Application Of Imperialist Competitive Algorithm Research Articles

Using adaptive neuro-fuzzy inference system and imperialist competitive algorithm for leak detection in pipe networks

Finding the position and quantity of leakage in water distribution networks (WDNs) is challenging in cities with old water pipelines. Previous studies proposed physical and data-driven techniques to find the location and quantity of leakage in WDNs. Most approaches rely on large sample of measurements from the WDN which makes them impractical. We propose an efficient model incorporating Adaptive Neuro-Fuzzy Inference System (ANFIS) and Imperialist Competitive Algorithm (ICA) techniques, to reduce the number of required samples. ANFIS approximates the locations and quantities of leakage, while the ICA corrects its estimations. Due to nonlinearity, the application of ICA alone results in long run times, while ANFIS reduces the number of decision variables for ICA, and hence the convergence rate improves. In other words, we reduce the search space leading to reduced computational time and improved accuracy. Results show that the normalized predicted leakage values had a maximum error rate of 10%.

An application of imperialist competitive algorithm to simulation of energy demand based on economic indicators: Evidence from Iran.

An application of imperialist competitive algorithm to simulation of energy demand based on economic indicators: Evidence from Iran.

Multi-objective optimization of energy use and environmental emissions for walnut production using imperialist competitive algorithm

Although the agricultural sector is an important source of bioenergy production, this production can be considered sustainable when energy consumed and environmental emissions are optimal. As such, the assessment of energy flow, environmental emissions of walnut orchards in Alborz province of Iran and their simultaneous optimization by multi-objective imperialist competitive algorithm are the main goals of this investigation. Input-output energy analysis, IMPACT 2002+ method of life cycle assessment, and multi-objective imperialist competitive algorithm are used in the energy-environmental evaluation for optimization in this study. Results ascertain that energy uses of the entire output and input are computed to be 31,015 and 27200 MJ ha−1, respectively and that gasoline with 40% is the dominated consumer of energy. Moreover, energy use efficiency is 0.88, which indicates energy inefficiency in walnut production. Environmental results shows that On-Orchard emissions with a share more than 50% in ecosystem quality, human health, and climate changes and gasoline in resources category are the main hotspots. Multi-objective optimization illustrates that the reduction in total energy is 19316 MJ ha−1 (about 62%) and gasoline with 58% is the most energy saving input among all. On the other hand, the total weighted emission decreases by about 1.47Pt (about 40%). Generally, results reveal that timely maintenance can help orchardist attain close to optimal condition. Furthermore, the application of imperialist competitive algorithm not only can offer optimum pattern of walnut production, but also be extended to the world for different crops.

An Integral Tilt Derivative Control Strategy for Frequency Control in Multimicrogrid System

The multimicrogrid system is a complicated nonlinear system, which brings performance degradation due to deficient damping under the unexpected fluctuation in power generation due to the presence of renewable sources, dynamically changing loading conditions, and parameter variations. Owing to this, to provide consistent electric power with superlative attribute, sturdy and intelligent control techniques are amazingly imperative in the automatic generation control of microgrid (MG). The application of imperialist competitive algorithm (ICA)-based fractional-order integral proportional derivative with filter (IPDF), i.e., integral tilt derivative with filter controller (ITDF) controller in frequency control in two areas interconnected MG (isolation mode) with renewable penetration, is a novel work. A maiden attempt of the ICA is proposed to optimize the gains of the ITDF controller utilizing the integral time absolute error criterion. To demonstrate the supremacy of ICA, its outcomes are contrasted with two existing optimization strategies, namely the genetic algorithm and the particle swarm optimization. The effectiveness of the proposed controller is revealed by contrasting the dynamic responses of multi microgrid (MMG) with proportional integral derivative with filter (PIDF) and tilt integral derivative with filter (TIDF) controllers. At last, a sensitivity investigation is performed to exhibit the power of the studied strategy to wide variations in the MG parameters, magnitude as well as the location of step/random load disturbance. The proposed MMG is simulated in MATLAB/Simulink environment.

Capacitive Energy Storage (CES) Optimization For Load Frequency Control in Micro Hydro Power Plant Using Imperialist Competitive Algorithm (ICA)

This research will discuss a strategy of frequency control at micro hydro power plant using Capacitive Energy Storage (CES). CES is a device that can store and release energy quickly. To optimize CES performance, proper tuning is required to optimize CES performance. To obtain optimal CES parameter on micro hydro, artificial intelligence method based on Imperialist Competitive Algorithm (ICA) is used. Proportional Integral Derivative Controller (PID) is still a controller that can not be separated from the system, therefore in this research will be combined with CES as the main controller for frequency control on micro hydro. The simulation results show that the application of ICA in optimizing PID-CES parameters, can well improve micro hydro performance. The control models discussed in this research are Proportional Controller (P), Proportional Integral Controller (PI), Proportional Derivative Controller (PD), PID Controller, CES Controller and PID-CES Controller. From the simulation results obtained, P controller overshoot of -0.0001254, with PI Controller -0.000125, with PD Controller -0.0001252, with PID controller -0.0001249, with CES controller -0.0001224, and with PID-CES -1.371e-05. From the results of some of the controller models, it can be concluded that the PID-CES controller proposed in this study has a very significant effect to reduce the frequency oscillation in micro hydro, and it is very suitable to be applied for frequency control at micro hydro.

Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs

Finding optimal designs for nonlinear models is complicated because the design criterion depends on the model parameters. If a plausible region for these parameters is available, a minimax optimal design may be used to remove this dependency by minimizing the maximum inefficiency that may arise due to misspecification in the parameters. Minimax optimal designs are often analytically intractable and are notoriously difficult to find, even numerically. A population-based evolutionary algorithm called imperialist competitive algorithm (ICA) is applied to find minimax or nearly minimax D-optimal designs for nonlinear models. The usefulness of the algorithm is also demonstrated by showing it can hybridize with a local search to find optimal designs under a more complicated criterion, such as standardized maximin optimality.

Application of Imperialist Competitive Algorithm for Optimal Location and Sizing of Static Compensator Considering Voltage Profile

This paper applies the Imperialist Competitive Algorithm (ICA) to find the optimal place and size of Static Compensator (STATCOM) in power systems. The output of the algorithm is a two dimensional array which indicates the best bus number and STATCOM's optimal size that minimizes all bus voltage deviations from their nominal value. Simulations are performed on IEEE 5, 14, and 30 bus test systems. Also some comparisons have been done between ICA and the famous Particle Swarm Optimization (PSO) algorithm. Results show that this method can be considered as one of the most precise evolutionary methods for the determination of optimum compensator placement in electrical grids.

The application of imperialist competitive algorithm for optimization of deposition rate in submerged arc welding process using TiO2 nano particle

We used a novel optimization algorithm based on the imperialist competitive algorithm (ICA) to optimize the deposition rate in the submerged arc welding (SAW) process. This algorithm offers some advantages such as simplicity, accuracy and time saving. Experiments were conducted based on a five factor, five level rotatable central composite design (RCCD) to collect welding data for deposition rate as a function of welding current, arc voltage, contact tip to plate distance, welding speed and thickness of TiO2 nanoparticles coated on the plates of mild steel. Furthermore, regression equation for deposition rate was obtained using least squares method. The regression equation as the cost function was optimized using ICA. Ultimately, the levels of input variables to achieve maximum deposition rate were obtained using ICA. Computational results indicate that the proposed algorithm is quite effective and powerful in optimizing the cost function.

A clustering model based on an evolutionary algorithm for better energy use in crop production

Energy consumption and its negative environmental impacts are of interesting topics in the recent centuries. Agricultural systems are both energy users and suppliers in the form of bio energy and play a key role in world economics as well as food security. A high amount of energy from different sources is used in this sector while researchers who investigated energy flow in crops production especially in developing countries, have reported a high degree of inefficiency. In order to differentiate between efficient and inefficient farms, a clustering model based on imperialist competitive algorithm (ICA) has been developed and the surveyed watermelon farms have been clustered based on three features, i.e. greenhouse gas (GHG) emission, input energy and farm size. The results showed that of the three developed clusters, the best cluster performed 20 and 46 % better than the two other clusters in energy and 22 and 52 % in CO2 emissions. The average of total energy input and GHG emissions for the best cluster were calculated as 43,423 MJ per ha and 8,120 CO2eq. The results of this study demonstrate the successful application of ICA for better use of energy in cropping systems which can lead to a better environmental and energy performance.

Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: A comparative study

One of the major tools for power system operators is Optimal Power Flow (OPF) problem which is designed to optimize a certain objective over power network variables under certain constraints. One of the simplest but most powerful optimization algorithms is imperialist competitive algorithm (ICA) outperforming many of the already existing optimization techniques. The original ICA method often converges to local optima. Therefore, in order to avoid this shortcoming, the interaction effects of colonies on each other are modeled to improve local search near the global optima. Also, a series of modifications is purposed to the assimilation policy rule of ICA method in order to further enhance algorithm’s rate of convergence for achieving a better solution quality. This article investigates the possibility of using recently emerged evolutionary-based approach as a solution for the OPF problems which is based on ICA method with its modified techniques for optimal settings of OPF control variables. The performance of this approach is studied and evaluated on the standard IEEE 57-bus test system with different objective functions and is compared to methods reported in the literature recently. The proposed modified techniques for ICA method provide better results compared to the original ICA and other methods recently reported in the literature as demonstrated by simulation results.

Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem

The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons.

A novel technology for minimizing the synthesis time of nanostructured powders in planetary mills

A key objective in processing of nanostructured powders via high energy ball milling is to minimize the synthesis time. This paper presents the application of imperialist competitive algorithm (ICA) for optimization of milling parameters in order to minimize the synthesis time of nanostructured powders in planetary mills. At first a direct relationship between the inverse of the milling time and the power of the planetary mill was established, which allows the validation of theoretical models proposed in the literature for the energy transfer in milling devices and the comparison of milling equipment efficiencies. Afterwards based on the obtained relation, eight design parameters in milling, namely, number of balls, ball diameter, vial radius, vial height, ball diameter distribution coefficient, plate spinning rate, vial spinning rate and distance between the center of the plate and the center of the vial were optimized. Using these optimized variables in milling process the energy transferred to the raw materials was maximized or in the equivalent expression the synthesis time of nanostructured powders was minimized. At the end a test case was solved to demonstrate the effectiveness and accuracy of the proposed design. Computational results showed that the proposed optimization algorithm is quite effective and powerful in optimizing the planetary mills.

The Application of Imperialist Competitive Algorithm for Fuzzy Random Portfolio Selection Problem

This paper presents an implementation of the Imperialist Competitive Algorithm (ICA) for solving the fuzzy random portfolio selection problem where the asset returns are represented by fuzzy random variables. Portfolio Optimization is an important research field in modern finance. By using the necessity-based model, fuzzy random variables reformulate to the linear programming and ICA will be designed to find the optimum solution. To show the efficiency of the proposed method, a numerical example illustrates the whole idea on implementation of ICA for fuzzy random portfolio selection problem.

Application of imperialist competitive algorithm and neural networks to optimise the volume fraction of three-parameter functionally graded beams

This paper deals with optimisation of three-parameter power-law distribution of functionally graded (FG) beam. The main goal of the optimisation problem is to determine the optimum volume fraction relation for maximising the first natural frequency of FG beam. Since the search space is large, the optimisation processes become very complicated and too time consuming. Thus, a novel meta-heuristic called imperialist competitive algorithm (ICA), which is a socio-politically motivated global search strategy is applied to find the optimal solution. Applying the proposed algorithm to some of benchmark cost functions, it shows its ability in dealing with different types of optimisation problems. A proper and accurate artificial neural network (ANN) is trained by training data sets obtained from generalised differential quadrature method and then is applied as the objective function in ICA. The ANN improves the speed of optimisation process by a considerable amount by reproducing the fundamental natural frequency of the structure. The performance of ICA is evaluated in comparison with other nature-inspired technique genetic algorithm. Comparison shows the success of combination of ANN and ICA for design of material profile of beam. Finally the optimised material profile for the optimisation problem is presented.

Application of Imperialist Competitive Algorithm to Solve Constrained Economic Dispatch

In the constrained dynamic ED problem, considering some practical operation constraints of generators, such as ramp rate limits and prohibited operating zones, electric power generation of units are scheduled. So, this paper, considering these constraints, presents a new optimization technique based on Imperialist Competitive Algorithm (ICA) to solve the Economic Dispatch (ED) problem in power systems. To show the efficiency of the proposed method, this algorithm is applied to solve constrained dynamic ED problem of two power systems with 6 and 15 units. The results are compared to those achieved from conventional methods such as Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results show that the proposed method is capable to determine the solutions of ED problem more fast and accurate than other conventional approaches.

An imperialist competitive algorithm for optimal design of plate-fin heat exchangers

This paper presents the application of imperialist competitive algorithm (ICA) for optimization of a cross-flow plate fin heat exchanger. Minimization of total weight and total annual cost are considered as objectives. Seven design parameters, namely, heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length and number of hot side layers are selected as optimization variables. A case study from literature is presented to show the effectiveness of the proposed algorithm. The numerical results reveal that ICA can find optimum configuration with higher accuracy in less computational time when compared to conventional genetic algorithm (GA).

Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification

selection plays an important role in pattern recognition. The better selection of a feature set usually results the better performance in a classification problem. This work tries to select the best feature set for classification of rice varieties based on image of bulk samples using imperialist competition algorithm. Imperialist competition algorithm is a new evolutionary optimization method that is inspired by imperialist competition. Results showed the feature set selected by the imperialist competition algorithm provide the better classification performance compared to that obtained by genetic algorithm technique

Application of Imperialist Competitive Algorithm for Automated Classification of Remote Sensing Images

  Abstract—Recently, a novel evolutionary global search strategy called Imperialist Competitive Algorithm (ICA) has proven its superior capabilities in optimization problems. This paper presents an application of ICA in automated clustering of remote sensing images. The proposed algorithm is basically a hierarchical two-phase process. At the first phase the original data set is decomposed into water bodies and land cover classes using near Infrared band's information. At the second phase, ICA has been applied to determine the number and centers of the land cover clusters using RGB band's information during an unsupervised clustering. The optimization is based on Fuzzy C-Means and an additional term for improving the accuracy of clustering. The method is applied on pan-sharpened IKONOS images of Tehran and 4 artificial data sets with different properties. Results obtained from applying the proposed method for both artificial data sets and RS image, indicate promising ability of this method in clustering data with unknown cluster number. Also the results show that the achieved overall accuracy can be available, better than 78% in comparison with other applied methods.

The Application of Imperialist Competitive Algorithm based on Chaos Theory in Perceptron Neural Network

Abstract In this paper, the weights of a Neural Network using Chaotic Imperialist Competitive Algorithm are updated. A three-layered Perseptron Neural Network applied for prediction of the maximum worth of the stocks changed in TEHRAN's bourse market. We trained this neural network with CICA, ICA, PSO and GA algorithms and compared the results with each other. The consideration of the results showed that the training and test error of the network trained by the CICA algorithm has been reduced in comparison to the other three methods.

Application of imperialist competitive algorithm for optimizing a thin resistant interphase

The Imperialist Competitive Algorithm (ICA) is used in optimal design of a thin resistant interphase layer for several temperature distributions on a specimen. In applications of interphase layers, the components of thickness and heat source are very important and they are assigned as multi-objective optimization design functions. The results of ICA are then compared with Finite Element Method (FEM) and another optimization method, the Genetic Algorithm (GA). The ICA results show good performance in optimal design of thin adhesive layers.