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imperialistic competitive algorithm:A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes

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Geoscience Frontiers 2016年第1期7卷 83-89页

作者： Ali Reza Kashani Amir Hossein Gandomi Mehdi Mousavi Department of Civil Engineering Arak University Department of Civil Engineering The University of Akron

In this study, imperialistic competitive algorithm（ICA） is utilized for locating the critical failure surface and computing the factor of safety（FOS） in a slope stability analysis based on the limit equilibrium approach. The factor of safety relating to each trial slip surface is calculated using a simplified algorithm of the Morgenstern-Price method, which satisfies both the force and the moment equilibriums. General slip surface is considered non-circular in this study that is constituted by linking random straight *** explore the performance of the proposed algorithm, four benchmark test problems are analyzed. The results demonstrate that the present techniques can provide reliable, accurate and efficient solutions for locating the critical failure surface and relating FOS. Moreover, in contrast with previous studies the present algorithm could reach the lower value of FOS and reached more exact solutions.

关键词： Meta-heuristic algorithms Morgen-stern and price method Non-circular slip surface imperialistic competitive algorithm

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imperialistic competitive algorithm for Solving a Dynamic Cell Formation Problem with Production Planning

Imperialistic Competitive Algorithm for Solving a Dynamic Ce...

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6th International Conference on Intelligent Computing

作者： Sarayloo, Fatemeh Tavakkoli-Moghaddam, Reza Islamic Azad Univ South Grad Sch Dept Ind Engn Tehran Branch Tehran Iran Univ Tehran Coll Engn Dept Ind Engn Tehran 14174 Iran Univ Tehran Coll Engn Dept Engn Sci Tehran 14174 Iran

ISBN: (纸本)9783642149214

Cellular manufacturing system an application of group technology has been considered as an effective way to obtain productivity in a factory For design of manufacturing cells several mathematical models and various algorithms have been proposed in literature However most of algorithms and models have more or fewer drawbacks A dynamic cell formation problem with production planning is considered in this paper where the sum of costs consisting constant machine costs variable machine costs intra-inter movement costs production planning costs and reconfiguration costs is to be minimized Because this type of problem is Np hard evolutionary algorithms are applied In this paper the imperialistic competitive algorithm (ICA) which optimizes inspired by imperialistic competition is used ICA is compared with other well-known evolutionary algorithms i e genetic algorithm (GA) and particle swarm optimization (PSO) to show its efficiency The computational results show the considerable superiority of ICA compared with PSO and GA

关键词： Cellular manufacturing Production Planning Evolutionary algorithm imperialistic competitive algorithm

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Multi-objective imperialistic competitive algorithm with multiple non-dominated sets for the solution of global optimization problems

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SOFT COMPUTING 2018年第24期22卷 8273-8288页

作者： Sherinov, Zhavat Unveren, Ahmet Eastern Mediterranean Univ Comp Engn Dept Mersin 10 Magusa Turkish Republi Cyprus

In this paper, we propose a multi-objective imperialistic competitive algorithm (MOICA) for solving global multi-objective optimization problems. The MOICA is a modified and improved multi-objective version of the single-objective imperialistic competitive algorithm previously proposed by Atashpaz-Gargari and Lucas (IEEE Congr Evolut Comput 7:4661-4666. doi:10.1109/CEC.2007.4425083, 2007). The presented algorithm utilizes the metaphor of imperialism to solve optimization problems. Accordingly, the individuals in a population are referred to as countries, of which there are two typescolonies and imperialists. The MOICA incorporates competition between empires and their colonies for the solution of multi-objective problems. To this end, it employs several non-dominated solution sets, whereby each set is referred to as a local non-dominated solution (LNDS) set. All imperialists in an empire are considered non-dominated solutions, whereas all colonies are considered dominated solutions. In addition to LNDS sets, there is one global non-dominated solution (GNDS) set, which is created from the LNDS sets of all empires. There are two primary operators in the proposed algorithm, i.e., assimilation and revolution, which use the GNDS and LNDS sets, respectively. The significance of this study lies in a notable feature of the proposed algorithm, which is that no special parameter is used for diversity preservation. This enables the algorithm to prevent extra computation to maintain the spread of solutions. Simulations and experimental results on multi-objective benchmark problems show that the MOICA is more efficient compared to a few existing major multi-objective optimization algorithms because it produces better results for several test problems.

关键词： Multi-objective metaheuristics imperialistic competitive algorithm Multiple non-dominated sets Global optimization

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Optimal allocation of FACTS devices for static security enhancement in power systems via imperialistic competitive algorithm (ICA)

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APPLIED SOFT COMPUTING 2016年第0期48卷 317-328页

作者： Jordehi, A. Rezaee Islamic Azad Univ Lashtenesha Zibakenar Branch Dept Elect Engn Lashtenesha Iran

The problem of optimal allocation of flexible AC transmission systems (FACTS) devices is deemed as a formidable optimisation problem. Metaheuristics are the common approaches for solving FACTS allocation problems. imperialistic competitive algorithm (ICA) is a well-established optimisation algorithm which has been successfully employed for solving complex optimisation problems in different fields. It is inspired by imperialistic competition and socio-political evolution of human beings and offers appropriate exploration and exploitation capabilities during the search for global optima. This paper employs ICA for solving FACTS allocation problem in a way that low values of overloads and voltage deviations are resulted both during line outage contingencies and demand growth. Thyristor-controlled phase shifting transformers (TCPST's) and thyristor-controlled series compensators (TCSC's) have been used as FACTS devices. The results of employing ICA for FACTS allocation problem indicate that ICA Offers better results than artificial bee colony (ABC), gravitational search algorithm (GSA), evolutionary programming (EP), bat swarm optimisation (BSO), nonlinear programming (NLP), pattern search (PS), asexual reproduction optimisation (ARO) and backtracking search algorithm (BSA). (C) 2016 Elsevier B.V. All rights reserved.

关键词： imperialistic competitive algorithm Thyristor-controlled phase shifting transformer Thyristor-controlled series compensator FACTS allocation problem Transmission system Optimisation

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Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2018年第3期15卷 551-560页

作者： Hasanipanah, M. Amnieh, H. Bakhshandeh Khamesi, H. Armaghani, D. Jahed Golzar, S. Bagheri Shahnazar, A. Islamic Azad Univ Qom Branch Young Researchers & Elite Club Qom Iran Univ Tehran Sch Min Coll Engn Tehran 111554563 Iran Univ Kashan Dept Min Engn Fac Engn Kashan Iran Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran

Ground vibration resulting from blasting is one of the most important environmental problems at open-cast mines. Therefore, accurately approximating the blast-induced ground vibration is very significant. By reviewing the previous investigations, many attempts have been done to create the empirical models for estimating ground vibration. Nevertheless, the performance of the empirical models is not good enough. In this research work, a new hybrid model of fuzzy system (FS) designed by imperialistic competitive algorithm (ICA) is proposed for approximating ground vibration resulting from blasting at Miduk copper mine, Iran. For comparison aims, various empirical models were also utilized. Results from different predictor models were compared by using coefficient of multiple determination (R (2)), variance account for and root-mean-square error between measured and predicted values of the PPVs. Results prove that the FS-ICA model outperforms the other empirical models in terms of the prediction accuracy. In other words, the FS-ICA model with R (2) of 0.942 can forecast PPV better than the USBM with R (2) of 0.634, Ambraseys-Hendron with R (2) of 0.638, Langefors-Kihlstrom with R (2) of 0.637 and Indian Standard with R (2) of 0.519.

关键词： Blasting operation Fuzzy system Ground vibration imperialistic competitive algorithm

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Introducing a novel method based on the imperialistic competitive algorithm to determine fluorine intermolecular potential from ab initio calculations and calculation of some properties via MD simulations

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MOLECULAR SIMULATION 2018年第3期44卷 243-253页

作者： Tashakor, Saeedeh Noorbala, Mohammad R. Payvandy, Pedram Mohammadi-Manesh, Hossein Namazian, Mansoor Yazd Univ Dept Chem Yazd Iran Yazd Univ Dept Text Engn Yazd Iran

In this work, the possibility of obtaining an accurate site-site potential model suitable for use in molecular dynamics (MD) simulations of fluorine from ab initio calculations has been explored. The exploration was made on ab initio calculations. To reduce the ab initio pair potentials into a site-site potential, a higher significance was assigned to the configuration which is more stable. For this purpose, the imperialistic competitive algorithm (ICA) was implemented as a powerful optimisation tool. The calculated second virial coefficients were compared to the experimental values to test the quality of the presented intermolecular potential. The relative error for the calculated second virial coefficient ranged from 0.1 to 5.6%. MD simulations were used to evaluate the ability of the proposed intermolecular potential function. The relative error for the MD simulations ranged from 0.5 to 5.2%. The results are in good agreement with experimental data.

关键词： Fluorine molecule Ab initio calculations imperialistic competitive algorithm molecular dynamics simulation

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Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019年 247卷 712-729页

作者： Wang, Yi Hong, Haoyuan Chen, Wei Li, Shaojun Panahi, Mahdi Khosravi, Khabat Shirzadi, Ataollah Shahabi, Himan Panahi, Somayeh Costache, Romulus China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Hubei Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China State Key Lab Cultivat Base Geog Environm Evolut Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Shaanxi Peoples R China Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Hubei Peoples R China Islamic Azad Univ North Tehran Branch Young Researchers & Elites Club Tehran Iran Sari Agr Sci & Nat Resources Univ SANRU Fac Nat Resources Dept Watershed Management Engn Sari Iran Univ Kurdistan Fac Nat Resources Dept Watershed Management Sanandaj Iran Univ Kurdistan Fac Nat Resources Dept Geomorphol Sanandaj Iran Univ Bucharest Res Inst 36-46 Bd M Kogalniceanu5th Dist Bucharest 050107 Romania Natl Inst Hydrol & Water Management Bucuresti Ploiesti Rd 97E1st Dist Bucharest 013686 Romania

Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task;so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA). A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30%) sets. To perform the proposed framework, the step-wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS-BBO and ANFIS-ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), root-mean-square error (RMSE). The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS-BBO and ANFIS-ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS-BBO and ANFIS-ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition,

关键词： Flood susceptibility mapping Adaptive neuro-fuzzy inference system Metaheuristic methods Biogeography based optimization imperialistic competitive algorithm

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Newly Developed Enhanced imperialistic competitive algorithm for Design Optimization of an Autonomous Hybrid Green Power System

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APPLIED MATHEMATICS & INFORMATION SCIENCES 2014年第1期8卷 309-320页

作者： Etesami, M. H. Ardehali, M. M. Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Energy Res Ctr Tehran 158754413 Iran

The importance of economics for owning hybrid green power systems (HGPS) warrants development of optimization methodologies with more effective search capabilities for determination of global minimum for costs. The objective of this study is to present several newly developed enhancements for imperialistic competitive algorithm (ICA) for design optimization of an autonomous HGPS with considerations for economics and reliability. HGPS examined consists of photovoltaic (PV) modules in a panel, wind turbines (WT), and storage batteries (SB). Utilizing an IEEE load profile and actual solar irradiation and wind speed data, the economics is evaluated based on annualized cost of system (ACS) and reliability constraint specified in terms of loss of power supply probability (LPSP). The simulation results show that enhanced ICA (EICA) developed in this study has a better convergence rate, as compared with other population based optimization methods such as ICA and genetic algorithm (GA). It is found that the new enhancements developed for EICA result in lower computation time for determining the optimal configuration of HGPS equipment by 40 and 79 % for ICA and GA, respectively. For LPSP of 2 %, it is determined that EICA results in lower ACS by 11.60 and 6 % in comparison with ICA and GA, respectively. For computation time, convergence occurs in 33, 55, and 160 minutes for EICA, ICA, and GA algorithms, respectively.

关键词： Hybrid green power system imperialistic competitive algorithm design optimization economics

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Application of Modified imperialistic competitive algorithm for Economic Emission Dispatch

Application of Modified Imperialistic Competitive Algorithm ...

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International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)

作者： Kuanr, Biswa Ranjan Lata, Madhavi Shekhar, Himanshu Das, Saborni Das, Sudipta Jadavpur Univ Power Engn Dept Kolkata India

ISBN: (纸本)9781479976782

Economic emission dispatch problem (EED) has gained attention of power system researchers for many years. With the deregulation of electricity market and strict environmental norms and regulations, there exists a further scope to revisit the problem. Actually the Economic emission dispatch minimizes both the fuel cost as well as emission from fossil fuel fired power plants under several equality and inequality constraints. A Modified imperialistic Competition algorithm (MICA) technique is proposed for solving EED problem and a fuzzy set theory approach is implemented for selecting the best compromise solution out of a set of non-dominated Pareto solutions. Proposed approach is verified on the standard 30-bus 6-generator test system. Equality constraints on power balance and inequality constraints on generation capacity limit are taken into consideration. The quality of solution by proposed algorithm is found to be superior to many other multi-objective techniques.

关键词： Economic Emission Dispatch Multi-objective Optimization imperialistic competitive algorithm Control parameter setting

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Assessment of machinability of inconel 718: A comparative study of CVD & PVD coated tools

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INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES 2019年第5-6期26卷 281-297页

作者： Yadav, Rajiv Kumar Chatterjee, Suman Abhishek, Kumar Mahapatra, Siba Sankar Natl Inst Technol Dept Mech Engn Rourkela 769008 India Inst Infrastruct Technol Res & Management Dept Mech Engn Ahmadabad 380026 Gujarat India

This paper highlights the parametric appraisal in turning of inconel 718 using fuzzy inference system coupled with imperialistic competitive algorithm (ICA) approach. The machining variables such as spindle speed, feed rate and depth of cut have been taken into consideration to analyse their effect on evaluation characteristics viz. material removal rate (MRR), flank wear and surface roughness. Fuzzy inference system (FIS) has been used to integrate aforementioned evaluation characteristics into a single response known as multi performance characteristic index (MPCI) to address the issue of impreciseness and uncertainties involved in decision making. Mathematical models have also been proposed for MPCI using non-linear regression analysis which acts as an objective function in ICA. ICA is new meta-heuristic based on social political theory which is used to obtain global optimal parametric combination in machining of Inconel 718. The results indicate that single layer (single coating: AlTiN) physical vapour deposition (PVD) coated tool is more efficient as compared to multi-layered (four coatings: TiN, TiCN, Al2O3 and TiN) chemical vapour deposition (CVD) coated tool.

关键词： Flank wear Fuzzy inference system imperialistic competitive algorithm Material removal rate Surface roughness

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