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The utilization of electrical energy in the Radial Distribution System (RDS) is increasing day by day due to various industrial, commercial, and residential loads. The increasing load demand shows negative impact in terms of degrades the voltage profile, voltage stability and increase the power loss of RDS. Negative impacts are counteracted by placement of solar and wind DGs connected to DS in an optimal way. In this article an efficient technique is used for proper allocation of DGs by using Particle swarm optimization and differential evolution (PSODE). Different cases are considered for the analysis. Further, the effectiveness of the proposed algorithm was tested by IEEE 85 bus system, and the simulated results are compared with other Metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the low accuracy and local optimization problems of standard grey wolf optimizer, an improved grey wolf optimizer (DE-GWO) based on differential evolution mechanism is proposed. The algorithm initializes the population by chaotic mapping iterative to increase the diversity of the population and uses a differential evolution mechanism to cross, mutate and select the gray wolf population to accelerate the convergence speed and improve the convergence precision. The simulation experiments of eight test functions are carried out to highlight the proposed algorithm's characteristics. The numerical results and related analysis show that the improved gray wolf optimization algorithm has higher precision and better stability than other GWO variants. [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]

Classification algorithms rely heavily on feature selection (FS) for accuracy and performance, and this is a significant research topic. Filter feature selection algorithms are becoming increasingly popular because of their simplicity and quickness in many jobsthat need feature selection. Each feature and the class labels are estimated using mutual information to determine the associations among each pair of features. A variety of factors, all based on shared knowledge, have led to the rise in popularity of this approach. Classification accuracy is frequently hindered by redundant or irrelevant elements in the obtained data. Instance and feature selection are two procedures that aid in the eradication of unnecessary data and so help to alleviate this issue. Users can categorizefiltering, wrapping, and hybrid techniques under the broad heading of FS strategy. Principal Component Analysis (PCA) is a typicalmethodology for filtering data that is based on the data itself. Feature subsets satisfying a preset classifier that can be found using wrapper approaches, on the other hand. As a result, their accuracy is thoroughly scrutinized. The use of learning methods to evaluatefeature subsets every time makes wrapper approaches expensive and prone to collapsing with a highnumber of features. Primarily Correlated Feature Extraction using Differential Evolution (PCFE-DE) is a new model for feature extraction that uses differential evolution to choose the most relevant features for analysis that is proposed in this research. Algorithms that have previously been used to classify data are compared to those that have not been used before. Tests on benchmark datasets reveal that the proposed approach may reduce the data size while maintaining or even improving classification performance in a majority of the cases. Thisin addition to the fact that the computing time has been much lowered with the proposed model. [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]

Multi-objective optimization (MOO) of ethylene glycol (EG) production via hydrogenation is performed for three problems with four different objectives: min/max energy cost and selectivity for problem 1 (P1); min/max energy cost and productivity for problem 2 (P2); min/max side product and productivity for problem 3 (P3). Multi-objective differential evolution (MODE) with ranking based mutation operator (RMO) is applied to tackle the optimization problem with ASPEN simulator as model-based optimization for EG production. The decision variables considered are pressure and temperature in the reactor. Based on the results, the highest productivity, lowest energy cost, and lowest side product are 172.1 Mil.RM/yea (P3), 0.000673 Mil.RM/year (P1) and 0.0354 kmol/hr (P3). The plots of decision variables denote that the optimum condition is heavily dependent on temperature. [ABSTRACT FROM AUTHOR]

Naked mole-rat algorithm (NMRA) is a recently developed swarm intelligence-based algorithm that is inspired by mole rats' (workers and breeders) mating behaviour. This algorithm has been used to solve optimization problems and is based on breeding capability of breeders to breed with the queen. However, the algorithm has issues with inadequate exploration and less speed of convergence to optimal solution. The improved NMRA (INMRA) is proposed in this research to boost its exploration's ability. The new version is based on trigonometric mutation and used for generation of new worker solution This attribute has been introduced to increase NMRA's exploration properties by keeping the search agents diverse. On CEC2005 benchmark problems, the proposed technique INMRA was tested and demonstrate its superiority to other algorithms, including well-known algorithms such as whale optimization based on opposition learning (OEWOA), differential evolution with self-adaptive properties (SaDE), external archive based differential evolution (JADE), extended grey wolf optimization (GWO-E), hybrid sine cosine crow search algorithm and NMRA. Further, performance of proposed version is also better than the original NMRA for CEC 2019 test problems according to the experimental data. The results are further validated by statistical testing and convergence profiles. [ABSTRACT FROM AUTHOR]

Humanity must plan for farming output to fuel a population that may reach 9.9 billion by 2050. In 1970, food demand was lower due to a lower population. We need lots of farmland to feed the populace. However, due to bad planning and water management in dry season, most farm areas were abandoned during droughts, making it necessary to save them. Water management plans can prevent large amounts of farming area from disappearing. Only 12% of land can be utilized for farming yet we need to feed 9.9 billion people with extra 70% more food by 2050. At present approximately 7.2 of population Farmers around food is produced in sufficient quantities to feed 1.5 billion people. This will feed ten billion people (we are at 7.6 billion currently). Hunger persists despite the plenty. So, we proposed a Differential Evolution algorithm which helps to predict food requirement for future population by considering the past, present agriculture data which helps to solve starvation in near future and also helps to save lands from droughts. [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]

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]

The design of steel transmission line support towers used in practice is a lattice structure. Most often, they are made up of angled steel bars. The advantage of the angle steel section is its ease of installation, but a significant drawback is its very low bending strength. The use of circular hollow sections can result in a better minimum weight due to their better buckling strength. Steel transmission towers can usually be divided into a truncated cone shape and an upper part for fixing the conductors themselves. Because the latter can be designed in a variety of shapes, only the lower part can be optimised in this case. We use the self-adaptive differential evolution (SaDE) technique for optimisation. The mass of the structure is the optimisation's objective function. The stresses are determined using finite element analysis. The overall and local buckling constraints have also been taken into account. Several structural layouts were examined and compared. [ABSTRACT FROM AUTHOR]

The article offers several variants of genetic algorithms, which are investigated using the Rastrygin, Svedefel, Grivenko functions. The developed software is used to visualize the process of extremum searching. Special medical software has been developed in order to visualize the process of function extremum searching. Heuristic algorithms for finding the extremum of functions of several variables are considered. The mathematical apparatus of heuristic algorithms based on evolutionary calculations is described. The target map of the optimization problem is determined. This map is feasible only if the set of feasible solutions is not empty, i.e. there is at least one feasible solution, otherwise the optimization problem is not feasible. The description of the proposed variant of the genetic algorithm is given. A software simulation of the operation of the genetic algorithm configuration has been developed. The algorithm includes random generation of solutions at the first iteration, stopping the search for solutions when localizing individuals in a separate neighborhood, using panmixis as an operator for choosing parents, obtaining new solutions to the optimization problem due to real recombination of genes, mutation of real genes offspring individuals, elite selection of individuals into a new population. The parameters of the genetic algorithm have been determined experimentally. These parameters ensure the finding of the global minima of the functions of Rastrygin, Shvefel, Gravenk in the smallest number of iterations. The work of the software for the implementation of visualization of the proposed genetic algorithm and the results of testing the algorithm are demonstrated. The programmed setting of such parameters of the genetic algorithm as the initial population size, the percentage of the best individuals capable of becoming parents, the generated number of parental pairs, the probability of mutation, the maximum deviation of the real gene from the current value, the percentage of the best individuals passing into the new population is implemented. [ABSTRACT FROM AUTHOR]

In this research work, six metaheuristic algorithms, e.g. of firefly algorithm, artificial bee colony optimization, ant colony optimization, differential evolution, particle swarm optimization and teaching-learning-based optimization techniques are used for parametric optimization of a magnetic abrasive finishing process for having maximum difference between initial and final SR values (ΔRa) (in µm). It is found that the teaching-learning-based optimization algorithm surpasses the others with respect to consistency and accuracy of the resulting solutions while satisfying a given set of machining constraints. A statistical test, i.e. paired t test is also used to confirm its superiority over the remaining algorithms. [ABSTRACT FROM AUTHOR]

The issue of late delivery is critical in the logistic management of a company. It plays an important role in the success of distribution activity and also essential in determining the customer satisfaction level. Solving the late delivery issue in distribution activity could increase the level of trust and loyalty of the customer and resolute the company's market value that aligns with the revenue and cost. This study presents the optimization of the route distribution of BULOG in East Lombok Regency using the Differential Evolution (DE) algorithm and Large Neighborhood Search (LNS) algorithm method. The study found that the existing route distribution of BULOG has been optimal compared to the route distribution result from the DE algorithm method. However, if we compared the existing route distribution with a new distribution route that resulted in using the LNS algorithm, then the study found that the new route distribution is more optimized. This is proven by the deduction of 33.74 km of travelled distance and cost savings of 12.19% from the total cost of transportation. [ABSTRACT FROM AUTHOR]

This study examines energy sharing in the context of imbalance between power generation and demand, and the need for better load handling, particularly in light of a growing population. Load interruption is pivotal, as this offers a rapid solution to unbalanced conditions, helping prevent grid breakdown and increasing reliability; however, its enforcement can be otherwise harmful, as classical load reduction techniques can lead to over or under reduction of the load in the electrical grid. In reality, most distribution feeders switch off in such systems without consideration of their priorities and the system may thus not perform as anticipated. In this research, a logarithmic reduction strategy is therefore suggested to lower loads based on both priorities and daily performance. A Tunning Factor Reduction Matrix was scaled based on the size of the grid, with higher priority demands fed with a reliable power source and power reduction restricted to lower priority demands. Using MATLAB software and an Excel spreadsheet, this study thus clarified the proposed optimisation technique using a differential evolution algorithm, which is applied to minimise the error with regard to the amount of load reduction required and the actual load reduction in electric power systems. The proposed algorithm was also tested on a practical data system sample provided by the Iraqi Ministry of Electricity and the control centre of the Iraqi national grid in Baghdad, verifying its use with regard to practical data systems. [ABSTRACT FROM AUTHOR]

We consider the continuous p-median problem which is a popular mathematical model for both facility location and cluster analysis. For the p-median problems as well as very similar k-means problems, genetic and Variable Neighborhood Search algorithms with the greedy agglomerative heuristic procedure demonstrate their efficiency. However, the operation of such algorithms highly depends on the parameter of such procedure. We propose a new (1 + λ) evolutionary algorithm involving the greedy agglomerative procedure as the mutation operator which adjusts the parameter of this procedure during the operation of the algorithm. The advantage of the new algorithm was demonstrated by several examples including large-scale ones. [ABSTRACT FROM AUTHOR]

In this study we consider dewatering sludge problem that appears in different industries and has both serious ecological and economic effect. Dewatering performance depends on real time water analysis content, which can be provided by the nuclear magnetic resonance sensors. These sensors produce the signal in a form of linear combinations of exponential functions with different amplitudes and relaxation time parameters, which represents the water exact content and volume of its components. We discuss different approaches on solving the water content analysis problem based on magnetic resonance sensors output and currently focus on solving the reduced optimization problem. We investigate the parameter fitting problem, discover its relation to content analysis problem. We examine influence of algorithm parameters and adding penalties to optimization criteria on approach performance. In this study we numerically prove the relation between the solution of the reduced optimization problem and water content analysis parameter identification. [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]

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]

The present work studies the performance of the less explored fractional order (FO) based cascade tilt-integral-tilt-integral-derivative (CTI-TID) controller for load frequency control (LFC) of interconnected restructured power systems having nonlinearity through time-domain simulation. The efficacy of the proposed controller is tested by comparing its results with that of other studied controllers such as cascade proportional-integral-tilt-derivative (CPI-TD) and proportional-integral-derivative (PID) controllers. However, a heuristic water cycle algorithm (WCA) is used for realistic comparison to find their optimized gain values. In this work, firstly, the efficacy of WCA over other powerful algorithms like genetic algorithm, teaching learning-based and differential evolution algorithm is established in tuning designed CTI-TID controllers. Secondly, to analyze the supremacy of the proposed WCA tuned CTI-TID controllers, frequency responses obtained under it are compared with that of WCA tuned CPI-TD and PID controllers. Lastly, the impact of the capacitive energy storage system on LFC's performance under the proposed WCA tuned CTI-TID controller is analyzed. [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]

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]

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]

The article proposes an algorithm for optimizing the number of features in classification problems. The algorithm combines the differential evolution method and the bat echolocation algorithm. The experiments used the k-nearest neighbors' classifier and the classifier based on the construction of a decision tree. The performance of the algorithm was evaluated on reference datasets from the UCI library. The performance of the algorithm was compared with competing algorithms using the Wilcoxon T-test and the Friedman test. [ABSTRACT FROM AUTHOR]

The article considers a two-level open quantum system, whose evolution is governed by the Gorini–Kossakowski– Lindblad–Sudarshan master equation with Hamiltonian and dissipation superoperator depending, correspondingly, on piecewise constant coherent and incoherent controls with constrained magnitudes. Additional constraints on controls' variations are also considered. The problem is analyzed using Bloch parametrization of system's density matrix. We adapt the section method for obtaining outer parallelepipedal and pointwise estimations of reachable and controllability sets in the Bloch ball via solving a number of problems for optimizing coherent and incoherent controls with respect to some objective criteria. The differential evolution and dual annealing optimization methods are used. The numerical results show how the reachable sets' estimations depend on distances between the system's initial states and the Bloch ball's center point, final times, constraints on controls' magnitudes and variations. [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]

Differential equations have been used to describe phenomena in many fields. However, not all of differential equations can be solved in analytic ways so there is some approach being made to solve them numerically. One of the approaches is using Fourier series expansion to approximate solutions of linear and nonlinear ordinary differential equations (ODEs). The coefficients of Fourier series expansion are estimated by an optimization method using Differential Evolution (DE). In this case, differential evolution will be used to minimize the residual function of the Fourier series that are implemented into the ordinary differential equations. A modification from the original DE is made by putting a simple adaptive scheme into the mutation part of the DE algorithm. The results show good performance of DE in solving various ODEs. [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]

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]

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 work, we consider an evolution differential inclusion known as the perturbed second order nonconvex sweeping process for a class of subsmooth moving sets. The right-hand side contains a set-valued perturbation with almost-convex values, which is a strictly weaker condition than the standard assumption of convexity. [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]

The equivalence group for a class of nonlinear evolution partial differential equations, generalizing the second order Kudryashov-Sinelshchikov equation which describes pressure waves in liquid with bubbles, is calculated. Then a preliminary group classification of the equations in the considered class is obtained. After a simplification, based on the use of the found equivalence group, a complete group classification of these equations is carried out. [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]

The present study aimed to perform the acidic adaptive evolution on a ε-PL-producing strain of Streptomyces diastatochromogenes T17 to increase its ε-PL production, verify the acid resistance abilities of the evolutionary strains and optimize the fermentation medium. The results showed that the dry cell weights of three evolutionary strains were significantly higher than that of the original strain after 4 days of fermentation (initial pH of 4.0). Besides, the better mycelium growth performance and spore maturity at plates with different pH values (5.0, 4.5 and 4.0) also proved that the acid resistance abilities of the evolutionary strains were stronger than that of the original strain. The ε-PL production of evolutionary S. diastatochromogenes AE 56 after fermentation medium optimization reached 1.08 g/L, increasing 12.5%. [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]

The object the research is to forecast maritime weather variables such wind speed and direction, temperature and wave height for an hour ahead by using artificial intelligence approach. Artificial intelligence is comprised of hybrid neural networks modified by genetic algorithms and particle swarm optimization which are functioned as a model predictor. The hybrid predictor works on every single predictor by weighing both artificial neural network-genetic algorithm (ANN-GA) and artificial neural network-particle swarm optimization (ANN-PSO) which weight is calculated by differential evolution algorithm optimization. When the unsurpassed model is obtained, it will be validated across real-time data that is delivered from type II buoyweather station measurement at the Madura Strait, Java Sea. The prediction results of learning and validation process indicate that the ANN-Hybrid predictor perform more accurate than the ANN-GA and ANN PSO on training and validation. However, the gap of RMSE on real-time test is relatively high compared to validation or training. It can be influenced by the different frequent of weather fluctuation between them. Concurring to real-time test stage, the foremost appropriate variable that predicted by this ANN-Hybrid is temperature. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of differential evolution (DE) algorithm, such as premature convergence and low convergence accuracy, we propose a multi-strategy different dimensional mutation differential evolution (MDMDE) algorithm. On one hand, the algorithm starts from the mutation dimension, and then proposes a different dimensional strategy. On the other hand, multi-strategy mutation is used in the whole generation cycle of the algorithm. In addition, a modified crossover rate and a dynamic mutation factor are proposed to jump out of the local maximum. Finally, MDMDE is applied to 8 kinds of test functions and compared with other two algorithms. The results show that MDMDE can obtain better optimization results and it exhibits more desirable performance than the other algorithms [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 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]

Differential evolution algorithms represent nowadays an efficient framework to cope with complex optimization tasks with many variables and involved constraints. Nevertheless, the classic differential evolution algorithms do not ensure the global convergence to the minimum of the cost function. That is why the author designed a modification of these algorithms that guarantees asymptotic global convergence in the probabilistic sense. The article shows that Lipschitz continuity of the cost function is a reasonable assumption making possible quantitative considerations and estimates. The next part describes how the cost function domain is explored by random individuals. More random individuals mean more detailed sampling of the cost function domain. This fact is the base for the asymptotic convergence of the modified differential evolution algorithm. [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]

In process of the shaping plastic part, the defects always encounter with the produced plastic part shape and functionality are affected by plastic injection molding process. This study is relating to a decreasing warpage and improving the process parameter in injection molding. One of the defects on injected parts is warpage, from a certain path this defect normally produces a band-like shape deviation. The most affected area of this defect is appearance, followed by assembly and also the quality of the product. This study was obtained to minimize the problem faced and getting the optimum result based on the problem facing by previous researchers that have been analyzed to getting the information. Besides that, the software of Autodesk Moldflow Insight (AMI) is used to do a simulation of the analysis. Thus, Design of Experiment (DOE) was used to optimize the result which is Response Surface Methodology (RSM). For the comparison, Differential Evolution (DE) approach will be used in combination with AMI and RSM result. The best combination of parameter setting is gained and effect the warpage to become decreases. To produce good quality of injection molding parts this will be contributed continuous production line run with high accuracy and good parameters setting. Without wasting time and resources, this advance research will contribute to producing a good quality injection molding parts. [ABSTRACT FROM AUTHOR]

The parallel Asynchronous Differential Evolution (ADE) method with adaptive correlation matrix (ACM) is utilized for numerical solution of the global minimization problem within the Separated Form Factors model (SFF) for analysis of structure of the drug-delivering phospholipid transport nanosystem (PTNS). In this framework, parameters of the SFF model are fitted to experimental data of the small angle scattering of X-rays (SAXS) and of neutrons (SANS). The ADE-SFF approach is described, the results of numerical study of polydispersed PTNS-based systems in water solutions depending on the disaccharide concentration, are presented. [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]

Differential evolution algorithms represent an efficient framework to solve complicated optimization tasks with many variables and complex constraints. Nevertheless, the classic differential evolution algorithm does not guarantee the convergence to the global minimum of the cost function. Therefore, the authors developed a modification of this algorithm that ensures asymptotic global convergence. The article provides a comparison of the ability to identify the global minimum of the cost function for the following three algorithms: the classic differential evolution algorithm, the above mentioned modified differential evolution algorithm and an algorithm of random sampling enhanced by a hill climbing procedure. We designed a series of numerical experiments to perform this comparison. The results indicate that the classic differential evolution algorithm is in general an extremely poor global optimizer (global minimum found in 2% of cases). On the other hand the performance of the modified differential evolution algorithm was considerably better (global minimum found in 83% of cases). [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]

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]

In this work we present a representative of a new wider family of high phase-lag order 16 Runge-Kutta pairs of orders 8(7) with smaller principal local truncation term than the methods suggested in the literature. Numerical experiments justify the characteristics of the new method. [ABSTRACT FROM AUTHOR]