A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

Imperialistic Competitive Algorithm:A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes

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 lines.To 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.

Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP

Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.

Machining Parameters Optimization of Multi-Pass Face Milling Using a Chaotic Imperialist Competitive Algorithm with an Efficient Constraint-Handling Mechanism

The selection of machining parameters directly affects the production time,quality,cost,and other process performance measures for multi-pass milling.Optimization of machining parameters is of great significance.However,it is a nonlinear constrained optimization problem,which is very difficult to obtain satisfactory solutions by traditional optimization methods.A new optimization technique combined chaotic operator and imperialist competitive algorithm(ICA)is proposed to solve this problem.The ICA simulates the competition between the empires.It is a population-based meta-heuristic algorithm for unconstrained optimization problems.Imperialist development operator based on chaotic sequence is introduced to improve the local search of ICA,while constraints handling mechanism is introduced and an imperialist-colony transformation policy is established.The improved ICA is called chaotic imperialist competitive algorithm(CICA).A case study of optimizing machining parameters for multi-pass face milling operations is presented to verify the effectiveness of the proposed method.The case is to optimize parameters such as speed,feed,and depth of cut in each pass have yielded a minimum total product ion cost.The depth of cut of optimal strategy obtained by CICA are 4 mm,3 mm,1 mm for rough cutting pass 1,rough cutting pass 1 and finish cutting pass,respectively.The cost for each pass are$0.5366 US,$0.4473 US and$0.3738 US.The optimal solution of CICA for various strategies with at=8 mm is$1.3576 US.The results obtained with the proposed schemes are better than those of previous work.This shows the superior performance of CICA in solving such problems.Finally,optimization of cutting strategy when the width of workpiece no smaller than the diameter of cutter is discussed.Conclusion can be drawn that larger tool diameter and row spacing should be chosen to increase cutting efficiency.

Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children.Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years.This research study aims to develop an automated tool for diagnosing autism in children.The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification phases.The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification.The Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this study.The performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research work.The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset.The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different classifiers.The Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

Fault Attribute Reduction of Oil Immersed Transformer Based on Improved Imperialist Competitive Algorithm

The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.

Optimal Allocation of STATCOM to Enhance Transient Stability Using Imperialist Competitive Algorithm

With the daily expansion of global energy consumption,developing the power grids is of uttermost importance.However,building a new trans-mission line is costly and time-consuming,so utilizing the same lines with possible higher transmission capacity is very cost-effective.In this regard,to increase the capacity of the transmission lines,the flexible alternating current transmission system(FACTS)has been widely used in power grids in recent years by industrialized countries.One of the essential topics in electrical power systems is the reactive power compensation,and the FACTS plays a significant role in controlling the reactive power current in the power grid and the system voltage oscillations and stability.When a static synchronous compensator(STATCOM)is embedded in a power system to increase the bus voltage,a supplementary damping controller can be designed to enhance the system oscillation damping.Given the expansion of the grids in the power system,the complexity of their optimization and the extraordinary ability of the imperialist competitive algorithm(ICA)for solving such problems,in this paper,the ICA has been used to determine the optimal position and size of the FACTS devices.

A New Method for Clustering Based on Development of Imperialist Competitive Algorithm

Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering.In this study,the Imperialist Competitive Algorithm(ICA) is developed and then used in the clustering process.Clustering of IRIS,Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA,GA and PSO algorithms,demonstrate the improvement of Imperialist competitive algorithm.

FICA: fuzzy imperialist competitive algorithm

Despite the success of the imperialist competitive algorithm(ICA)in solving optimization problems,it still suffers from frequently falling into local minima and low convergence speed.In this paper,a fuzzy version of this algorithm is proposed to address these issues.In contrast to the standard version of ICA,in the proposed algorithm,powerful countries are chosen as imperialists in each step;according to a fuzzy membership function,other countries become colonies of all the empires.In absorption policy,based on the fuzzy membership function,colonies move toward the resulting vector of all imperialists.In this algorithm,no empire will be eliminated;instead,during the execution of the algorithm,empires move toward one point.Other steps of the algorithm are similar to the standard ICA.In experiments,the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition.Results of experiments confirm the performance of the algorithm.

基于MICA的声级计频率计权数字IIR滤波器设计

针对双线性变换法在设计声级计频率计权数字滤波器时存在固有频率失真问题,提出一种基于改进帝国竞争算法的数字IIR滤波器设计方法.为避免帝国竞争算法出现早熟收敛而陷入局部最优的问题,在帝国竞争算法同化阶段引入混沌函数来增大搜索范围,与此同时,在帝国竞争阶段引入克隆进化算子,引导算法向IIR滤波器参数最优解方向搜索,得到改进帝国竞争算法.在研究声级计A、C计权的IIR滤波器误差来源的基础上,利用改进帝国竞争算法对声级计频率计权数字IIR滤波器系数进行寻优求解,构建基于改进帝国竞争算法的频率计权数字IIR滤波器优化模型.仿真与实验结果表明,本文提出的数字滤波器设计方法精度较高,且滤波器的误差能控制在10-3dB数量级范围内.在噪声环境下不同声信号级进行的频率计权测试结果表明,改进帝国竞争算法测试的声信号级的计权误差能维持在10-2 dB数量级范围内,完全满足国家标准GB/T3241—2010对1级声级计的设计要求.

基于FAST-ICA的城市轨道交通乘客路径选择方法

提出一种基于改进帝国主义竞争算法(FAST-ICA)的城市轨道交通乘客路径选择方法,以提高乘客路径选择的效率。选取6种影响乘客路径选择的关键因素,在此基础上,构建广义出行费用函数,并建立乘客路径选择模型;通过改进帝国主义竞争算法(ICA)中的帝国竞争方式,在ICA算法的每次迭代中快速瓜分最弱帝国集团,以加快收敛速度;基于所提出的FAST-ICA算法求解乘客在不同环境下的路径选择问题,并进行深入分析。实验结果表明,FAST-ICA算法具有良好的稳定性,且收敛速度较快。

基于ICA阈值优化耦合信息熵的边缘提取算法

为了解决传统边缘提取算法对噪声敏感和阈值难以选取,边缘清晰度不高以及边缘不平滑等问题,提出了一种基于ICA阈值优化耦合信息熵的边缘提取算法.首先,基于灰度分布模式将图像分成若干子块,并计算每个子块的分段阈值;然后,为了从大量的分段阈值选择合适的阈值,引入了帝国主义竞争(imperialist competitive algorithm,ICA)优化算法,计算图像的最优阈值,根据获得的最优阈值将每个图像子块划分为不同的均匀区域;最后,通过计算每个均匀区域的信息熵,利用信息熵检测所有处于不同均匀区域的边界像素来提取边缘.实验结果表明:与当前常用的边缘提取算法比较,本文算法具有更高的品质因数与边缘连续性,能够抑制过于微小和琐碎的细节,突出有效的边缘信息,边缘定位精度高且平滑连贯,能够准确地提取目标轮廓.

Selective harmonic elimination method for wide range of modulation indexes in multilevel inverters using ICA

Selective harmonic elimination(SHE) in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective function with proper weighting is proposed and also its efficiency is compared with the objective function which is more similar to the proposed one. To enhance the ability of the SHE in eliminating high number of selected harmonics, at each level of the output voltage, one slot is created. The SHE problem is solved by imperialist competitive algorithm(ICA). The conventional SHE methods cannot eliminate the selected harmonics and satisfy the fundamental component in some ranges of modulation indexes. So, to surmount the SHE defect, a DC-DC converter is applied. Theoretical results are substantiated by simulations and experimental results for a 9-level multilevel inverter. The obtained results illustrate that the proposed method successfully minimizes a large number of identified harmonics which consequences very low total harmonic distortion of output voltage.

基于ICA-NN的短期风功率预测研究

随着风电大规模的接入电网,风电对电网的影响越来越大。由于风电出力具有随机性、间歇性和不可控性,导致风电对电网调度运行带来巨大的挑战。为了充分利用风电,必须将风电由未知变为基本已知,提高对风电出力的预测精度。提出一种基于帝国主义竞争算法的神经网络(ICA-NN)方法来提高短期风功率预测的精度。在该方法中,首先,建立一个基于多层感知器(MLP)人工神经网络的风速预测模型,然后,用帝国主义竞争算法优化神经网络中的权值。将该预测方法应用于新疆某风电场,验证了该方法应用于短期风功率预测的有效性,证明了该方法可以提高短期风功率预测的精度。

基于改进ICA算法的电力系统无功优化

电力系统无功优化可以提高电能质量、降低网损,预防事故发生和扩大,而现有的无功优化算法容易陷入早熟和局部收敛的不足。提出一种改进帝国竞争算法求解无功优化问题,通过模糊动态聚类分析法对帝国群体进行划分,采用适应度共享技术对联盟国家内各个国家的适应度进行调整,以提高全局寻优能力,有效避免算法早熟现象。选取IEEE 30节点系统进行测试仿真,并将优化结果与遗传算法和传统帝国竞争算法进行对比分析,结果表明改进帝国竞争算法在解决无功优化问题中具有更强的全局搜索能力,能得到更好的收敛效果。

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.

Optimal design of hybrid wind/photovoltaic electrolyzer for maximum hydrogen production using imperialist competitive algorithm

The rising demand for high-density power storage systems such as hydrogen,combined with renewable power production systems,has led to the design of optimal power production and storage systems.In this study,a wind and photovoltaic(PV)hybrid electrolyzer system,which maximizes the hydrogen production for a diurnal operation of the system,is designed and simulated.The operation of the system is optimized using imperialist competitive algorithm(ICA).The objective of this optimization is to combine the PV array and wind turbine(WT)in a way that,for minimized average excess power generation,maximum hydrogen would be produced.Actual meteorological data of Miami is used for simulations.A framework of the advanced alkaline electrolyzer with the detailed electrochemical model is used.This optimal system comprises a PV module with a power of 7.9 kW and a WT module with a power of 11 kW.The rate of hydrogen production is 0.0192 mol/s;an average Faraday efficiency of 86.9 percent.The electrolyzer works with 53.7 percent of its nominal power.The availability of the wind for longer periods of time reflects the greater contribution of WT in comparison with PV towards the overall throughput of the system.

基于ICEEMDAN-ICA-ELM的中国采购经理人指数预测研究

文章引入机器学习算法,基于改进的带有自适应白噪声的完全集合经验模态分解(ICEEMDAN)-帝国竞争算法(ICA)—极限学习机(ELM),构建中国采购经理人指数预测模型,引入Diebold-Mariano统计量进行预测结果的比较。结果发现:ICEEMDAN技术可以准确提取数据中的有效信息,改进模型拟合效果;提出的组合模型ICEEMDAN-ICA-ELM预测效果优良,泛化能力强,误差较小,能够为PMI的走势提供新的预测方法。

Direct load control by distributed imperialist competitive algorithm

Demand side management techniques have drawn significant attentions along with the development of smart grid.This paper proposes a new direct load control(DLC)model for scheduling interruptible air conditioner loads.The model is coordinated with the unit commitment and economic dispatch to minimize the total operation cost over the whole dispatch horizon.The network constraints are also considered in the model.To ensure the thermal comfort of the occupants,we are among the first to incorporate the advanced two-parameter thermal inertia dynamical model of customer houses into the DLC model to calculate the indoor temperature variation.This paper also proposes a distributed imperialist competitive algorithm to effectively solve the model.The simulation studies prove the efficiency of the proposed methodology.

Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

Nonlinear constrained optimization problem(NCOP) has been arisen in a diverse range of sciences such as portfolio, economic management, airspace engineering and intelligence system etc.In this paper, a new multiobjective imperialist competitive algorithm for solving NCOP is proposed.First, we review some existing excellent algorithms for solving NOCP;then, the nonlinear constrained optimization problem is transformed into a biobjective optimization problem. Second, in order to improve the diversity of evolution country swarm, and help the evolution country swarm to approach or land into the feasible region of the search space, three kinds of different methods of colony moving toward their relevant imperialist are given. Thirdly, the new operator for exchanging position of the imperialist and colony is given similar as a recombination operator in genetic algorithm to enrich the exploration and exploitation abilities of the proposed algorithm. Fourth, a local search method is also presented in order to accelerate the convergence speed. At last, the new approach is tested on thirteen well-known NP-hard nonlinear constrained optimization functions, and the experiment evidences suggest that the proposed method is robust, efficient, and generic when solving nonlinear constrained optimization problem. Compared with some other state-of-the-art algorithms, the proposed algorithm has remarkable advantages in terms of the best, mean, and worst objective function value and the standard deviations.