World Cup Optimization Algorithm

A new core sampler was developed. Its sampler pipes are made of acrylic acid resin to be convenient to examine the stratification of a core. A fur of seal is pasted inside the sampler cutter in order to prevent the loss of sample when the sampler is pulled up. The sampler joint was also deviced to be suitable for jointing the sampler pipes. The sampler was used for the measurement of water equibalence of snow covers and for stratigraphic observations within them. All devices were successful. It was observed that the amount of contraction of a core depended on snow form and sampling procedure, and the value of the ratio of the snow depth to the core length varied from 1. 04 to 1. 35. In order to reproduce the stratification within a snow cover from that in a core, it is suggested that the height of an interface within the snow cover is, as an approximation, estimated from multiplying that of the corresponding interface by the value of the ratio of the snow depth to the core length. It is shown that the error of this estimated height is less than 8. 4% of the snow depth. A method for the improvement on this error is suggested. The new devices for the sampler are applicable to core samplers used for other deposits as well as snow cover.

This paper presents a new optimization algorithm based on human society's intelligent contests. FIFA World Cup is an international association football competition competed by the senior men's national teams. This contest is one of the most significant competitions among the humans in which people/teams try hard to overcome the others to earn the victory. In this competition there is only one winner which has the best position rather than the others. This paper introduces a new technique for optimization of mathematic functions based on FIFA World Cup competitions. The main difficulty of the optimization problems is that each type of them can be interpreted in a specific manner. World Cup Optimization (WCO) algorithm has a number of parameters to solve any type of problems due to defined parameters. For analyzing the system performance, it is applied on some benchmark functions. It is also applied on an optimal control problem as a practical case study to find the optimal parameters of PID controller with considering to the nominal operating points (K g , T g ) changes of the AVR system. The main objective of the proposed system is to minimize the steady-state error and also to improve the transient response of the AVR system by optimal PID controller. Optimal values of the PID controller which are achieved by WCO algorithm are then compared with particle swarm optimization and imperialist competitive algorithm in different situations. Finally for illustrating the system capa-B Navid Razmjooy

Synoptic-scale meteorological conditions causing the long-range transport of air pollutant with light gradient winds were investigated by analyzing the atmospheric pressures and ground-level winds as well as the aerological data for the period from May to September 1979. Special attention was paid to the thermal low which was generated in the daytime in the mountainous region, and the characteristics of the upper atmosphere. Long-range transport occurred under a synoptic-scale high pressure with an upper wind of about half the wind speed averaged over the entire period under study. Under these conditions, local winds developed in the lower atmosphere, and the thermal low and subsidence inversion accompanied by the synoptic-scale high pressure led to the combination of the local winds into two large-scale winds blowing towards the thermal low. The synoptic-scale pressure distribution determines the location of the thermal low center and therefore also determines the horizontal extent of the long-range transport.

Observation of air temperatures was conducted simultaneously in urban and suburban areas in Tokyo for the validation of a one-dimensional urban canopy model. The observation results showed that the distribution of daytime temperature was inhomogeneous due to the presence of insolation, while temperature at nighttime was not remarkably different among the measurement points. Averaged temperature in suburban area tends to be higher than that of urban area during daytime. However, at nighttime, temperature in urban area was about 1 degree higher. This suggests that, during our measurement period, typical heat island phenomenon occurred. Using these observed results, we evaluated numerical model performance. The performance was largely affected by solar radiation used in the model, that is, calculated daytime temperature was under estimated as solar radiation was obtained from GPV of JMA-RSM. On the other hand, the performance was fairly improved using solar radiation obtained form sunshine percentage data provided by AMeDAS sites.

Designing of a PID controller is a very common method for industrial process control and due to its very simple and efficient function; it is used in a wide variety of industrial applications. PID controller to reduce the steady state error and dynamic response of the system is used. PID controller design is an inevitable problem in setting the coefficients need to try a lot of trial and error, therefore the optimization of parameters in this controller is attention of many researcher and there are many methods to find optimal parameters of PID controller. Fast and exactly adjustment of the parameters optimized controller is to create high quality answers. In this paper, an optimized tuning method for PID controller is presented. In this method the PSO algorithm is used to design the parameters of an AVR (Automatic Voltage Regulation) system using various fitness functions. Easy implementation, stable convergence characteristic and high computational efficiency are among advantages of presented method.

Kimura, F. and Takahashi, S. (1991): The effects of land-use and anthropogenic heating on the sur face temperature in the Tokyo metropolitan area. Atmospheric environment, 25B, 155-164. Kondo, H. (1990): A numerical experiment of the "Extended Sea Breeze" over the Kanto plain. J.

In this paper optimal design of Proportional-Integral-Derivative (PID) controller parameters in an Automatic Voltage Regulator (AVR) system has been considered. In this study, many performance criteria used in evaluating performance of the PID controller are employed to the proposed AVR system. This criteria are include system responsibility rate, settling time of system response, disappear overshoot and undershoot in system response and important of all system stability. Optimization algorithm namely Imperialistic Competition (ICA) is applied for determining the optimal PID controller parameters in an AVR system. For simulate system we used m-file of matlab 2011 software. Finally, simulation result simulated annealing algorithm (SA) is compared with proposed method in order to show the efficiency of ICA. The results analysis reveals that the ICA based PID type AVR control system is effective and provides good terminal voltage oscillations damping ability. Moreover, it is superior that of the other SA method in terms of accuracy, convergence and computational effort.

Feature selection is used in machine learning as well as in statistical pattern recognition. This is important in many applications, such as classification. There are so many extracted features in these applications which are either useless or do not have much information. If not removing these features, make raises the computational burden for the main application. In different methods of feature selection, a subset is selected as the answer, which can optimize the value of an evaluation function. In this study, a new algorithm for classification of Dermoscopy images into two types of malignant and benign are presented. To develop the general skin cancer detection system, at first a pre-processing step is applied to enhance image quality. Then the lesion area is removed from the healthy areas using the Otsu threshold method. Nine shape feature and nine color features are extracted from the segmented image using different optimization schema. At the end of the operation, classification was done by SVM, KNN and Decision Tree methods. The results show that combination of buzzard optimization algorithm for feature extraction and SVM classifier accuracy is 94.3%. This result shows the high potential of buzzard optimization algorithm for feature extraction.

One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN). World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In th...

A comparative study is performed to design an optimal PID controller for an automatic voltage regulator system using different optimization techniques. The presented approaches are referred to as particle swarm optimization (PSO) algorithm, cuckoo search optimization (CSO) algorithm, moth flame optimization (MFO) algorithm, water cycle optimization (WCO) algorithm, teaching-learning-based optimization (TLBO) algorithm, and hill climbing optimization (HCO) algorithm. Transient response parameters, which are rise time Tr, settling time Ts, and percentage overshoot Mp, are used as comparison criteria. The integral time absolute error ITAE is the used performance index. All the proposed optimization techniques improved the transient response of the AVR system in a different way and gave good preliminary results.

Abstract: This paper presents dynamic modelling and control of a linear prismatic series of elastic actuator. The capability of generating large torques is why this actuator is increasingly used in human-assistive robotic systems. Due to having human in the loop, the actuator requires precise control. A fractional PID controller known for its improved performance is used for the control, due to having additional degrees of freedom than the classical PID. The actuator has one servo driver and five controller gains to be tuned. The gains are optimized using both Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithms (ICA). Comparison of the results from the two optimization methods illustrates that the PSO tuned FOPID controller has a slightly better performance, faster convergence and better settling time. Next, the PSO tuned controller is compared with a Genetic Algorithm (GA) tuned PID controller. It is shown that the PSO tuned FOPID controller continues to offer ...

In this present study, a metaheuristic-based algorithm named ant lion optimizer (ALO) is performed combined with eigenvalue obtained from numerical simulation of the composite beam for detection of the damage. Fibre reinforced plastic (FRP) composite beams have been modeled using finite element method. Free vibration analysis of the beam is carried out for both undamaged and damaged beam to acquire the modal data such as natural frequencies and mode shapes, which is the baseline data used in the ALO algorithm in MATLAB. Since, damage in any structure causes loss of stiffness, in the present study, the damage is induced in the beam by reducing the stiffness of the beam, which is achieved by reducing the thickness of the beam at specified locations. The ALO algorithm minimizes the differences between the vibrational responses of the baseline model and those acquired from the finite element models of the composite structures. The objective function based on frequencies of the composite beam is formulated. It is observed that the ALO algorithm is satisfactory to detect the location and severity of the damage.

In the generator set (genset), the voltage stability system is affected by the excitation system controlled by control circuit called AVR (Automatic Voltage Regulator). One of the important components in the AVR system is the algorithm of the controller. The application of the PID control method has been widely used in the design of AVR controllers. This study applies the GWO-GA (Grey Wolf Optimization - Genetic Algorithm) hybrid method on PID parameters setting. The best transient automatic voltage regulator (AVR) response results were obtained when using the hybrid genetic algorithm - grey wolf optimization (HGAGW) method with a fitness score of 4.3039, the Grey wolf optimization (GWO) method with a fitness score of 4.5059, and the genetic algorithm (GA) method with a fitness score of 6.0214.

Skin cancer is the most common cancer of the body. It is estimated that more than one million people worldwide develop skin cancer each year. Early detection of this cancer has a high effect on the disease treatment. In this paper, a new optimal and automatic pipeline approach has been proposed for the diagnosis of this disease from dermoscopy images. The proposed method includes a noise reduction process before processing for eliminating the noises. Then, the Otsu method as one of the widely used thresholding method is used to characterize the region of interest. Afterward, 20 different features are extracted from the image. To reduce the method complexity, a new modified version of the Thermal Exchange Optimization Algorithm is performed to the features. This improves the method precision and consistency. To validate the proposed method's efficiency, it is implemented to the American Cancer Society database, its results are compared with some state-of-the-art methods, and the ...

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and view of potato from digital camera. In the proposed algorithm, after selecting appropriate color space, distance between an image pixel and real potato pixels is computed. Furthermore, this distance feeds to a fuzzy rule-based classifier to extract potato candidate in the input image. A subtractive clustering algorithm is also used to decide on the number of rules and membership functions of the fuzzy system. To improve the performance of the fuzzy rule-based classifier, the membership functions shapes are also optimized by the GA. To segment potatoes in the input color image, an image thresholding is applied to the output of the fuzzy system, where the corresponding threshold is optimized by the GA. To improve the segmentation results, a sequence of some morphological operators are also applied to the output of thresholding stage. The proposed algorithm is applied to different databases with different backgrounds, including USDA, CFIA, and obtained potato images database from Ardabil (Iran's northwest), separately. The correct segmentation rate of the proposed algorithm is approximately 98% over totally more than 500 potato images. Finally, the results of the proposed segmentation algorithm are evaluated for some images taken from real environments of potato industries and farms.

This paper presents a novel method of skin diseases classification. The complete work is divided into four parts. First is preprocess the image then segment the image by using modified sobel edge detection technique, and extract the features of the segmented image, extracted features are sub divided in to sub space features and calssified the features by artificial Neural Network(ANN). The performance of the different training algorithm has been investigated. Mean Square Error (MSE) is evaluated. Bayesian regularization backpropagation algorithm gives minimum MSE is 4.8561e-13 and gradient is 1.6337e-08 at 190 epochs. Levenberg-Marquardt backpropagation algorithm provides MSE 1.0559e-10 and gradient is 9.9001e-08 at 105 epochs. Resilient backpropagation algorithm 3.5354e-07 and gradient is 8.5468e-06 at 347 epochs. Scaled conjugate gradient backpropagation algorithm give MSE 0.02269 and gradient is 8.6124e-07 at 115 epochs.

Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer...

In this paper, an efficient numerical approach is developed for solving the baseline problem with interval uncertainties. Interval arithmetic is also utilized for developing the proposed method in the presence of uncertainties with only lower and upper bounds of parameters. In the proposed method, the equation of motion, performance index, and boundary conditions are first changed into some algebraic equations. This process converts the problem into an optimization problem. The presented technique is based on an interval extension of Chebyshev polynomials where its coefficients are achieved by world cup optimization algorithm, as a new optimization algorithm. The proposed method approximates the control and state variables as a function of time. The proposed solution is based on state parameterization, such that the state variable is approximated by the proposed interval Chebyshev polynomials with unknown interval coefficients. Finally, by solving the baseline problem in the presence of interval uncertainties, the reliability and effectiveness of the proposed method are demonstrated.

This paper has been devoted to finding optimal construction of advanced functionally graded steels (FGSs) in order to achieve the best compromise between strength and ductility. FGSs have been recently produced via diffusion of the alloying elements during electroslag remelting, which results in a variety of multi-phase composites containing ferrite, bainite, martensite, and austenite. Due to the correlation enforced between high strength and poor formability for conventional high strength steels, applying a Pareto-based multi-objective optimization technique is inevitable. The tensile strength and the energy absorption capacity of FGS were considered as fitness functions which should be maximized simultaneously. The tensile strength of FGS was related to the tensile behavior of individual phases by means of the rule of mixtures. Also, the total area under the stress-strain curve was evaluated to quantify the energy absorption capacity. It was assumed that the spatial distribution of the constituents in the graded regions of FGS obeys power functions, including parameters that were chosen as decision variables. A multi-objective ant lion optimizer algorithm (MOALO) was extended and implemented to find a set of Pareto optimal solutions for a five-layer FGS. The results highly demonstrate the efficiency of the proposed MOALO algorithm and its applicability for the excellent approximation of the optimal features of FGSs.

In this paper, a new interval version of Runge-Kutta methods is proposed for time discretization and solving of optimal control problems (OCPs) in the presence of uncertain parameters. A new technique based on interval arithmetic is introduced to achieve the confidence bounds of the system. The proposed method is based on the new forward representation of Hukuhara interval difference and combining it with Runge-Kutta method for solving the OCPs with interval uncertainties. To perform the proposed method on OCPs, the Lagrange multiplier method is first applied to achieve the necessary conditions and then, using some algebraic manipulations, they are converted to an ordinary differential equation to achieve the interval optimal solution for the considered OCP with uncertain parameters. Shooting method is also employed to cover the Runge-Kutta methods restrictions in solving the OCPs with boundary values. The simulation results are applied to some practical case studies for demonstrating the effectiveness of the proposed method.

One of the most dangerous cancers in humans
is Melanoma. However, early detection of melanoma can
help us to cure it completely. This paper presents a new
efficient method to detect malignancy in melanoma via
images. At first, the extra scales are eliminated by using
edge detection and smoothing. Afterwards, the proposed
method can be utilized to segment the cancer images.
Finally, the extra information is eliminated by morpho-
logical operations and used to focus on the area which
melanoma boundary potentially exists. To do this, World
Cup Optimization algorithm is utilized to optimize an MLP
neural Networks (ANN). World Cup Optimization algo-
rithm is a new meta-heuristic algorithm which is recently
presented and has a good performance in some optimi-
zation problems. WCO is a derivative-free, Meta-Heuris-
tic algorithm, mimicking the world’s FIFA competitions.
World cup Optimization algorithm is a global search algo-
rithm while gradient-based back propagation method is
local search. In this proposed algorithm, multi-layer per-
ceptron network (MLP) employs the problem’s constraints
and WCO algorithm attempts to minimize the root mean
square error. Experimental results show that the pro-
posed method can develop the performance of the stand-
ard MLP algorithm significantly.

This paper presents a new optimization algorithm
based on human society’s intelligent contests. FIFA
World Cup is an international association football competition
competed by the senior men’s national teams. This
contest is one of the most significant competitions among
the humans in which people/teams try hard to overcome the
others to earn the victory. In this competition there is only
one winner which has the best position rather than the others.
This paper introduces a new technique for optimization
of mathematic functions based on FIFA World Cup competitions.
The main difficulty of the optimization problems
is that each type of them can be interpreted in a specific
manner. World Cup Optimization (WCO) algorithm has a
number of parameters to solve any type of problems due to
defined parameters. For analyzing the system performance,
it is applied on some benchmark functions. It is also applied
on an optimal control problem as a practical case study to
find the optimal parameters of PID controller with considering
to the nominal operating points (Kg, Tg) changes of
the AVR system. The main objective of the proposed system
is to minimize the steady-state error and also to improve
the transient response of the AVR system by optimal PID
controller. Optimal values of the PID controller which are
achieved byWCOalgorithm are then compared with particle
swarm optimization and imperialist competitive algorithm in
different situations. Finally for illustrating the system capability against the disturbance, it is applied on a generator with
disturbance on it and the results are compared by the other
algorithms. The simulation results show the excellence of
WCO algorithm performance into the nature base and other
competitive algorithms.

One of the most dangerous cancers in humans
is Melanoma. However, early detection of melanoma can
help us to cure it completely. This paper presents a new
efficient method to detect malignancy in melanoma via
images. At first, the extra scales are eliminated by using
edge detection and smoothing. Afterward, the proposed
method can be utilized to segment the cancer images.
Finally, the extra information is eliminated by morpho-
logical operations and used to focus on the area in which the melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize MLP
neural Networks (ANN). World Cup Optimization algo-
rithm is a new meta-heuristic algorithm which is recently
presented and has a good performance in some optimi-
zation problems. WCO is a derivative-free, Meta-Heuris-
tic algorithm, mimicking the world’s FIFA competitions.
World cup Optimization algorithm is a global search algo-
rithm while gradient-based back propagation method is
local search. In this proposed algorithm, multi-layer per-
ceptron network (MLP) employs the problem’s constraints
and WCO algorithm attempts to minimize the root mean
square error. Experimental results show that the pro-
posed method can develop the performance of the stand-
ard MLP algorithm significantly.

Image segmentation has been widely used in different applications of the image processing. The main objective of image segmentation is to subdivide the input images to their main components. Generally, the main purpose of the segmentation is to simplify or change an image representation into something that is more meaningful and easier to analyze. In this paper, World Cup Optimization Algorithm (WCO) is proposed to classify the main components of an image (pixels) into different groups. In the experiment, the proposed method performance is measured by comparing with Otsu as a classic method and GA based and APSO based image segmentation algorithms as the heuristic based algorithms for segmentation. When compared with the other segmentation methods, the proposed WCO based method achieved good performance. The final efficiency of the proposed system is compared with the described methods. Experimental results show that the proposed method has overcome the others in the performance.

In this paper a new evolutionary computing method based on Imperialist Competitive Algorithm (ICA) is used for tuning the elements of a PID controller which is applied in a Multi Area Load Frequency Control System (MA-LFC). If a large power imbalance is suddenly happened in a multi area power electric system, generation units and also consumer sides will be affected by the distortion in the energy balance between both two sides. This inequality is firstly handled by the kinetic energy of the system turning components, but, eventually, the frequency will change. Therefore, LFC is considered as one of the most challenging issues in power system control and operation. PID type controllers are conventional solutions for MA-LFC. The three parameters of the PID controllers have been adjusted traditionally. In this paper, a PID controller is applied for the MA-LFC problem and then its parts are modified by using ICA method. To validate the application of the technique, a multi area network with some uncertainties is provided. Finally the results of the ICA-PID controller are compared with the ones of GA optimized controllers. The simulation results show the success and the validity of the ICA-PID controller in compare with the GA -PID controller.