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Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And imperialist competitive algorithm

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COMPUTER JOURNAL 2022年第1期65卷 18-66页

作者： Osmani, Amjad Mohasefi, Jamshid Bagherzadeh Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Urmia Univ Dept Comp Engn Orumiyeh Iran

Artificial bee colony (ABC) optimization and imperialist competitive algorithm (ICA) are two famous metaheuristic methods. In ABC, exploration is good because each bee moves toward random neighbors in the first and second phases. In ABC, exploitation is poor because it does not try to examine a promising region of search space carefully to see if it contains a good local minimum. In this study, ICA is considered to improve ABC exploitation, and two novel swarm-based hybrid methods called ABC-ICA and ABC-ICA1 are proposed, which combine the characteristics of ABC and ICA. The proposed methods improve the evaluations results in both continuous and discrete environments compared to the baseline methods. The second method improves the first optimization method as well. Feature selection can be considered to be an optimization problem because selecting the appropriate feature subset is very important and the action of appropriate feature selection has a great influence on the efficiency of classifier algorithms in supervised methods. Therefore, to focus on feature selection is a key issue and is very important. In this study, different discrete versions of the proposed methods have been introduced that can be used in feature selection and feature scoring problems, which have been successful in evaluations. In this study, a problem called cold start is introduced, and a solution is presented that has a great impact on the efficiency of the proposed methods in feature scoring problem. A total of 16 UCI data sets and 2 Amazon data sets have been used for the evaluation of the proposed methods in feature selection problem. The parameters that have been compared are classification accuracy and the number of features required for classification. Also, the proposed methods can be used to create a proper sentiment dictionary. Evaluation results confirm the better performance of the proposed methods in most experiments.

关键词： optimization methods sentiment classification imperialist competitive algorithm artificial bee colony feature selection

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imperialist competitive algorithm with Effective Assimilation Strategy: A Comparative Study on Numerical Benchmark Functions

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IETE JOURNAL OF RESEARCH 2020年第5期66卷 697-710页

作者： Davoodi, Elnaz Babaei, Ebrahim Mohammadi-ivatloo, Behnam Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Near East Univ Engn Fac Mersin 10 TR-99138 Nicosia North Cyprus Turkey

In recent years, the nature-inspired optimization algorithms known as intelligent optimization methods have been applied successfully for solving the different problems, along with the well-known mathematical methods. One of the new evolutionary algorithms presented lately is the imperialist competitive algorithm (ICA). This algorithm is based on the behaviour of imperialists in their attempt to conquer the colonies. In this paper, the original ICA has been extended and a new version of ICA has been proposed entitled "MICA". The proposed MICA uses an efficient assimilation strategy to enhance the global exploration ability and to preserve a premature convergence. This new assimilation scheme uses the most powerful imperialist's information to update. To validate the efficiency of the proposed algorithm, MICA is tested on a set of 28 non-linear benchmark functions with various dimensions and complexities. The results demonstrate that the proposed strategy enables the modified ICA to have better or at least comparable outcomes in comparison with the original ICA and the other state-of-the-art approaches at handing different types of problems.

关键词： Assimilation strategy Evolutionary algorithms imperialist competitive algorithm Modified imperialist competitive algorithm Time-varying coefficients

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Damage Detection of a Scaled Steel Frame Offshore Structure Using an Improved imperialist competitive algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2022年第2期46卷 1011-1025页

作者： Gerist, Saleheh Maheri, Mahmoud R. Unger, Jorg F. Shiraz Univ Dept Civil Engn Shiraz Iran Shiraz Univ Shiraz Iran Fed Inst Mat Res & Testing Berlin Germany

In this paper, the imperialist competitive optimization algorithm is improved by damage functions to detect damage in a model steel frame test structure for offshore applications. A finite element model of the test structure is developed, validated and updated using the proposed method. As there are much more design variables, which are related to the stiffness of each finite element than the measured mode shapes, the problem is underdetermined. Therefore, damage functions are used to regularize the problem and decrease the number of design variables. A new objective function is proposed for the algorithm using the mode shapes and their l(1) norm. The first ten measured mode shapes are used to solve the problem. It is shown that the proposed method is capable of predicting the damage locations with acceptable accuracy.

关键词： Damage detection imperialist competitive algorithm Steel frames Offshore structure Damage functions

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Multi-objective traveling salesman problem with drone: imperialist competitive algorithm 34

Multi-objective traveling salesman problem with drone: imper...

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34th Chinese Control and Decision Conference (CCDC)

作者： Xiong, Hum Lei, Deming Lie, Ming Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Anhui Polytech Univ Sch Econ & Management Wuhu 241000 Peoples R China

ISBN: (纸本)9781665478960

Truck-drone delivery is a hybrid one combining the advantages of truck including large capacity and long travel distance and those of drone like high speed together and attracting much attention in the past several years. The usage of this delivery leads to some generalized routing problems such as traveling salesman problem with drone (TSP-D). TSP-D has been considered fully since the pioneering work of Murray and Chu (2015);however, multi-objective TSP-D is seldom investigated and the applications of meta-heuristics are limited. In this paper, multi-objective TSP-D is addressed and an imperialist competitive algorithm with empire cooperation (ECICA) is developed to minimize operational cost and completion time. In ECICA, a coding method with a truck's path and a set of drone nodes and an effective decoding are presented, and an adaptive empire cooperation between the strongest empire and the weakest one is implemented by exchanging computing resources and search ability. Historical evolution data are used and a new imperialist competition is adopted. Numerical experiments are conducted on various instances. ECICA is compared with comparative algorithms from the literature. The computational results reveal that ECICA has promising advantages on solving multi-objective TSP-D.

关键词： imperialist competitive algorithm multi-objective optimization traveling salesman problem drone

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An effective channel allocation designed using hybrid memory dragonfly with imperialist competitive algorithm in distributed mobile adhoc network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第16期37卷

作者： Rangasamy, Suganya Ramasamy, Kanmani Thangavel, Rajesh Kumar Dr NGP Inst Technol Coimbatore India SNS Coll Technol Coimbatore India Sri Krishna Coll Engn & Technol Coimbatore India

The channel availability problem reaches a higher degree in mobile ad hoc networks (MANETs) and garners a lot of attention in communication networks. Because increased mobile usage might result in a lack of channel allocation, an improved channel allocation technique is presented to tackle the availability problem. The distributed dynamic channel allocation (DDCA) model is built in this paper using the hybrid memory dragonfly with imperialist competitive (HMDIC) method. Based on optimization logic, this strategy assigns the channel to mobile hosts. The MANET provides a dispersed network within the coverage region in the absence of base station infrastructure. The HMDIC optimizer approach in this circumstance randomly begins every respective node to update and store their pbest value utilizing RAM dragonfly employing satellite images. The constraint values are then used to construct the cost function, which results in a strong kind of global optimum solution. The channels are therefore distributed in an effective manner. The HMDIC algorithm is used in this research to build a novel channel allocation system. It makes advantage of the exploration capabilities to successfully explore the individual node using MDA (Modified Dragonfly algorithm) and locate the global best solution using imperialist competitive algorithm (ICA). Both of these combined tactics are more effective in accelerating the convergence of the allocation model. To validate the performance, the HMDIC-based DDCA system provides promising results in terms of assigning available channels, thereby enhancing channel reuse efficiency and fractional interference. center dot The proposed technique demonstrates superior channel reuse efficiency compared to existing approaches. For a transmission range of 100 m, the proposed method achieves an efficiency of 95.28%.center dot In contrast, existing approaches such as grouping genetic algorithm (GGA), grouping ICA (GICA), and MHDA show lower efficiency rates. This

关键词： distributed dynamic channel allocation dragonfly algorithm HMDIC-based distributed dynamic channel allocation (DDCA) imperialist competitive algorithm optimization and mobile ad hoc network the hybrid memory dragonfly with imperialist competitive (HMDIC)

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Double-Assimilation of Prosperity and Destruction Oriented Improved imperialist competitive algorithm with Computational Thinking

Double-Assimilation of Prosperity and Destruction Oriented I...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Li, Bin Tang, Zhi-Bin Fujian Univ Technol Sch Mech & Automot Engn Fuzhou Peoples R China Fujian Univ Technol Sch Transportat Fuzhou Peoples R China

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Whereas the imperialist competitive algorithm (ICA) shows limited global search ability and be liable to be trapped into local optimum, a double-assimilation of prosperity and destruction oriented improved imperialist competitive algorithm (DPDO-IICA) is proposed tentatively to overcome inherent defects. The imperialist assimilation and colonial reform strategy are customized purposefully, and a novel population redistribution mechanism is introduced as well. The three improvement measures are supposed to further promote population diversity and searching accuracy. The CEC2017 test set is selected to verify the performance of the DPDO-IICA by the different types of numerical function problems with the different dimensions. Moreover, the DPDO-IICA is compared with the three first-class intelligent optimization algorithms, which have achieved significant rankings in the CEC2017 competition. The comparison shows that the DPDO-IICA has good performances, which is demonstrated by the accuracy and stability. In addition, the proportion of imperialists and colonies is investigated, and it is through the community partitioning and clustering dynamically to enhance the population diversity. In conclusion, the DPDO-IICA can effectively improve the ability of global exploration and avoid premature convergence in comparison with the original ICA.

关键词： imperialist competitive algorithm computational thinking double assimilation prosperity and destruction of empires population redistribution elite opposition-based learning differential evolution operator partitioning and clustering

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imperialist competitive algorithm hybridized with multilayer perceptron to predict the load-settlement of square footing on layered soils

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MEASUREMENT 2021年 172卷 108837-108837页

作者： Moayedi, Hossein Gor, Mesut Foong, Loke Kok Bahiraei, Mehdi Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn Da Nan 550000 Vietnam Firat Univ Engn Fac Civil Engn Dept Div Geotech Engn TR-23119 Elazig Turkey Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Razi Univ Fac Engn Kermanshah Iran

To forecast the value of bearing capacity in shallow footings, a total of 2430 finite element modelling WEND simulation is performed. In this regard and to optimize the performance of the artificial neural network (ANN), it is combined with the imperialist competitive algorithm (ICA). The new combined technique is called ICA-MLP (multi-layer perceptron). To develop the ICA-MLP model, the input parameters were the soil type (i.e., having particular soil properties for each of the sandy soil types) installed at the top, the soil type installed at the bottom, the first-layer thickness ratio (h/B) and the applied stress on the footing (kPa), while the output was the vertical settlement (mm) under the square footing. The estimations were compared with a predeveloped ANN model to demonstrate the ability of the ICA-MLP hybrid model. The results showed a high ability of ICA metaheuristic ensembles for understanding the non-linear relationship between the influential factors and the selected target. Meanwhile, a comparison between the used models revealed that the best-combined structure is when the ICA algorithm is followed by the swarm size equal to 350. In this sense, the results from the predeveloped ANN model, based on R-2 values, were 0.83 and 0.89 for the training and testing data sets, respectively, whereas the R-2 and RMSE values for the ICA-MLP model for the training and testing datasets were 0.983, 0.062 and 0.977, 0.070, respectively. Therefore, the ICA-MLP model can be regarded as a new model that is superior to the conventional MLP technique.

关键词： imperialist competitive algorithm Square footing Load-settlement response Artificial neural network Multi-layered soil

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Two-phase imperialist competitive algorithm for energy-efficient flexible job shop scheduling

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 12125-12137页

作者： Guo, Jiong Lei, Deming Li, Ming Wuhan Univ Technol Sch Automat Wuhan Peoples R China Anhui Polytech Univ Sch Econ & Management Wuhu 241000 Peoples R China

Energy-efficient flexible job shop scheduling problems (EFJSP) have been investigated fully;however, energy-related objectives often have lower importance than other ones in many real-life situations and this case is hardly considered in the previous works. In this study, EFJSP with sequence-dependent setup times (SDST) is considered, in which total tardiness and makespan are given higher importance than total energy consumption. A two-phase imperialist competitive algorithm (TPICA) is proposed. The importance difference among objectives is implemented by treating all objectives equally in the first phase and making energy consumption not to exceed a diminishing threshold in the second phase. A dynamical differentiating assimilation and a novel imperialist competition with the enforced search are implemented. Extensive experiments are conducted and the computational results show that TPICA is very competitive for EFJSP with SDST.

关键词： Flexible job shop scheduling energy-efficient scheduling imperialist competitive algorithm sequence-dependent setup times

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A novel imperialist competitive algorithm for fuzzy distributed assembly flow shop scheduling

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 4545-4561页

作者： Li, Ming Su, Bin Lei, Deming Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Assembly flow shop scheduling problem with DPm -> 1 layout has important applications in various manufacturing systems and has been extensively considered in single factory;however, this problem with fuzzy processing time is seldom studied in multiple factories. In this paper, fuzzy distributed assembly flow shop scheduling problem (FDAFSP) is considered, in which each factory has DPm -> 1 layout, and an imperialist competitive algorithm with empire cooperation (ECICA) is developed to minimize fuzzy makespan. In ECICA, an adaptive empire cooperation between the strongest empire and the weakest empire is implemented by exchanging computing resources and search ability, historical evolution data are used and a new imperialist competition is adopted. Numerical experiments are conducted on various instances and ECICA is compared with the existing methods to test its performance. Computational results demonstrate that ECICA has promising advantages on solving FDAFSP.

关键词： Assembly flow shop scheduling distributed scheduling imperialist competitive algorithm fuzzy makespan

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Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption

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COMPLEX & INTELLIGENT SYSTEMS 2021年第1期7卷 311-326页

作者： Tao, Xin-rui Li, Jun-qing Huang, Ti-hao Duan, Peng Liaocheng Univ Sch Comp Liaocheng 252059 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

The resource-constrained hybrid flowshop problem (RCHFS) has been investigated thoroughly in recent years. However, the practical case that considers both resource-constrained and energy consumption still has rare research. To address this issue, a discrete imperialist competitive algorithm (DICA) was proposed to minimize the makespan and energy consumption. In the proposed algorithm, first, each solution was represented by a two-dimensional vector, where one vector represented the scheduling sequence and another one showed the machine assignment. Then, a decoding method considering the resource allocation was designed. Finally, we combined DICA and the simulated annealing algorithm (SA) to improve the performance of the proposed approach. Furthermore, we tested the proposed algorithm based on a randomly generated set of real shop scheduling system instances and compared with the existing heuristic algorithms. The results confirmed that the proposed algorithm can solve the RCHFS with high efficiency.

关键词： Hybrid flowshop imperialist competitive algorithm Resource-constrained

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