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imperial competitive algorithm with policy learning for the traveling salesman problem

SOFT COMPUTING 2017年第7期21卷 1863-1875页

作者： Chen, Meng-Hui Chen, Shih-Hsin Chang, Pei-Chann Yuan Ze Univ Dept Informat Management Innovat Ctr Big Data & Digital Convergence Chungli Taiwan Cheng Shiu Univ Dept Informat Management Kaohsiung Taiwan

The traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems. In this paper, we present the new idea of combining the imperial competitive algorithm with a policy-learning function for solving the TSP problems. All offspring of each country are defined as representing feasible solutions for the TSP. All countries can grow increasingly strong by learning the effective policies of strong countries. Weak countries will generate increasingly excellent offspring by learning the policies of strong countries while retaining the characteristics of their own country. Imitating these policies will enable the weak countries to produce improved offspring;the solutions generated will, therefore, acquire a favorable scheme while maintaining diversity. Finally, experimental results for TSP instances from the TSP library have shown that our proposed algorithm can determine the salesman's tour with more effective performance levels than other known methods.

关键词： Traveling salesman problem imperial competitive algorithm Combinatorial optimization problems Artificial chromosomes Genetic algorithm

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Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

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JOURNAL OF APPLIED GEOPHYSICS 2014年 108卷 61-68页

作者： Ansari, Hamid Reza Petr Univ Technol Dept Petr Explorat Engn Abadan Fac Petr Engn Northern Bowarde Abadan Iran

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), epsilon-support vector regression (epsilon-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Neural networks Neuro-fuzzy Support vector regression Power law committee machine imperial competitive algorithm Colored inversion

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An examination energy price policies in Iran for optimal configuration of HGPS based on the imperial competitive algorithm

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2016年第11期11卷 1080-1088页

作者： Gharavi, H. Aalami, H. Gharoei, H. Amirkabir Univ Technol Dept Elect Engn Energy Res Ctr Tehran Polytech 424 Hafez Ave Tehran 158254413 Iran Emam Hosseyn Univ Dept Elect Engn Tehran Iran Power & Water Univ Technol Dept Elect Engn Tehran Iran

The pollution produced by power purchased from electrical network and the extensive effort of Iranian government toward the elimination of energy subsides are the encouraging factors in Iran for employing hybrid green power system (HGPS) to produce power. Therefore, in this study, the effect of current and future energy prices as well as energy budget on optimal design of an HGPS is studied. Three cases are considered to examine the optimization problem thoroughly: (i) autonomous HGPS;(ii) nonautonomous HGPS, but HGPS cannot sell electricity to electrical network;and (iii) nonautonomous HGPS and HGPS is allowed to sell electricity to electrical network. The results for Case 2 and Case 3 show that with the increase of energy price from three to five times (0.7 and 0.5), budgets for Cases 2 and 3 (0.6 and 0.2,, respectively) are required to ensure that the design is economical.

关键词： Energy price policy hybrid green power system imperial competitive algorithm

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A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining

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APPLIED SOFT COMPUTING 2020年第0期88卷 106013-000页

作者： Gordan, Meisam Razak, Hashim Abdul Ismail, Zubaidah Ghaedi, Khaled Tan, Zhi Xin Ghayeb, Haider Hamad Univ Malaya Dept Civil Engn StrucHMRSGrp Kuala Lumpur 50603 Malaysia Univ Malaya Dept Civil Engn Kuala Lumpur 50603 Malaysia Queensland Univ Technol Sch Civil Engn & Built Environm Brisbane Qld 4001 Australia

Implementation of data mining (DM) techniques in different areas of civil engineering has recently given very good results. However, application of DM in structural health monitoring (SHM) is not used as much as expected, thus, many challenges are still ahead. Therefore, it seems a vital need is required to develop the applicability of DM in SHM. To this end, the current study attempts to present a DM-based damage detection methodology using modal parameter data, which trained by means of a hybrid artificial neural network-based imperial competitive algorithm (ANN-ICA). Likewise, the hybrid ANN is optimized by a new optimization-based evolutionary algorithm, called ICA, to predict the severity and location of multiple damage cases obtained from experimental modal analysis of intact and damaged slab-on-girder bridge structures. Furthermore, the applicability of DM approach was developed to detect the hidden patterns in vibration data using Cross Industry Standard Process for DM (CRISP-DM) tool. The performance of the model was carried out using comparison of a pre-developed ANN and ANN-ICA model. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Damage identification Data mining imperial competitive algorithm Artificial neural network Cross Industry Standard Process for Data Mining Evolutionary algorithms

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Data mining based damage identification using imperialist competitive algorithm and artificial neural network

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2018年第8期15卷 1-14页

作者： Gordan, Meisam Razak, Hashim Abdul Ismail, Zubaidah Ghaedi, Khaled Univ Malaya Dept Civil Engn StrucHMRSGrp Kuala Lumpur 50603 Malaysia Univ Malaya Dept Civil Engn Kuala Lumpur 50603 Malaysia

Currently, visual inspections for damage identification of structures are broadly used. However, they have two main drawbacks;time limitation and qualified manpower accessibility. Therefore, more precise and quicker technique is required to monitor the condition of structures. To aid the aim, a data mining based damage identification approach can be utilized to solve these drawbacks. In this study, to predict the damage severity of single-point damage scenarios of I-beam structures a data mining based damage identification framework and a hybrid algorithm combining Artificial Neural Network (ANN) and imperial competitive algorithm (ICA), called ICA-ANN method, is proposed. ICA is employed to determine the initial weights of ANN. The efficiency coefficient and mean square error (MSE) are used to evaluate the performance of the ICA-ANN model. Moreover, the proposed model is compared with a pre-developed ANN approach in order to verify the efficiency of the proposed methodology. Based on the obtained results, it is concluded that the ICA-ANN indicates a better performance in detection of damage severity over the ANN method used only.

关键词： Structural health monitoring damage detection data mining artificial neural network imperial competitive algorithm hybrid algorithm

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Partial discharge localisation methodology for power transformers based on improved acoustic propagation route search algorithm

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第8期12卷 1023-1030页

作者： Wang, Yan-Bo Fan, Yu-Hang Qin, Shao-Rui Chang, Ding-Ge Shao, Xian-Jun Mu, Hai-Bao Zhang, Guan-Jun Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China State Grid Anhui Elect Power Res Inst Hefei 230022 Anhui Peoples R China

The localisation of partial discharge (PD) sources using the acoustic emission (AE) technique has attracted increasing research attention. The complicated propagation routes and wave-type conversion in power transformer can induce considerable localisation error. In this paper, the catadioptric phenomenon of AE wave propagation is explained in detail. With the incident angle varying, the wave type of the direct wave in tank wall could convert and the velocity will change accordingly. A transformer model is established in which each node refers to a suspected PD position and a shortest route search algorithm is proposed to calculate the shortest path between two nodes in this model. However, the fastest route is most significant to PD localisation rather than the shortest route when TDOA method is used. As a result, an improved propagation route search (IPRS) algorithm, which can recreate the propagation process and calculate the fastest AE routes, is proposed to localise the PD origin. To verify the feasibility of the IPRS algorithm, localisation experiments were performed in 35 and 110 kV transformers, respectively. Compared with other present localisation methods, such as the Chan algorithm, the genetic algorithm and the imperial competitive algorithm, the proposed algorithm can effectively reduce localisation error.

关键词： condition monitoring time-of-arrival estimation partial discharge measurement genetic algorithms power transformers acoustic emission testing acoustic wave propagation search problems TDOA method propagation process suspected PD source PD origin IPRS algorithm localisation experiments localisation methods Chan algorithm genetic algorithm imperial competitive algorithm partial discharge localisation methodology power transformer improved acoustic propagation route search algorithm partial discharge sources acoustic emission technique wave-type conversion AE wave propagation tank wall direct wave transformer model suspected PD position shortest route search algorithm shortest path optimisation algorithms TDOA equations localisation error propagation route search algorithm time difference of arrival equations voltage 110 0 kV

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A novel planning-attack-reconfiguration method for enhancing resilience of distribution systems considering the whole process of resiliency

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第2期30卷

作者： Wang, Hongkun Wang, Shouxiang Yu, Lu Hu, Ping Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Shihezi Univ Coll Mech & Elect Engn Shihezi Peoples R China State Grid Hebei Elect Power Co Ltd Shijiazhuang Hebei Peoples R China

To enhance the resilience of distribution systems and fight against extreme disasters, a novel planning-attack-reconfiguration optimization method is proposed in this paper. Firstly, according to the processes of prevention, defence, and restoration for a resilient distribution system through disruption, the novel resilience evaluation indicators are presented, which include the node degree of distributed generation (DG) bus, survival rate, and recovery ability. Secondly, a novel planning-attack-reconfiguration optimization model is developed to improve the resilience of distribution systems. In DG planning stage, the multi-objective planning model is formulated, which includes the minimization of the total cost of investment and operation, and the maximization of the node degree of DG buses for critical loads. In the attack stage, a clear worst case of N-k contingencies on the basis of generalized nodes is presented to reduce the computational complexity. Then, the post-disaster network reconfiguration model is formulated to maximize the restoration rate of critical loads (RRCL). Finally, the proposed method is illustrated by the case study on PG&E 69-bus distribution system. The simulation results indicate that all the RRCL can reach about 90% in the four multipoint fault scenarios. Meanwhile, other evaluation indicators are greatly improved. It is shown that the resilience of distribution systems can be dramatically enhanced by the proposed method.

关键词： distribution networks evaluation indicators imperial competitive algorithm N-k contingencies planning-attack-reconfiguration model resilience enhancement topology simplification

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Data mining-based damage identification of a slab-on-girder bridge using inverse analysis

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MEASUREMENT 2020年 151卷 107175-000页

作者： Gordan, Meisam Ismail, Zubaidah Razak, Hashim Abdul Ghaedi, Khaled Ibrahim, Zainah Tan, Zhi Xin Ghayeb, Haider Hamad Univ Malaya Dept Civil Engn StrucHMRSGrp Kuala Lumpur 50603 Malaysia Univ Malaya Dept Civil Engn Kuala Lumpur 50603 Malaysia Queensland Univ Technol Sch Civil Engn & Built Environm Brisbane Qld 4001 Australia

Classical damage detection methods such as visual inspections have many limitations, i.e. time consuming procedure, costly process and ineffective for large and complex structural systems. To overcome these difficulties, a data mining-based damage identification approach is developed in this study. First four natural frequencies which obtained from the experimental modal analysis of a slab-on-girder bridge structure are used as an input database. The laboratory work is carried out through single-type and multiple-type damage scenarios. The applicability of machine learning, artificial intelligence and statistical data mining techniques are here examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to predict the model behavior and damage severity. Then, a hybrid algorithm is proposed in the deployment step of Cross Industry Standard Process for Data Mining (CRISP-DM) model. According to the obtained results, the hybrid algorithm performs a better accuracy in compare to ANN technique itself. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Data mining-based damage identification Experimental modal analysis Cross Industry Standard Process for Data Mining imperial competitive algorithm Hybrid algorithm

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