolf Optimization (GWO) algorithm for training the NN. This algorithm is inspired by lifestyle and hunting method of GWs. GWO has a superior ability to solve the high-dimension problems, so we try to classify the Sonar dataset using this algorithm. To test the proposed method, this algorithm is compared to Particle Swarm Optimization (PSO) algorithm, gravitational search algorithm (GSA) and the hybrid algorithm (i.e. PSOGSA) using three sets of data. Measured metrics are convergence speed, the possibility of trapping in local minimum and classification accuracy. The results show that the proposed algorithm in most cases provides better or comparable performance compared to the other mentioned algorithms.

关键词： classification sonar Multi-Layer Perceptron Neural Network Grey Wolf Optimization Particle Swarm Optimization gravitational search algorithm

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BGSA: binary gravitational search algorithm

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NATURAL COMPUTING 2010年第3期9卷 727-745页

作者： Rashedi, Esmat Nezamabadi-pour, Hossein Saryazdi, Saeid Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.

关键词： gravitational search algorithm Heuristic search algorithms Law of gravity Optimization

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Research on the Evaluation of Teaching Quality Based on CGSAB

Research on the Evaluation of Teaching Quality Based on CGSA...

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The 1st International Conference on Education, Management Science and Economics (ICEMSE 2016)

作者： Jiatang Cheng Yan Xiong The Engineering College Honghe University

Teaching quality evaluation is an important work in teaching *** order to improve the accuracy of teaching quality evaluation,according to the evaluation data at a certain university,an evaluation model based on BP neural network optimized by gravitational search algorithm(GSABP) is *** the GSA algorithm is easy to fall into the local optimal,the ergodicity of chaotic sequence is used to generate the initial population of GSA,and then the chaotic gravitational search algorithm(CGSA) is *** experimental results show that,compared with BP neural network and GSABP algorithm,the model using CGSABP has high credibility and strong generalization ability,which provides a feasible method for the accurate evaluation of teaching quality.

关键词： teaching quality evaluation gravitational search algorithm chaotic series BP neural network

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Design and Implementation of Fractional Order Integrator with Reduced Hardware 3

Design and Implementation of Fractional Order Integrator wit...

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3rd International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Barsainya, Richa aggarwal, Meenakshi Rawat, Tarun Kumar Netaji Subhas Inst Technol Div Elect & Commun Engn New Delhi 110078 India

ISBN: (纸本)9781467391979

This paper proposes a novel methodology for design and multiplierless implementation of fractional order integrator (FOI) based on lattice wave digital filter using gravitational search algorithm (GSA). The FOI design problem is formulated as an optimization problem using the transfer function of lattice wave digital filter (LWDF). The realization of FOI using LWDF structure increases the accuracy of design due to its excellent properties and also it requires only N number of multiplier coefficients for Nth order FOI. Furthermore, the minimum hardware and low power dissipation of FOI, which are the main concern of efficient implementation is also presented in this paper. The design level area optimization of the proposed lattice wave digital FOI (LWDFOI) is done by converting constant multipliers into shifts and adds using canonical signed digit code (CSDC) technique. The proposed LWDFOI is implemented and successfully tested on Xilinx Spartan XC3s200-4ft256 field programmable gate array (FPGA) device. Simulation results are accomplished to show the comparison of the proposed LWDFOI with recent literature. The performance of the proposed LWDFOI is also evaluated in terms of speed, i.e. maximum frequency, area (number of slices) and power consumption. The proposed LWDFOI have been found to outperform the existing ones reasonably well in low and mid frequency range.

关键词： Fractional order integrator lattice wave digital filter gravitational search algorithm Canonic signed digit code Carry save adder

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Pairwise Sequence Alignment of Biological Database using Soft Computing Approach 5

Pairwise Sequence Alignment of Biological Database using Sof...

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5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

作者： Kaur, Harleen Chand, Lal Punjabi Univ Dept Comp Engn Patiala Punjab India

ISBN: (纸本)9781509014897

A Biological sequence alignment is the cornerstone of bioinformatics. The sequence alignment is carried out to extrapolate the evolutionary relationship among the living species which can help to characterize the functionality of unidentified sequences. The overall objective of pairwise alignment is to determine the uttermost alikeness among residues. Dynamic programming (DP) is the most popular technique for pairwise alignment but the downside of this approach is the proliferation of space and time complexity while handling considerable biological sequence. Various soft computing algorithms such as GA, PSO, ACO, GSA and many more, are in trend from past few years. These algorithms are inspired by natural evolution which helps to find near optimal solutions for optimization problem in reasonable amount of time. In this paper pairwise sequence alignment of protein is done using hybrid approach of soft computing algorithms which subsume gravitational search algorithm (GSA) and Particle Swarm Optimization (PSO). The enactment of this hybrid approach is examined by comparing the simulation results with the DP based algorithms.

关键词： Bioinformatics Sequence Alignment gravitational search algorithm Particle Swarm Optimization

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Comparison of Hybrid HOD-GSA, HOD and PSO for the Tuning of Extended Kalman Filter 5

Comparison of Hybrid HOD-GSA, HOD and PSO for the Tuning of ...

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5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

作者： Kaur, Navreet Kaur, Amanpreet Chandigarh Engn Coll Dept Informat Technol Landran Punjab India

ISBN: (纸本)9781509014897

State estimation is the basic problem in every area of science and engineering. For the state estimation problem, Kalman filter is the generally used technique when the system is linear. Various derivatives of Kalman filter are proposed earlier for non-linear systems, i.e. Extended Kalman Filter and Unscented Kalman Filter. But, there is a need of tuning in these estimation techniques and therefore the tuning of process and measurement noise covariance matrices is required. Earlier, the different optimization techniques are used for the tuning of Extended Kalman Filter like Genetic algorithm, Human Opinion Dynamics based Optimization and Particle Swarm Optimization. In this paper, Hybrid HOD-GSA has been proposed for the tuning of Extended Kalman Filter and also to solve the trapping problem of GSA. Then, the results taken from Hybrid HOD-GSA are compared with the results taken from Human Opinion Dynamics and Particle Swarm Optimization in terms of accuracy, error rate, standard deviation and convergence.

关键词： Extended Kalman Filter gravitational search algorithm Hybrid HOD-GSA optimization state estimation

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A GSA- based Method in Human Identification Using Finger Vein Patterns 1

A GSA- based Method in Human Identification Using Finger Vei...

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1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Saadat, Fateme Nasri, Mehdi Islamic Azad Univ Dept Elect Engn Sirjan Sci & Res Branch Sirjan Iran Islamic Azad Univ Dept Elect Engn Sirjan Branch Sirjan Iran Islamic Azad Univ Young Researchers & Elite Club Khomeinishahr Branch Khomeinishahr Iran

ISBN: (纸本)9781467387378

Biometric and multibiometric science play an important role in human authentication systems nowadays. Finger vein pattern is one of the most reliable and secure biometrics due to its invariability and safety from stealth. In this paper, a heuristic method is proposed for score level fusion of three different finger vein's patterns. In the proposed multibiometric system, gravitational search algorithm is used to tune the weights of sum fusion strategy. The performance of the method is evaluated using FAR, FRR and EER criteria. Experimental results confirm the superiority of the proposed method over classic fusion strategy in human identification.

关键词： biometric human identification finger vein gravitational search algorithm

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Multi-objective Fitness-proportional Attraction Approach with Weights

Multi-objective Fitness-proportional Attraction Approach wit...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Laack, Patrick Zille, Heiner Mostaghim, Sanaz Otto Von Guericke Univ Inst Intelligent Cooperat Syst Magdeburg Germany

ISBN: (纸本)9781509006229

This paper proposes a new multi-objective optimization algorithm that is called Fitness-Proportional Attraction with Weights (F-PAW). In contrast to many other approaches, this work was inspired by physics rather than biology. It is based on concepts from several methods, including the attraction principle of gravity from the gravitational search algorithm (GSA), the weight sum approach from Multi-Objective Evolutionary algorithm based on Decomposition (MOEA/D) as well as particle swarm optimization methods. These and other algorithms that were providing inspiration are introduced during the text and their techniques are investigated for the use in F-PAW. The performance of F-PAW is compared to three well-known multi-objective algorithms through an experiment on 16 common test problems taken from the WFG and DTLZ benchmarks. The results indicate two conclusions. On the one side, the proposed approach with the weight sum obtains a good diversity. On the other side, the currently implemented local search is lacking reliability and speed.

关键词： Multi-objective optimization gravitational search algorithm Particle Swarm Optimization Evolutionary algorithms

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