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.

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.

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.

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.

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.

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.

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.

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.

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.

A fuzzy imperialistic competitive algorithm for optimizing convex functions

Purpose–The purpose of this paper is to describe imperialist competitive algorithm(ICA),a novel socio-politically inspired optimization strategy for proposing a fuzzy variant of this algorithm.ICA is a meta-heuristic algorithm for dealing with different optimization tasks.The basis of the algorithm is inspired by imperialistic competition.It attempts to present the social policy of imperialisms(referred to empires)to control more countries(referred to colonies)and use their sources.If one empire loses its power,among the others making a competition to take possession of it.Design/methodology/approach–In fuzzy imperialist competitive algorithm(FICA),the colonies have a degree of belonging to their imperialists and the top imperialist,as in fuzzy logic,rather than belonging completely to just one empire therefore the colonies move toward the superior empire and their relevant empires.Simultaneously for balancing the exploration and exploitation abilities of the ICA.The algorithms are used for optimization have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures.FICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing fuzzy logic on it.Findings–Therefore several solution procedures,including ICA,FICA,genetic algorithm,particle swarm optimization,tabu search and simulated annealing optimization algorithm are considered.Finally numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures.Test results present the suitability of the proposed fuzzy ICA for convex functions with little fluctuations.Originality/value–The proposed evolutionary algorithm,FICA,can be used in diverse areas of optimization problems where convex functions properties are appeared including,industrial planning,resource allocation,scheduling,decision making,pattern recognition and machine learning(optimization techniques;fuzzy logic;convex functions).

Energy loss optimization of run-off-road wheels applying imperialist competitive algorithm

The novel imperialist competitive algorithm(ICA)has presented outstanding fitness on various optimization problems.Application of meta-heuristics has been a dynamic studying interest of the reliability optimization to determine idleness and reliability constituents.The application of a meta-heuristic evolutionary optimization method,imperialist competitive algorithm(ICA),for minimization of energy loss due to wheel rolling resistance in a soil bin facility equipped with single-wheel tester is discussed.The required data were collected thorough various designed experiments in the controlled soil bin environment.Local and global searching of the search space proposed that the energy loss could be reduced to the minimum amount of 15.46 J at the optimized input variable configuration of wheel load at 1.2 kN,tire inflation pressure of 296 kPa and velocity of 2 m/s.Meanwhile,genetic algorithm(GA),particle swarm optimization(PSO)and hybridized GA–PSO approaches were benchmarked among the broad spectrum of meta-heuristics to find the outperforming approach.It was deduced that,on account of the obtained results,ICA can achieve optimum configuration with superior accuracy in less required computational time.

Tele-Network Design Based on Queue Competition Algorithm

In this paper, we report research on how to design the tele-network. First of all, we defined the reliability of tele-network. According to the definition, we divide the whole reliability into two parts:the reliability of the mini-way and that of the whole system. Then we do algebra unintersection of the mini-way, deriving a function of reliability of tele-network. Also, we got a function of the cost of tele-network after analyzing the cost of arcs and points. Finally, we give a mathematical model to design a tele-network. For the algorithm, we define the distance of a network and adjacent area within certain boundaries . We present a new algorithm--Queue Competition Algorithm (QCA) based on the adjacent area . The QCA correlates sequence of fitnesses in their father-generations with hunting zone of mutation and the number of individuals generated by mutation, making the stronger fitness in a small zone converge at a local extreme value, but the weaker one takes the advantage of lots of individuals and a big zone to hunt a new local extreme value. In this way, we get the overall extreme value. Numerical simulation shows that we can get the efficient hunting and exact solution by using QCA. The QCA efficient hunting and exact solution.

Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

The modeling and optimization of an industrial-scale crude distillation unit （CDU） are addressed. The main spec- ifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a com- plicated CDU, an improved wavelet neural network （WNN） is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm （LCA）, the supe- rior performance of the proposed approach is verified. Compared with the base operating condition, it is validat- ed that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around （PA2） and third Dump-around （PA3）.

Evolutionary algorithm for optimization of multilayer coatings

In this paper, a new evolutionary algorithm, the well-known imperialist competition algorithm, is proposed for optimizing the optical thin-films. In this method, the process is modeled of the competition between countries as imperialists and their colonizing of others as colonies. This algorithm could be an appropriate alternative to some of the more popular algorithms for optimizing the optical thin-films for good performance. The polarizer and edge filter for example are designed by using the imperialist competition algorithm method and the results are compared with those from two optimization high-performance methods： the genetic algorithm and differential evolutionary algorithm. Based on these results,the performance of the imperialist competition algorithm method shows that this algorithm is not sensitive to the change of its parameters and it can be an important advantage for quickly achieving a global optimal point. On the other hand the results show a better ratio of P-polarization transmittance to S-polarization transmittance in the design of a 1540-nm polarizer, which is more appropriate than the results from the other two methods. In the second design, an edge filter with a lower number of layers and more uniform bandpass spectrum than the counterparts of those methods is obtained. These results indicate that the imperialist competition algorithm is a robust method for optical thin-film designs.

Hourly traffic flow forecasting using a new hybrid modelling method

Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

Power Maximization of A Point Absorber Wave Energy Converter Using Improved Model Predictive Control

This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves’ behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method’s efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

Estimation of tensile strength of ductile iron friction welded joints using hybrid intelligent methods

A hybrid intelligent method for evaluation of near optimal settings of friction welding process parameters of ductile iron was presented, The optimization of welding parameters was carried out in automatic cycle with the use of support vector regression （SVR）, genetic algorithm （GA） and imperialist competitive algorithm （ICA）. The method suggested was used to determine welding process parameters by which the desired tensile strength was obtained in the friction welding of ductile iron. The highest tensile strength （TS） of 256.93 MPa was obtained using SVR plus GA method for the following friction welding parameters： heating force 40 kN, heating time 300 s and upsetting force 10.12 kN. The samples were welded by friction and subjected to the tensile strength test. The optimized values obtained by means of these hybrid techniques were compared with the experimental results. The application of hybrid intelligent methods allowed to increase the tensile strength joints from 211 to 258 MPa for the friction welder ZT-14 type.

Developing energy forecasting model using hybrid artificial intelligence method

An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

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.

Modeling and Optimization for Short-term Scheduling of Multipurpose Batch Plants

In the past two decades, short-term scheduling of multipurpose batch plants has received significant attention. Most scheduling problems are modeled using either state-task-network or resource-task-network(RTN) process representation. In this paper, an improved mixed integer linear programming model for short-term schedul-ing of multipurpose batch plants under maximization of profit is proposed based on RTN representation and unit-specific events. To solve the model, a hybrid algorithm based on line-up competition algorithm and linear programming is presented. The proposed model and hybrid algorithm are applied to two benchmark examples in literature. The simulation results show that the proposed model and hybrid algorithm are effective for short-term scheduling of multipurpose batch plants.