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第37届中国控制会议

作者： Yong Cheng Deming Lei School of Automation Wuhan University of Technology

This study considers chain reentrant flow shop scheduling problem which exists in real-world industries. Each job visits all machines in a fixed order and return back to the first machine for its last operation. The goal of the problem is to decide the processing sequence of jobs on each machine in order to minimize makespan and total tardiness simultaneously. An improved imperialist competitive algorithm（IICA） is proposed, in which new solutions are generated by the revolution of imperialists, assimilation and revolution of colonies. Some new strategies are also adopted to construct initial empires and implement imperialist competition. A number of experiments are conducted to test the performance of IICA. The computational results demonstrate that IICA can provide promising results for the considered problem.

关键词： Reentrant Flow shop scheduling Multi-objective optimization imperialist competitive algorithm

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Black-box identification of a pilot-scale dryer model: A Support Vector Regression and an imperialist competitive algorithm approach

Black-box identification of a pilot-scale dryer model: A Sup...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Salat, Robert Awtoniuk, Michal Korpysz, Krzysztof Warsaw Univ Life Sci Fac Prod Engn Nowoursynowska 164 PL-02787 Warsaw Poland

This report describes system identification by means of the hybrid black-box method. The identification was carried out on the regulation object (a dryer model) representing the pilot-scale processes occurring during air conditioning and drying. The new approach proposed in the paper is the use of the imperialist competitive algorithm (ICA) as a tool for selecting the best parameters for support vector regression (SVR) and for selecting an optimal set of regressors. The advantage of this method is that the selection of an optimal set of regressors and the optimal parameters of SVR for this set is performed automatically, which reduces the time needed for identification. The results of the SVR with the ARX, ARMAX, OE, Box -Jenkins (BJ) and low-order transfer function (Tf) models were compared. The research was conducted for two fan speeds, equal to 40% and 60%. The Fit and MSE indicators for the SVR achieved a higher value with respect to those of the ARX, ARMAX, OE, BJ and Tf models. This method is sufficiently universal and can be applied to any plant as an efficient alternative method. This report is supported by National Science Centre grant UMO-2015/17/B/NZ7/02937 (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： black box system identification support vector machine imperialist competitive algorithm

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An imperialist competitive algorithm for Service Composition and Optimal Selection in Cloud Manufacturing 5

An Imperialist Competitive Algorithm for Service Composition...

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5th International Symposium on Computational and Business Intelligence (ISCBI)

作者： Akbaripour, Hossein Houshmand, Mahmoud Kerdegari, Adeleh Sharif Univ Technol Dept Ind Engn Tehran Iran

ISBN: (纸本)9781538617724

Cloud manufacturing is an emerging service-oriented manufacturing paradigm that integrates and manages distributed manufacturing resources through which complex manufacturing demands with a high degree of customization can be fulfilled. The process of Service Composition and Optimal Selection (SCOS) is an important issue for practical implementation of cloud manufacturing. In this paper, a new Mixed Integer Programming (MIP) model for solving the SCOS with transportation consideration has been proposed. This model minimizes both manufacturing and transportation costs subject to some constraints such as necessity of transportation between manufacturing resources and the requirements specified by customers in terms of maximum cost and delivery date. Then, an imperialist competitive algorithm (ICA) was tailored to solve the SCOS for an example of online motorcycle production in the United States. The results showed that the ICA is able to find the optimal solution faster than the CPLEX solver in GAMS optimization software. The ICA can be applied for solving the large-scale instances of the SCOS problem as well.

关键词： Cloud Manufacturing Service Selection Mixed Integer Programming imperialist competitive algorithm Optimization

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Improved imperialist competitive algorithm for Flexible Flow Shop Scheduling 9th

Improved Imperialist Competitive Algorithm for Flexible Flow...

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2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017)

作者： SUN Yue LIN Shuo LI Tan MA Xiaofu Shenyang Jianzhu University Faculty of Information and Control Engineering Shenyang Jianzhu University Municipal and Environmental Engineering Institute in Shenyang Jianzhu University the Department of Electrical and Computer Engineering Virginia Tech

ISBN: (纸本)9781509065769;9781509065752

In this paper,we investigate the flexible flow-shop scheduling problem（FFSP）.The mathematical model is established to minimize the *** imperialist competitive algorithm（IICA） is proposed as the global searching mechanism for this optimization *** on the standard imperialist competitive algorithm,two improvements,reform operation and elite individual retention strategy have been *** the same time,to improve the diversity of solutions,the Hamming distance has also been introduced to select the *** the standard instance tests,the proposed IICA outperforms the genetic algorithm and the standard imperialist competitive algorithm in terms of the convergence speed and global optimum searching for the flexible flow-shop scheduling problem.

关键词： Flexible flow-shop problem imperialist competitive algorithm Hamming distance makespan

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Black-box identification of a pilot-scale dryer model: A Support Vector Regression and an imperialist competitive algorithm approach

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IFAC-PapersOnLine 2017年第1期50卷 1559-1564页

作者： Sałat R. Awtoniuk M. Korpysz K. Warsaw University of Life Sciences Faculty of Production Engineering Nowoursynowska 164 Warsaw 02-787 Poland

This report describes system identification by means of the hybrid black-box method. The identification was carried out on the regulation object (a dryer model) representing the pilot-scale processes occurring during air conditioning and drying. The new approach proposed in the paper is the use of the imperialist competitive algorithm (ICA) as a tool for selecting the best parameters for support vector regression (SVR) and for selecting an optimal set of regressors. The advantage of this method is that the selection of an optimal set of regressors and the optimal parameters of SVR for this set is performed automatically, which reduces the time needed for identification. The results of the SVR with the ARX, ARMAX, OE, Box-Jenkins (BJ) and low-order transfer function (Tf) models were compared. The research was conducted for two fan speeds, equal to 40% and 60%. The Fit and MSE indicators for the SVR achieved a higher value with respect to those of the ARX, ARMAX, OE, BJ and Tf models. This method is sufficiently universal and can be applied to any plant as an efficient alternative method. This report is supported by National Science Centre grant UMO-2015/17/B/NZ7/02937 © 2017

关键词： black box imperialist competitive algorithm support vector machine system identification

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imperialist competitive algorithm with Fuzzy Logic for Parameter Adaptation: A Parameter Variation Study 14th

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14th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets (IWIFSGN)

作者： Bernal, Emer Castillo, Oscar Soria, Jose Tijuana Inst Technol Tijuana BC Mexico

ISBN: (纸本)9783319262116;9783319262109

This paper applies the imperialist competitive algorithm (ICA) to benchmark mathematical functions with the original method to analyze and perform a study of the variation of the results obtained with the ICA algorithm as we vary the parameters manually for 4 mathematical functions. The results demonstrate the efficiency of the algorithm to optimization problems and give us the pattern for future work in dynamically adapting these parameters.

关键词： imperialist competitive algorithm ICA Mathematical functions

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Structural Damage Detection Using FRF Data, 2D-PCA, Artificial Neural Networks and imperialist competitive algorithm Simultaneously

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2017年第7期17卷 1750073-1750073页

作者： Khoshnoudian, Faramarz Talaei, Saeid Fallahian, Milad Amirkabir Univ Technol Fac Civil Engn Tehran Iran Islamic Azad Univ Young Researchers & Elite Club Cent Tehran Branch Tehran Iran

In this study, a promising pattern recognition based approach is introduced for structural damage identification using the measured dynamic data. The frequency response function (FRF) is preferably employed as the input of the proposed algorithm since it contains the most information of structural dynamic characteristics. The 2D principal component analysis (2D-PCA) is used to reduce the large size of FRFs data. The output data generated by the 2D-PCA are used to extract the damage indexes for each of the damage scenarios. A dataset of all probable damage indexes is provided;of which 30% are selected to form the train dataset and to be compared with the unknown damage index for an unidentified state of the structure. The sum of absolute errors (SAE) are calculated between the unknown damage index and the selected indexes from the dataset;of which the minimum refers to the most similar damage condition to the unknown one. The artificial neural networks (ANNs) are used to form a smooth function of the SAEs and the imperialist competitive algorithm (ICA) is utilized to minimize this function in order to find the location and severity of the damages of the unknown state of the structure. To validate the proposed method, the damage identification of a truss bridge structure and a two-story frame structure is conducted by considering all the single damage cases as well as multi damage scenarios. In addition, the robustness of the proposed method to measurement noise up to 20% is thoroughly investigated.

关键词： Damage detection damage index frequency response function artificial neural network imperialist competitive algorithm

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A prototype-based classification using extended imperialist competitive algorithm

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SCIENTIA IRANICA 2017年第4期24卷 2062-2081页

作者： Zarandi, M. H. Fazel Teimouri, M. Zaretalab, A. Hajipour, V. Amirkabir Univ Technol Dept Ind Engn 424 Hafez Ave Tehran Iran Amirkabir Univ Technol Dept Ind Engn Tehran Polytech 424 Hafez Ave Tehran Iran Islamic Azad Univ Qazvin Branch Fac Ind & Mech Engn Qazvin Iran Bu Ali Sina Univ Dept Ind Engn Fac Engn Hamadan Iran

Classification is an important machine learning technique used to predict group membership for data instances. In this paper, we propose an efficient prototype based classification approach in the data classification literature by a novel soft-computing approach based on extended imperialist competitive algorithm. The novel classifier is called EICA. The goal is to determine the best places of the prototypes. EICA is evaluated under three different fitness functions on twelve typical test datasets from the UCI Machine Learning Repository. The performance of the proposed EICA is compared with well-developed algorithms in classification including original imperialist competitive algorithm (ICA), the Artificial Bee Colony (ABC), the Firefly algorithm (FA), the Particle Swarm Optimization (PSO), the Gravitational Search algorithm (GSA), the Grouping Gravitational Search algorithm (GGSA), and nine well-known classification techniques in the literature. The analysis results show that EICA provides encouraging results in contrast to other algorithms and classification techniques. (C) 2017 Sharif University of Technology. All rights reserved.

关键词： Prototype-based classification imperialist competitive algorithm UCI machine learning repository

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Prediction of hydrate formation temperature based on an improved empirical correlation by imperialist competitive algorithm

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PETROLEUM SCIENCE AND TECHNOLOGY 2016年第2期34卷 162-169页

作者： Amin, Javad Sayyad Nejad, Samira Keshavarz Babaee Veiskarami, Mehdi Bahadori, Alireza Univ Guilan Dept Chem Engn Rasht Iran Univ Guilan Dept Civil Engn Rasht Iran Shiraz Univ Sch Engn 1 Dept Civil & Environm Engn Shiraz Iran So Cross Univ Sch Environm Sci & Engn Lismore NSW 2480 Australia

Formation of gas hydrates is an important problem in production and transmission of natural gases. Hence, it is a particular interest and also vital to estimate the hydrate formation conditions. To do so, the Katz gas gravity method can be used as one of the simplest techniques. Numerous correlations have been presented so far, among which one of the most popular one with very good accuracy is the so called Ghiasi model. In this study, the imperialist competitive algorithm is applied to improve the Ghiasi correlation by using the Katz data. Error analysis of the results demonstrated that the improved equation had the highest precision in comparison with the Ghiasi correlation and also the other models reported by researchers.

关键词： Gas hydrates hydrate formation temperature Katz gas gravity chart imperialist competitive algorithm

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Bare-bones imperialist competitive algorithm for a compensatory neural fuzzy controller

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NEUROCOMPUTING 2016年第Part3期173卷 1519-1528页

作者： Chen, Cheng-Hung Chen, Wen-Hsien Natl Formosa Univ Dept Elect Engn Huwei Township 632 Yunlin Taiwan

This study proposes a bare-bones imperialist competitive algorithm (BBICA) for a compensatory neural fuzzy controller in order to solve prediction problems and nonlinear control system problems. In the proposed BBICA, the population is divided into two types: imperialists and colonies. The purpose is that the cultures of colonies and societies can be similar to their empires. Moving colonies toward their relevant imperialist refers the assimilation policy. This study focuses on improving and exploring the process of assimilation. In the assimilation policy, there are two main control parameters for adjusting the direction of motion of colonies, i.e., location and angle. This study proposes the BBICA in order to reduce the use of the control parameters. Therefore, the proposed BBICA adopts a Gaussian distribution to improve the assimilation policy and the exploration ability of the global solution. Finally, the experimental results show that the proposed BBICA better approximates the global optimal solution and that it has faster convergence compared with other methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： imperialist competitive algorithm Compensatory neural fuzzy controller Function approximation Prediction of a chaotic time series Prediction of a time series Control of backing up the truck

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