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A modified imperialist competitive algorithm for scheduling single batch-processing machine with fuzzy due date

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期85卷 2439-2458页

作者： Molla-Alizadeh-Zavardehi, S. Tavakkoli-Moghaddam, R. Lotfi, F. Hosseinzadeh Islamic Azad Univ Masjed Soleiman Branch Dept Ind Engn Masjed Soleiman Iran Univ Tehran Dept Ind Engn Coll Engn Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Math Tehran Iran

This paper considers a fuzzy single batch-processing machine (SBPM) scheduling problem and aims to make it closer to a real-world application through a fuzzy set theory. For this purpose, jobs' due dates are set to be fuzzy numbers and the membership function of a fuzzy due date assigned to each job represents the degree of satisfaction that a decision maker has with the completion time of that job. The objective is to maximize the total degree of satisfaction or equivalently to minimize the total degree of dissatisfaction over given jobs. Then, the fuzzy mathematical programming model is presented. To solve the model, we propose the imperialist competitive algorithm (ICA) and modify the assimilation policy (i.e., colonies moving) and imperialistic competition in order to overcome its immature convergence and improve its performances. Moreover, due to the significant role of parameters on the quality of random search algorithms, a robust calibration is applied on the parameters using the Taguchi optimization technique. To evaluate the proposed ICA, several random test problems are generated and its performance is compared to the traditional ICA, simulated annealing (SA), particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and earliest due date (EDD). The obtained computational results demonstrate the superiority and robustness of the modified ICA.

关键词： Single batch-processing machine Fuzzy due date imperialist competitive algorithm Genetic algorithm Taguchi experimental design

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An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第2期58卷 597-614页

作者： Lei, Deming Yuan, Yue Cai, Jingcao Bai, Danyu Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China Nanjing Forestry Univ Coll Econ & Management Nanjing Jiangsu Peoples R China

Distributed scheduling becomes popular with the shift of production from single factory to multiple factories and reveals new features and the increasing optimisation difficulties. In this study, distributed unrelated parallel machines scheduling problem with makespan minimisation is considered in the heterogeneous production network, which is directly simplified as an extended machine assignment. A novel imperialist competitive algorithm with memory (MICA) is presented, in which a machine assignment string is adopted and four neighbourhood structures and a global search operator are introduced. In each empire, some best colonies learn from a member of memory or imperialist and other colonies move toward imperialist or one of the best colonies, and revolution is newly implemented by using good solutions. Global search of imperialist is added into imperialist competition to avoid the addition of the weakest colony of the weakest empire into the winning empire. Lower bound is provided. Extensive experiments are conducted to test the performance of MICA and computational results show that MICA is a very competitive method for the considered problem.

关键词： parallel machines scheduling imperialist competitive algorithm multi-factory production network

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A novel chaotic imperialist competitive algorithm for production and air transportation scheduling problems

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NEURAL COMPUTING & APPLICATIONS 2015年第7期26卷 1709-1723页

作者： Mortazavi, A. Khamseh, A. Arshadi Naderi, B. Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

Although production and transportation scheduling problems are of two essential issues in supply chain management, they are rarely integrated. In this paper, we study the problem of production and transportation scheduling to minimize total cost of supply chain. The problem is mathematically modeled. Moreover, a novel metaheuristic based on an imperialist competitive algorithm is developed for solving the problem. The proposed algorithm is enriched by a chaotic map and two new operators to avoid from local optima. To tune the parameters, Taguchi experimental design is applied. For evaluation of the proposed algorithm, it is compared with an available genetic algorithm. Finally, using numerical experiments, the outperformance of the proposed algorithm is shown in both solution quality and performance robustness.

关键词： Supply chain coordination Air transportation imperialist competitive algorithm Chaos theory Taguchi experimental design

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Ranking of decision making units using the imperialist competitive algorithm in DEA

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MEASUREMENT & CONTROL 2021年第9-10期54卷 1326-1335页

作者： Keshteli, Hasan Babaei Rostamy-Malkhalifeh, Mohsen Lotfi, Farhad Hosseinzadeh Islamic Azad Univ Dept Math Sci & Res Branch Tehran *** Iran

One of the challenging and important subjects in Data Envelopment Analysis (DEA) is the ranking of Decision Making Units (DMUs). In this paper, a new method for ranking the efficient DMUs is firstly proposed by utilizing the DEA technique and also developing a capable metaheuristic, imperialist competitive algorithm, derived from social, political, and cultural phenomena. Efficient DMUs are known as colonizers, and the virtual units, which are within their regions of exclusive domination, are considered as colonies. Efficient units are ranked by utilizing the factor of competition among imperialists to attract each other's colonies. One advantage of proposed method is that, without solving any mathematical, and complex solution approaches, all extreme and non-extreme units are ranked only by comparing the pairs.

关键词： Ranking data envelopment analysis imperialist competitive algorithm region of domination

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A hybrid genetic and imperialist competitive algorithm approach to dynamic cellular manufacturing system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2014年第3期228卷 458-470页

作者： Bagheri, Masoud Bashiri, Mahdi Shahed Univ Fac Engn Dept Ind Engn Tehran *** Iran

Cellular manufacturing system is an important application of group technology, which deals with grouping the parts and machines into cells, based on their similarity in design or production process. Since a purpose of this article is designing the cells under indefinite demand values up to the end of previous period, a dynamic multi-objective mathematical model based on this condition is proposed, and a heuristic approach has been developed in order to solve the problem. During this procedure, since the proposed model is a nondeterministic polynomial-time-hard problem, a new hybrid meta-heuristic approach called genetic imperialist competitive algorithm is implemented to solve it. Also, an experimental design has been used for tuning essential parameters. The proposed model has been verified by some hypothetical numerical examples with different input parameters and also by a real-world case study taken from a glass mold company. The comparison of results of the proposed genetic imperialist competitive algorithm and the genetic algorithm verifies the efficiency of the proposed solution approach. Also, the results show that the accuracy of the proposed algorithm is comparable with the exact solution methods such as branch and bound algorithm in both solution quality and computational time aspects.

关键词： genetic algorithm imperialist competitive algorithm design of experiments cellular manufacturing

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A novel permutation-diffusion technique for image encryption based on the imperialist competitive algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期82卷 725-746页

作者： Golalipour, Keyvan Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran

Recently, there has been explosive growth in the sharing of images on social media networks. This has occurred primarily because people are using smart phones with high quality imaging facilities and a mobile Internet connection. Accordingly, digital image security has formed a particular focus of recent research. Image encryption is the most common method used to keep images safe during their transmission over the Internet. There are two main phases in image encryption: permutation and diffusion. In this paper, a hybrid model is proposed, which is a combination of a novel permutation technique in conjunction with a new diffusion method. In the permutation phase of the suggested model, pixels are relocated based on their indices. The resulting permuted image has a high difference (more than 99.3%) in the gray level of pixels compared to the corresponding pixels in a plain image. The proposed diffusion method is based on a combination of an imperialist competitive algorithm (ICA) and adjacent lattices as a chaotic function. To date, the ICA, as an evolutionary algorithm, in combination with adjacent lattices, has never been exploited for image encryption. The result analysis demonstrates that the proposed method has excellent resistance against brute force and statistical attacks, in addition to obtaining a 7.9993 entropy score for the encrypted image.

关键词： Image encryption Permutation Coupled map lattice imperialist competitive algorithm

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A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop

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APPLIED MATHEMATICAL MODELLING 2013年第23期37卷 9603-9616页

作者： Goldansaz, Seyed Morteza Jolai, Fariborz Anaraki, Amir Hossein Zahedi Univ Tehran Dept Ind Engn Coll Engn Tehran Iran

In this paper a new mixed-integer linear programming (MILP) model is proposed for the multi-processor open shop scheduling (MPOS) problems to minimize the makespan with considering independent setup time and sequence dependent removal time. A hybrid imperialist competitive algorithm (ICA) with genetic algorithm (GA) is presented to solve this problem. The parameters of the proposed algorithm are tuned by response surface methodology (RSM). The performance of the algorithm to solve small, medium and large sized instances of the problem is evaluated by introducing two performance metrics. The quality of obtained solutions is compared with that of the optimal solutions for small sized instances and with the lower bounds for medium sized instances. Also some computational results are presented for large sized instances. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Multi-processor open shop scheduling problems Makespan imperialist competitive algorithm Genetic algorithm Response surface methodology

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Prediction of free flowing porosity and permeability based on conventional well logging data using artificial neural networks optimized by imperialist competitive algorithm - A case study in the South Pars gas field

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2015年 24卷 89-98页

作者： Jamshidian, Majid Hadian, Mohsen Zadeh, Mostafa Mansouri Kazempoor, Zohreh Bazargan, Pouya Salehi, Hoorash Petr Univ Technol Dept Petr Engn Ahvaz Iran Petr Univ Technol Dept Automat & Instrumentat Engn Ahvaz Iran Islamic Azad Univ Dept Petr Engn Omidiyeh Branch Omidieh Iran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

Nuclear Magnetic Resonance (NMR) logging is one of the most effective tools in determining permeability and porosity of a formation layer, so it is a reliable method to characterize a reservoir. Since NMR logging is not applicable in certain circumstances, its parameters are usually correlated with conventional logging parameters. In this research, an Artificial Neural Network (ANN) with Multi Linear Perceptron (MLP) structure and a feed-forward back-propagation algorithm is employed to predict the NMR logging parameters from their conventional counterparts. The ANN is optimized using the imperialist competitive algorithm (ICA). The ANN-ICA model is applied to two data sets measured in two separate gas wells of south Pars gas field. The data obtained from one well are used as the training data set and the other well data are utilized to test the proposed model. Finally, a sensitivity analysis (based on the parameters of the ICA algorithm and the number of neurons in the ANN) is applied to explore their effects on the ANN'S performance. According to the results, the accuracy and efficiency of the proposed model are more desirable than the traditional neural network. It is found that the optimization method is not sensitive to the ICA parameters. (C) 2015 Elsevier B.V. All rights reserved.

关键词： NMR parameters Conventional well logging Neural network imperialist competitive algorithm

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A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research

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APPLIED SOFT COMPUTING 2014年 24卷 1078-1094页

作者： Hosseini, Seyedmohsen Al Khaled, Abdullah Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA MEDSEEK Inc Birmingham AL USA

The imperialist competitive algorithm (ICA), derived from the field of human social evolution, is a component of swarm intelligence theory. It was first introduced in 2007 to deal with continuous optimization problems, but recently has been extensively applied to solve discrete optimization problems. This paper reviews the underlying ideas of how ICA emerged and its application to the engineering disciplines mainly on industrial engineering. The present study is the first ever comprehensive review on ICA, which indicates a statistically significant increase in the amount of published research on this metaheuristic algorithm, especially research addressing discrete optimization problems. Future research directions and trends are also described. (C) 2014 Elsevier B.V. All rights reserved.

关键词： imperialist competitive algorithm Metaheuristic Swarm intelligence imperialist competition

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Simulated imperialist competitive algorithm in two-stage assembly flow shop with machine breakdowns and preventive maintenance

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2016年第5期230卷 934-953页

作者： Seidgar, Hany Zandieh, Mostafa Fazlollahtabar, Hamed Mahdavi, Iraj Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management Tehran Iran Iran Univ Sci & Technol Fac Ind Engn Tehran Iran

This article deals with the two-stage assembly flow shop problem considering preventive maintenance activities and machine breakdowns. The objective is to minimize the makespan. This problem is proven to be NP-hard;thus, we proposed two meta-heuristic algorithms, namely, imperialist competitive algorithm and genetic algorithm, to obtain solutions of the problem. This article describes an approach incorporating simulation with imperialist competitive algorithm for the scheduling purpose having machine breakdowns and preventive maintenance activities. The results obtained are analyzed using Taguchi experimental design, and the parameters of proposed algorithms are calibrated by artificial neural network. The computational results demonstrate that imperialist competitive algorithm is statistically better than genetic algorithm in quality of solution and reaches to better solutions at the same computational time.

关键词： Two-stage assembly flow shop production and maintenance imperialist competitive algorithm Taguchi method artificial neural network

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