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In this paper we present an application of two Computational Intelligence Algorithms, namely Particle Swarm Optimization (PSO) Algorithm and Differential Evolution (DE) for finding and optimal solution to two optimization problems that occur in a paper industry. The first problem deals with the economic optimization of a hypothetical but realistic Kraft pulping process and in the second problem we have considered the optimization of Boiler load allocation problem. Both the problems form an integral part of paper making process. The simulation results show the efficiency and time effectiveness of DE and PSO. [ABSTRACT FROM AUTHOR]

The paper considers a new approach to the identification of dynamic objects in the form of the first-order differential equations system. The peculiarities of the proposed approach are the absence of restrictions on the structure of the differential equations included in the system and the symbolic representation of the obtained solution. The proposed approach includes a self-configuring genetic programming algorithm for selecting the structure of differential equations. The number of algorithms corresponds to the number of equations in the system. The method of differential evolution is used to optimize numeric constants. The authors apply a self-configuring procedure for the indicated evolutionary algorithms. The proposed approach has been tested on a variety of problems. The accuracy of the obtained solution has been investigated depending on the presence of noise in the sample data. [ABSTRACT FROM AUTHOR]

One of the steps in building decision trees is to select a splitting attribute at each node. The quality of the classification depends on this selection. Classical decision tree learning algorithms, such as ID3 and CART, use exhaustive search over the entire attribute space. However, it is a very time-consuming process. The calculation of the objective function is performed for all objects according to all characteristics. The early studies proved that the efficiency of the hybridization of the attribute selection method and differential evolution in decision tree learning. It will help significantly speed up the learning process of a decision tree without losing the quality of classification. However, the research related to the selection of an attribute selection method has not been conducted. The authors of the paper compare nine of the most popular methods for splitting attribute selection. The attribute is of different complexity. Some methods use knowledge only about attributes; others use knowledge about class labels. The comparison was carried out while solving some classification problems. The authors selected decision tree learning time and classification accuracy were chosen as performance criteria. The estimation of methods for attribute selection is carried out as an average indicator for all classification problems. [ABSTRACT FROM AUTHOR]

Dc motors represent linear systems up to point of saturation. In this paper, optimized tuning of DC motors has been discussed with the help of different meta-heuristic algorithms. The model of the DC motor is basically a third-order system. Dc motors, that are used in different industrial applications including conveyors, turntables, and other places where adjustable speed and constant or low-speed torques are required, owing to their simple configuration. They also find its application in dynamic braking and reversing applications as well. Here, in this paper Genetic Algorithm, Differential Evolution, Teaching Learning Based Optimization, Particle Swarm Optimization with different performance indices (Mean Square Error and Integral time absolute error) is compared with the standard Ziegler & Nichols method. Comparison of results using standard step parameters i.e., maximum overshoot, steady-state, rise time and peak time, etc. is being discussed. [ABSTRACT FROM AUTHOR]

Physical analysis of problems of engineering dynamics leads typically to hyperbolic systems of partial differential equations of evolution of 2nd order with some nonlinear terms, supplied with Dirichlet and Neumann boundary conditions together with some interface ones and with Cauchy initial conditions. Their numerical treatment needs coupling the finite element (or similar) method with the method of discretization in time. The preference of distributed and parallel computations for large problems, e. g. of multiple contacts of moving deformable bodies, stimulates the analysis of convergence and stability properties of explicit integration schemes, as simple, effective and robust as possible. This paper demonstrates such research direction, significant for practical calculations, on the conditional stability of a model simple explicit algorithm, motivated by the central difference method, implemented ad hoc e. g. in the LS-DYNA software package. [ABSTRACT FROM AUTHOR]

Curve reconstructions are widely been used in reverse engineering problems. The reverse engineering process is the creation process of computer model from its original real-life model or its prototypes. The purpose of the reverse engineering is to improve the visualization of two-dimensional data from a series of data point. This paper presents a curve fitting of cubic Bézier curve with parameter optimization by using Differential Evolution. In this research, differential evolution algorithm is used to optimize the parametric value t associated with each point so that the distance between the original images and the parametric curve is minimize. In addition, Sum Square Error (SSE) has been used to calculate the distance between the original images and the parametric curve. The proposed technique that we used in this paper provides the comparison of numerical result to solve curve fitting problem. [ABSTRACT FROM AUTHOR]

This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost,and load. [ABSTRACT FROM AUTHOR]

This paper investigates the Nelder-Mead, Powell, and Differential Evolution algorithms in the optimization of a multidimensional optimization problem. Actually, this type of the problem is difficult to solve since the objective function mixes quadratic terms and trigonometric functions. Optimization techniques were use; the ideal values for the design variables that minimize the objective function i discovered. Using 3D surface plots, contour plots, convergence rate plots, and fitness landscape plots, the optimization process made visible. The outcomes show how each algorithm works and behaves in terms of convergence, giving information about both how well it performs and how the objective function is distributed. The results add to our understanding of optimization strategies and offer suggestions for choosing the best algorithms for situations with similar complexity in optimization. Finally, the numerical optimization approach has been implemented by Python language. [ABSTRACT FROM AUTHOR]

Due to the higher power consumption demand arising from rapid and exponential growth in power transmission networks, the use of Flexible A.C. Transmission System (FACTS) equipment has become necessary to increase the controllability and flexibility of power system operation to facilitate high-quality power transmission. One significant factor that plays a particular role in efficiency of operation is the maximum power transmission capacity, and this paper thus examines one of the evolutionary computation techniques used to determine the best location and parameter extraction of FACTS devices, such as Unified Power Flow Controllers (UPFCs), which must be installed within a power system to maximise this. Differential evolution with an adaptive mutation a roach (DEAM) was a lied to reduce power system losses and optimise the network voltage profile. The system used was interactively loaded from the base case in steps of 5%, 10%, 15% and 20%)of the total load demand, and system performance both with and without UPFCs then analysed to confirm the effects within the power system. The acquired results allowed a theoretical a lication of the a roach to the Iraqi national high voltage grid transmission system (400 kV) as a convenient way to enhance power handling, including managing power losses and optimising voltage profile and of enhancing the control capacity of A.C. power flow systems. MATLAB software was used to execute DEAM and Newton Raphson methods to solve system load flow analysis, and the results, which may be are considered very encouraging and of value in restructuring the electrical grid, are thus presented with proper discussion in this work. [ABSTRACT FROM AUTHOR]

Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines. [ABSTRACT FROM AUTHOR]

Solar modules are generally modeled with equivalent electrical circuits for simulation, optimal operation and detailed study. In the lack of field test data, parameter estimation of the solar module model can be carried out using the manufacturer's data. This paper presents the use of a multi-trial vector-based differential evolution (MTDE) algorithm to estimate the parameter values of solar cells or modules. Information from the manufacturer is used as data in estimating the parameter values of the solar cells or modules model. Various types of commercial solar cells or modules are used for testing purposes. The minimum value of the difference between the actual value and the estimated value is used as an objective function to be achieved. The test results show that the MTDE algorithm can estimate the parameter values of the models accurately by producing the objective function value close to zero for all types of solar modules. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) process has a wide capability to generate complex shapes on different materials which are occasionally difficult to cut. Its ability to machine a variety of materials makes it an extensively accepted non-traditional machining process in modern day manufacturing sector. Thus, selection of the optimal input parameters for an ECM process is crucial for its efficient utilization. In this paper, a comparative analysis is made among four metaheuristics, i.e. firefly algorithm (FA), differential evolution (DE), ant colony optimization (ACO) algorithm and teaching-learning-based optimization (TLBO) algorithm to discover the optimal values of various control parameters for an ECM process. Dimensional inaccuracy, tool life and material removal rate are the three responses considered which are subjected to temperature, choking and passivity constraints. The TLBO algorithm shows the best performance among the others without violating any of the constraints. The paired t-test is also performed to prove the efficacy of TLBO algorithm over the other optimization techniques. The results derived from these algorithms are finally compared with those obtained by the past researchers using other optimization methods for both single and multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical analysis of the hybrid algorithm DESA, a combination of Differential Evolution (DE) and Simulated Annealing (SA) algorithm. DESA is a DE-based algorithm with SA-like selection operator. The discussion includes the detailed algorithmic frameworks and characteristics of the DESA-population and its comparison with the classical DE algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we obtain optimality conditions for a problem of convex and non-convex second order evolution differential inclusions with phase constraints. Beginning with second order discrete inclusions problem, we derive necessary and sufficient optimality conditions for the discrete case. We use Locally Dual Mapping definition to derive necessary and sufficient conditions for the optimality of the discrete approximation problem. We prove equivalence theorems in order to obtain a relation between discrete approximation and continuous problems. Passing to the limit, sufficient conditions to the continuous optimal problem are established. [ABSTRACT FROM AUTHOR]

Security constraint optimal power flow study was attracted the researchers over the last decades by using different successful artificial algorithms. However, a little focus has been given to the application of differential evolution (DE) in these studies. This paper presents a study to evaluate and improve the static security of the Iraqi super high-voltage grid at 400 kV (ISHV). By evaluating the security of the power system, secure and insecure operating conditions are determined in the network. An appropriate control action is suggested to alleviate the insecure operating situations in order to avoid the occurrence of a disturbance in the system, which can transfer the system to an emergency state. For this purpose, a security constrained optimal power flow (SCOPF) is used to evaluate and address the overload on transmission lines and voltage violations for buses that arise when the system is exposed to an emergency state. The proposed formulation also takes into account security constraints, including the branch flow limits and bus voltage limits. An evolutionary optimization technique, namely, differential evolution (DE), is adopted to solve the SCOPF problem and improve the security of the grid. The proposed technique is applied to the ISHV to assess and improve the security of the network. The results clearly reveal that the suggested algorithm is effective in getting the optimal contribution from the generating rescheduling. [ABSTRACT FROM AUTHOR]

This paper outlines the initial investigations on the influence of chaotic dynamics to the performance of evolutionary algorithms. The focus of this paper is the embedding of chaotic system in the form of chaos number generator for Differential Evolution. The chaotic systems of interest are the discrete dissipative systems. The two-dimensional Dissipative Standard map was selected as a possible chaos number generator for Differential Evolution. Repeated simulations were performed on a selected benchmark function. Finally, the obtained results are compared with canonical Differential Evolution. [ABSTRACT FROM AUTHOR]

The concept of using network centrality measures in the selection process of memetic differential evolution algorithm is proposed. The usual aim for introducing changes in global optimization algorithms is to make it perform better. This short paper does not intend to provide enough experimental details to decide upon the performance of the new method, nevertheless, we definitely obtain interesting insights on how the discovery of local optima were done. [ABSTRACT FROM AUTHOR]

This study explores a dynamic checkpoint strategy for an on-demand flexible transit service called a Mobility Allowance Shuttle Transit with Dynamic Checkpoint (MAST-DC) that clusters customers by spatial and temporal proximities. By introducing a dynamic checkpoint option to customers in their booking process, we allow customers to walk less distance with a slight increment on the fare price, which is expected to improve the system's level of service and increase the service provider's profit. Hence, we propose an optimal operational framework to cluster customers to dynamic checkpoints by minimizing their walking distance and create the shortest vehicle route between checkpoints without violating the promised scheduled running time at two consecutive fixed checkpoints. This paper proposes a sequential two-phase heuristic method that cluster first and routing second to find a sequence that finds: 1) route that originates and terminates at the fixed checkpoints by minimizing vehicle distance traveled, 2) each cluster centroid visited only once by the same vehicle, and 3) customers' walking distance minimized towards their assigned dynamic checkpoint. The sequential heuristic method clusters customers using the memetic differential evolution (MDE)-based clustering algorithm, one of the state-of-the-art metaheuristic algorithms to solve the minimum sum-of-squares clustering (MSSC) problem. The clustering model is later fused with the second phase, which uses the branch-and-cut method for the traveling salesman problem (TSP) model to find the shortest possible path between identified checkpoints. The parametric simulation experiment is conducted to explore the impact of system parameters on the number of dynamic checkpoints in the analysis region and average walking distance. The results show that the average vehicle travel time uses approximately 90 percent of the scheduled running time. As the demand rate increase, the average number of dynamic checkpoints decrease and customers' average walking distance increases significantly. Therefore, considering the average passengers' walking distance of a fixed transit system, the proposed MAST-DC system is expected to benefit from the demand rate of less than four per square miles and the slack time of 150 percent or 200 percent of the direct travel time. [ABSTRACT FROM AUTHOR]

This paper studies how redundant data affect maximizing of weighted distances of vectors in a set of vectors. To maximize distances differential evolution is used, because the problem does not have analytical solution and is complex. This paper at first describes suppressing of redundant data mathematically and then it checks this theoretical result in two experiments practically. As a result it was found that both experiments are in correspondence with theory. [ABSTRACT FROM AUTHOR]

We present a case study that demonstrates a bio-inspired approach in the process of finding optimal parameters for GSM fault model. This model is constructed using Petri Nets approach it represents dynamic model of GSM network environment in the suburban areas of Ostrava city (Czech Republic). We have been faced with a task of finding optimal parameters for an application that requires high amount of data transfers between the application itself and secure servers located in datacenter. In order to find the optimal set of parameters we employ bio-inspired algorithms such as Differential Evolution (DE) or Self Organizing Migrating Algorithm (SOMA). In this paper we present use of these algorithms, compare results and judge their performance in fault probability mitigation. [ABSTRACT FROM AUTHOR]

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values. [ABSTRACT FROM AUTHOR]

In this initial study, the idea of synthesizing objective function during the evolution process is tested for the improvement of optimization performance of Differential Evolution (DE) algorithm. Since many of the real world problems require computationally expensive simulations there is a demand for specialized optimization algorithms to solve them in as few objective function evaluations as possible. This paper proposes a new approach which combines DE with Analytical Programming (AP), a symbolic regression tool used for the synthesis of objective function in order to adapt the control parameter settings during evolution. [ABSTRACT FROM AUTHOR]

The nonlinear auto-regressive moving average with exogeneous input (NARMAX) model known as one of superior type of models to represent a wide class of dynamic systems. In this paper, a modified NARMAX is proposed in modeling dynamic system. The aim is to investigate the performance of the modified NARMAX model and compared to the conventional NARMAX model for modeling CE8 coupled electric drives system. Multi-objective optimization differential evolution (MOODE) algorithm is used as a model structure selection algorithm to obtain the final model from both approached models. Model predicted output (MPO) test is applied in order to reveal the performance of each model. Through the MPO test, it is concluded that the modified NARMAX model offers a better predicted output than conventional NARMAX model. [ABSTRACT FROM AUTHOR]

Imperialist Competitive Algorithm (ICA) is an evolutionary algorithm inspired by the phenomenon of human's socio-political evolution among human empires in the real world, known as imperialistic competition. Meanwhile, logic programming in data mining can explore the underlying relationship in real life data sets. In this paper, enhanced Imperialist Competitive Algorithm is incorporated in the training phase of Hopfield Neural Network to perform the 2-Satisfiability logic mining. Then, the performance of ICA algorithm will be compared with the widely used conventional method, Exhaustive Search (ES) algorithm. The hybrid network will be tested in bank marketing data set. The performance of both algorithms will be evaluated in term of performance evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), Schwarz Bayesian Criterion (SBC), accuracy and CPU time to determine the effectiveness of the hybrid model. ICA is expected to outperform ES algorithm in doing 2-SAT logic programming. [ABSTRACT FROM AUTHOR]

In this paper is reported our progress in the synthesis of two partially different areas of research: complex networks and evolutionary computation. Ideas and results reported and mentioned here are based on our previous results and experiments. The main core of our participation is an evolutionary algorithm performance improvement by means of complex network use. Complex network is related to the evolutionary dynamics and reflect it. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). Only the main ideas and results are reported here, for more details it is recommended to read related literature of our previous research and results. [ABSTRACT FROM AUTHOR]

This paper presents the optimization of selected CNC milling parameters namely, spindle speed, feed rate and depth of cut on surface roughness of mild steel bar using High Speed Steel (HSS) insert tool. Performance of machining part and production cost were influenced by quality of the surface roughness, Ra. The optimum machining parameters setting is vital and critical. Thus, this study is designed to analyse and optimize the Ra using conventional method, Response Surface Method (RSM) and non-conventional method, Differential Evolution (DE). Firstly, the required experimental runs for data collection was designed by using Design of Expert software. Then, 36 experimental runs were conducted using CNC milling machine and the surface roughness were measured using surface roughness tester. Using the data collected a regression model was developed. Next, DE optimisation method using Matlab is carried out to find the optimum parameters value for minimum Ra. Finally, optimum machining parameters setting generated by RSM and DE, were validated and evaluated experimentally. DE found to be better in finding the optimum parameters value for lower Ra compared to RSM in this study. [ABSTRACT FROM AUTHOR]

Categorization of water bodies and land areas from the satellite image is performed since the prediction of satellite image has become a major challenging issue due to weather condition, atmosphere, etc. Previously, data mining is used for clustering in various application such as text data, similarities in images and bioinformatics data. In this paper, a novel approach has been designed by incorporating the PSO and DE algorithm for data mining technique in the satellite image. Here feature extraction is carried out by using DWT, PCA, and GLCM techniques. In the proposed method, an optimized PSO-DE algorithm is designed to obtain the best solution in order to get the better satellite data. Finally, the estimated output is compared with the existing method on the bases of performances, and it is found to be efficient. The performance parameters such as PSNR, MSE, RMS, mean, variance, correlation, contrast, energy, homogeneity, SD, and entropy are evaluated for the Landsat and MODIS satellite images. [ABSTRACT FROM AUTHOR]

This paper extends the study of structural bias in popular metaheuristic global optimisation methods. Previously, it has been shown that both Genetic Algorithms and Particle Swarm Optimisation suffer from such bias. This means that difficulties already posed for a structurally biased algorithm by the fitness landscape itself are further unnecessarily exacerbated by the unexpected oversampling of some regions of the search space and avoidance of the others, to potential great detriment of the overall optimisation performance. Such bias is inherent in the core design of the algorithm. After careful examination, the authors conclude that some variants of Differential Evolution are not free of the structural bias. However, investigation suggests that the mechanisms of the formation of structural bias in Differential Evolution is different and can be balanced through a more careful design. [ABSTRACT FROM AUTHOR]

The most widely used manufacturing process for the production of plastic parts is injection molding. The produced parts, particularly the thin-walled ones, typically have a tendency to be distorted, which is very easily to be tended. How to reduce the warpage of the thin-walled product become a major problem to plastic manufacturer to control the quality of the molded part produced. An optimization of injection molding process parameters of the thin shell part using Differential Evolution (DE) method is suggested in this paper. A three-pin terminal cover of an electric kettle was used in this research as thin shell plastic part. The simulation software which is Autodesk Moldflow Insight 2012 (AMI) used in this research to identify the recommended process parameter settings which will be used into Response Surface Methodology (RSM) to gain the mathematical model and able to optimize process parameter setting using Differential Evolution (DE) method. The DE optimization method has been reduced the warpage on the molded part by 33.36% for y-axis direction and 11.64% for z-axis direction. So that, this study helps to produce quality injection molding parts with less time consuming and the production run with high accuracy process parameter setting. [ABSTRACT FROM AUTHOR]

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and population size. It is reported that DE is sensitive to the choice of these parameters. To improve the quality of the solution, in this paper, we propose a modified differential evolution introducing self-adaptive parameters, modified mutation and the inversion operator. We test our method with a set of nonlinear continuous optimization problems with simple bounds. [ABSTRACT FROM AUTHOR]

A general algorithm is presented to approximately solve a great variety of systems of ordinary differential equations (ODEs) independent of their form, order, and given conditions. The systems of ODEs are formulated as optimization problem because it isn't an easy way to get the exact solution for systems of ODEs. Therefore, approximate solution is needed for solving systems of ODEs. One of the approaches used in this paper is using Fourier series expansion to approximate solutions of system of ODEs. The Differential Evolution (DE) algorithm, classified as a metaheuristic algorithm, is used as an optimization method to estimate the most accurate coefficients of Fourier series expansion. In this case, DE will be used to minimize the residual functions of the system of ODEs with Fourier series approximations. The original DE is made by putting the best base vector into the mutation part of the DE algorithm. The results show good performance of DE in solving system of ODEs. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) is an area of wireless technologies that is attracting a great deal of interest. There are still several areas of VANETS, such as security and routing protocols, medium access control, that lack large amounts of research. There is also a lack of freely available simulators that can quickly and accurately simulate VANETs. The main goal of this paper is to develop a freely available VANETS simulator and to evaluate popular mobile ad-hoc network routing protocols in several VANETS scenarios. The VANETS simulator consisted of a network simulator, traffic (mobility simulator) and used a client-server application to keep the two simulators in sync. The VANETS simulator also models buildings to create a more realistic wireless network environment. Ad-Hoc Distance Vector routing (AODV), Dynamic Source Routing (DSR) and Dynamic MANET On-demand (DYMO) were initially simulated in a city, country, and highway environment to provide an overall evaluation. [ABSTRACT FROM AUTHOR]

The sound speed provides insight in ocean properties, as it depends on depth, temperature and salinity. Here, we propose a method to invert sound speed profiles (SSPs) from multibeam echosounder (MBES) measurements, providing a SSP for every ping. Using erroneous SSPs results in a mismatch in the estimated bathymetry between overlapping swaths. The SSP is estimated by minimizing this mismatch using Differential Evolution. In this work, SSPs are described using empirical orthogonal functions (EOFs), which are obtained from historical SSPs. As a proof-of-concept, we apply the inversion on a simulated MBES survey, where the synthetically generated SSPs are fully described by 3 EOFs. The inverted SSPs deviate 1 m/s from the correct profiles. In the case of actual SSPs, more EOFs are possibly required. The number of required EOFs to get an accurate estimate of the SSP is assessed by using SSPs acquired in the North Sea. Results show that including only 2 EOFs is sufficient to accurately estimate the SSP, although larger deviations up to 3 m/s were found. In this paper, we demonstrated the potential of the proposed method to invert SSPs from MBES measurements, which can provide information about the vertical structure of the water column. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is used for curve fitting by cubic Ball curves. DE is evolutionary algorithm that encodes solutions as vectors and applies some operations and exchange of components to generate new solutions from the existing ones. In this paper, the DE optimize the two control points in cubic Ball interpolant. In order to obtain the segment or data points, the boundary data of the images should be extracted and detected the corners. Hence, the images reconstruction conducted from the cubic Ball curve and approximated the original image. Some results and numerical examples illustrated in this study. [ABSTRACT FROM AUTHOR]

Swarm intelligence is a strong optimization algorithm based on a biological behavior of insects or animals. The success of any optimization algorithm is depending on the balance between exploration and exploitation. In this paper, we present a new swarm intelligence algorithm, which is based on daddy long legs spider (VDLLA) as a new optimization algorithm with virtual behavior. In VDLLA, each agent (spider) has nine positions which represent the legs of spider and each position represent one solution. The proposed VDLLA is tested on four standard functions using average fitness, Medium fitness and standard deviation. The results of proposed VDLLA have been compared against Particle Swarm Optimization (PSO), Differential Evolution (DE) and Bat Inspired Algorithm (BA). Additionally, the TTest has been conducted to show the significant deference between our proposed and other algorithms. VDLLA showed very promising results on benchmark test functions for unconstrained optimization problems and also significantly improved the original swarm algorithms. [ABSTRACT FROM AUTHOR]

This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration. [ABSTRACT FROM AUTHOR]

This research deals with a synthesis of control law for selected discrete chaotic system - logistic equation by means of analytic programming. The novelty of the approach is that a tool for symbolic regression - analytic programming - is used for the purpose of stabilization of higher periodic orbits - oscillations between several values of chaotic system. The paper consists of the descriptions of analytic programming as well as used chaotic system and detailed proposal of cost function used in optimization process. For experimentation, Self-Organizing Migrating Algorithm (SOMA) with analytic programming and Differential evolution (DE) as second algorithm for meta-evolution were used. [ABSTRACT FROM AUTHOR]