imperialist competitive algorithm (ica)

Due to the special position of flow shop scheduling systems in production centers, these issues have received a lot of attention in recent years. One common assumption in these cases is the availability of machines on the planning horizon. In real industrial environments, a machine may be temporarily unavailable for reasons such as the need for preventative maintenance. Due to the importance of this issue, in the present study the flexible flow shop scheduling problem with reverse flows is investigated by considering the activity of preventive maintenance in which there are two flows of jobs (direct and reverse) that cover the same machines in opposite directions. An essential issue for modeling the flexible flow shop-scheduling problem is to consider the limitations of access to machines in order to perform preventive maintenance. The maintenance operation on each machine has a fixed duration and its beginning and end occur in a certain time window. For the mentioned problem, a Mixed Integer NonLinear Programing (MINLP) model is presented. The objective of this model is to minimize the maximal completion time of the jobs (i.e., the ma kespan). Due to the complexity of the model and the NP-hardness of the proposed problem, the Imperialist Competitive Algorithm (ICA) is proposed to solve large-scale problems. In order to evaluate the performance of the proposed algorithm, numerical sample problems in different sizes are solved using this algorithm, the General Algebraic Modeling System (GAMS) software as well as the genetic algorithm. Computational results demonstrate the effectiveness of the Imperialist Competitive Algorithm for the considered problem.

Determining the internal structure of objects such as identification of impurities, estimation of mechanical properties and identification of internal boundaries and cavities is one of the important issues in industries. Objective of the present article is to investigate the effect of the mechanical properties and the effect of the location of the interfacial boundary in solving the inverse problem of identifying the internal boundaries of a Non- homogeneous body, consisting of two homogeneous bodies, along with the estimation of the modulus of elasticity and Poisson's ratio of its components. The solution has been done using the combination of the numerical method of boundary elements method along with the optimization method of the Imperialist Competitive Algorithm (ICA) and the simplex method. The obtained results, show the effectiveness of the ICA optimization and the simplex method in estimating the mechanical properties and identifying the interfacial boundary configurations. But, as the boundary approaches the lower part of the material and the mechanical properties approach, the boundary estimation error increases to 8.65% for the solved examples, which again indicate the strength of this method to estimate the boundary. Also, in the investigation of the effect of unavoidable errors in the measurement of displacements, with the increase in the error percentage, the convergence to the real boundary is reduced, so that for error greater than 5%, the geometry of the internal boundaries converges to unrealistic boundaries and the calculations are no longer valid.

One of the applications of optimization methods is solving the inverse problems of identifying internal boundaries, estimating the mechanical properties of materials and etc. In most of the articles, due to the simplicity of the relationships, the cases where the material is a homogeneous body have been considered, But in industry, when two molten material are combined together, there is a possibility that the resulting material is non-homogeneous material.In this article, identifying the geometry of the irregular internal boundaries between three materials and estimating the mechanical properties is presented. The intermediate material has a variable modulus of elasticity. This problem has been studied by combining three methods of colonial competition algorithm, simplex and conjugated gradian method along with the numerical method of Boundary Elements Method. The effect of the type and hardness of the constituent materials of the non-homogeneous body and the effect of the geometry and position of the internal boudaries on the convergence have been investigated. The obtained results indicate the power and ability of the presented method to estimate unknown parameters.

Secret sharing refers to methods of distributing a secret amongst a group of participants, each of whom is assigned with a share of the secret. The secret can be reconstructed only when a sufficient number, of possibly different types of shares are combined together. Different secret sharing methods have been      presented, such as secret sharing schemes based on dominating set and edge dominating set. In edge     dominating set method, it is required that all of the edge dominating sets are obtained for the graph, which is a NP-complete problem. All of the edge dominating sets can be easily obtained, using tree decomposition of the graph and dynamic programming. Although generating tree decomposition of a graph with finite treewidth can be solved in polynomial time, but it is shown to be NP-complete for general graphs. In this paper, to generate tree decompositions of general graphs, we use the notion of Imperialist Competitive   Algorithm (ICA) which can be applied in parallel. Therefore, the proposed method, in addition to being a new method for implementation of the secret sharing scheme, can reduce runtime by up to five percent in parallel.

In this paper, a new approach based on imperialist competitive algorithm (ICA) has been proposed to solve the combined heat and power economic dispatch (CHPED) problem. In order to avoid trapping in local optimum and improve the solution quality of the original ICA, a new assimilation policy has been addressed with varying coefficients during iterations. CHPED problem is a non-convex and non-linear optimization problem which has various constraints. Unlike previous methods, valve point effects are considered in some case studies and the effect of valve-point in cost function considered with adding an absolute sinusoidal term to conventional polynomial cost function. To evaluate the effectiveness of the proposed method, three different test cases with small, medium and large scales have been applied to investigate the performance of the proposed method on the CHPED problems. Each case study is including different test systems. Numerical results demonstrate the superiority of the proposed framework and reveal that MICA can find better solutions in comparing with the other methods.   Keywords:

Renewable energies are used by micro grid (MG) to produce electrical power. Scatered loads receive service from MG in both Island and Grid connected operation modes. Uncertainties of power system and natural deviations of power produced by renewable energies attenuate the performance of classic controllers. In this paper, fuzzy system determines the parameters of PI controller in response to this challenge. The Imperialist competitive algorithm (ICA) optimizes the nominal values of controller parameters and the membership functions of the proposed fuzzy system. The interval of the proposed controller parameters are determined by Kharitonov's theorem to ensure the stability of the closed loop system. Comparison of RMS, overshoot and undershoot, number of oscillations, and settling time of the frequency deviations of the proposed control scheme with two other controllers in several load changes demonstrates the better performance of the proposed controller. Considering the uncertainties of system parameters proves that the proposed controller is robust.

Imperialist Competitive Algorithm (ICA) is considered as prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA, at run time, the suggested method (ACICA) finds the optimum number of clusters while optimal clustering of the data simultaneously .To increase the accuracy and speed of convergence, the structure of ICA changes. The proposed algorithm requires no background knowledge to classify the data. In addition, the proposed method is more accurate in comparison with other clustering methods based on evolutionary algorithms. DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method, the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic clustering algorithms based on evolutionary algorithms.

Identification of the thermal conductivity of a functionally graded material (FGM) is considered as an inverse heat conduction problem. ýIn this investigation, the measurements of the temperatures on the portion of the 2D body where heat flux is specified as the boundary ýcondition and/or the heat flux on the portion of the boundary where temperature is specified as the boundary condition are used as ýadditional data needed to identify the thermal conductivity of the FGM domain in an inverse procedure. The thermal conductivity is ýapproximated as a quadratic function of only one direction, and therefore three constant coefficients should be estimated simultaneously.ýþ þThe solution of the direct heat conduction problem forþ þFGM domain is obtained using the boundary elements method (BEM).ýþ þThe ýimperialist competitive algorithm (ICA) which is an evolutionary and meta-heuristic global optimization is used to identify the constants ýin the thermal conductivity function of the quadratic FGM. An inverse computer code is developed which employs the boundary ýtemperature and heat flux measurements data obtained by solving the direct boundary elements code with known thermal conductivity. ýTo show the feasibility and effectiveness of the developed inverse code, a number of example problems are solved and results are ýverified.ý

Microgrids (MG) are a branch of distributed energy sources, which often use renewable energies to produce electrical power and gives service to scattered loads in Island and Grid connected operation modes. Because of the uncertainties of power system and natural variations of the power produced by renewable energies, in this paper, fractional order PID (FOPID) is used to control the frequency of the microgrid. The output of a fuzzy system is the input of the FOPID controller which results in Fuzzy fractional order PID (FFOPID). Imperialist competitive algorithm (ICA) determines the optimal values of the controller parameters. Comparison of the proposed FFOPID with FOPID and classical PID controllers in several load changes scenarios shows a better performance of the proposed controller in terms of RMS, overshoot and undershoot, number of oscillations, and settling time of the frequency deviations. Simulations indicate the robust performance of the proposed controller against the changes of system parameters as well.

One of the basic components of future distribution networks are renewable energy resources (RER). The uncertainty in power production of renewable resources such as wind and solar as well as load is another characteristic of such networks. Conventional power flow methods as a basic tool for power system studies may not be suitable for active distribution networks such as microgrids. In this paper a heuristic load flow method considering the effects of intermittent behavior of RERs and load is modeled in probabilistic load flow algorithm. The method is suitable for both radial and weakly meshed distribution networks with RER for operation and planning of microgrids. Imperialist competitive algorithm (ICA) as heuristic based optimization algorithm is applied to formulate and solve the Probabilistic Load Flow (PLF). Based on PLF technique calculated parameters of the system such as bus voltages and feeders'' current¡ are extracted as random variables. A modified version of conventional IEEE 33-bus test system with RER is used as a weakly meshed network to evaluate efficiency and capability of the algorithm. Results are compared with Monte Carlo Simulation (MCS) method. The PDF and CDF of bus voltages¡ power of feeders'' and network total loss are compared. Based on the results the presented approach can solve the PLF problem regardless of the type of distribution network.

This paper presents a new method for distribution system planning considering load uncertainty that the obtained network is robust to the load forecasting uncertainty. At first a method for optimal network planning ignoring load uncertainty has been presented and then by mixing this method with Monte Carlo Method, a novel method has been presented for planning distribution system considering load uncertainty. Supposing that the location and size of HV substations are obtained in another work, the optimal route of MV feeders are obtained through the new Imperialist Competitive Algorithm (ICA) developed for the optimal expansion planning of distribution network. In order to check the radial structure of the network obtained in any iteration of ICA, a novel mathematical algorithm is employed. For the scenario with considering load uncertainty, optimal network and the histogram figures for some electrical parameters of the optimal network are obtained. According to the results, the obtained optimal network is robust to the load uncertainty.

One of the most important issues in industry, particular casting industry is to determine the internal structure of objects such as identifying the interfacial boundary configurations between material, identification of impurities or mechanical properties of the material. The objective of the present inverse problem is to identified simultaneously two regular interfacial boundary configurations and mechanical properties of the components of a multiple (three) connected domains using a discrete number of displacement measurements obtained from an uniaxial tension test. A unique combination of a global optimization method i.e. the Imperialist Competitive Algorithm (ICA) and local optimization methods i.e. Simplex Method (SM) along with the inverse application of the Boundary Elements Method (BEM) are employed in an inverse software package. A fitness function, which is the summation of squared differences between the measured displacements and computed at identical locations on the exterior boundary, is minimized. The obtained results (run-time and error-rate), clearly demonstrate the efficiency of this present algorithm (the Imperialist Competitive Algorithm and Simplex Method) to optimize the objective function and the estimation simultaneously two regular interfacial boundary configurations and mechanical properties.