Search

In this study, the efficiency of estimating the model parameters of sheet-shaped single and multiple sources of the self-potential (SP) anomaly using the differential search algorithm (DSA) is investigated. First, noise-free and noisy synthetic anomalies are calculated for a single sheet-shaped source, and its model parameters estimated by DSA. The DSA inversion is also done for a model consisting of three inclined sheets. To test the effectiveness of the method, the same processes are repeated with a more conventional algorithm, particle swarm optimization (PSO), and the solutions of both methods are compared. The results of synthetic anomaly analyses show that DSA can predict the parameters as accurately as PSO. Then, both algorithms are also applied to two field SP anomalies (Surda and Beldih) that have been evaluated by different algorithms in the literature. The source of the Surda anomaly is modelled as one sheet, whereas the source model of the Beldih anomaly is assumed to consist of three sheets. The five model parameters for each model are estimated using both algorithms and it is determined that they are in good agreement with the findings of the previous studies. The contribution of the regional background anomaly to the synthetic and field anomalies are also included and regional coefficients are estimated. Finally, we conclude that DSA can solve the source parameters without the need for the initial values required in conventional iterative inversion methods and is an efficient and promising algorithm for determining the parameters of SP sources. [ABSTRACT FROM AUTHOR]

Energy efficiency and energy hole phenomena are still dominant issues in wireless sensor networks. Based on recent studies, mobile data ferry could be a powerful approach to address these vital issues. Determination of sojourn points and efficient pathway design is vital in this approach. However, mobile data ferry visits to specific nodes make a long tour distance. Moreover, improper sojourn points affect to energy consumption. This paper proposes an energy-efficient mobile data ferry pathway construction protocol (EEMDFPC) based on transmission coverage and a hybrid differential search algorithm (DSA). EEMDFPC establishes non-overlapping clusters and selects the optimal CHs by adopting hybrid DSA. Hybrid DSA is an improvement of DSA by employing particle swarm optimization (PSO) and chaotic map. Moreover, the proposed protocol discovers the sojourn points based on the full coverage area and the intersection area between CHs in a minimum number. Next, hybrid DSA with swap strategic constructs mobile data ferries route. Performance evaluation of the proposed protocol was carried out in a simulated environment and compared with the MSEAC-4R and MSEAC-8R protocols. The evaluation results show that the proposed protocol can extend the network lifetime compared to MSEAC-4R, MSEAC-8R, and LEACH_CD are 15.08%-25.63%, 7.72%-17.76%, and 10.41%-29.44% respectively. The proposed protocol can also prolong the stability compared to MSEAC-4R and MSEAC-8R, namely 643-1025, 621-965 rounds, and 18-546 rounds, respectively. The superior performance of the proposed protocol proves that the proposed protocol operates efficiently and reduces the energy hole problem. [ABSTRACT FROM AUTHOR]

A differential search algorithm (DSA) application, which is a metaheuristic inspired by nature, for total field aeromagnetic data caused by volcanoes over a 2D dipping dyke is presented. Inversion of the total magnetic anomalies was performed by adding the background level in addition to the parameters of the dyke model (e.g., dip angle, the depth to the top, half-width, the distance from the origin to the reference point, and amplitude coefficient), which are often tried to be estimated in the literature studies. In synthetic dyke models, the efficiency of the DSA in parameter estimation of theoretically generated magnetic anomalies that do not contain noise and contain random noise at different levels has been demonstrated. Firstly, in the synthetic dyke model, the efficiency of the DSA in parameter estimation of theoretically generated noise-free magnetic anomaly is demonstrated. Additionally, different levels of random noise were added to the same synthetic model anomaly to test the performance of the algorithm in case the data contained noise. The results of the inversion show that the model parameters estimated from the DSA agree well with the correct ones. This fit was also statistically checked by calculating the probability density function. In the real case, the inversion approach was then used to interpret five prominent total aeromagnetic anomalies over the well-known Kula volcanic field located in western Türkiye. The depths and widths of these magmatic bodies lying underneath these volcanic cones are about 450 m and 470 m, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a 60 nm Complementary-Vertical TFET (C-VTFET) is designed using silicon on insulation technology is implemented for low power quadrature mirror filter design for different search algorithm using silvaco TCAD simulation. Various advantages of SOI have been incorporated for the realization of low-voltage with low power (LVLP) for VLSI design digital circuits. To prevent the losses of the Lattice mismatch structure, gate staking of high k -dielectric (HfO2) material with SiO2 was employed using equivalent oxide thickness method. Various engineering method incorporated to optimized the Drain-Voltage characteristics. N-Type and P-Type VTFET is considered using the mixed mode technique. The SiGe layer is employed to enhance the tunneling method of by reducing the bandgap from 1.1 eV to 0.7 eV. The highest ION current and minimum IOFF current is reported for the proposed device is I (3.62 × 10−4 A/μm) and (1.58 × 10−18 A/μm) respectively. The ON/OFF current ratio out recorded as ~1013 respectively. The reconstruction quadrature mirror filter architecture in this manuscript is computationally efficient and virtually flawless. Sparsity among the coefficients is introduced to reduce the number of multipliers and adders required to construct prototype the filter H0(Z), resulting in a reduction in computational complexity. A well-known population-based evolutionary optimization differential search algorithm is used to optimized the objective function. However, Levy's differential search method is also advocated due to sluggish exploitation of traditional differential search algorithm. The suggested algorithm's effectiveness is evaluated by comparing it to other evolutionary optimization techniques. The results demonstrate that the proposed technique outperforms previously published evolutionary optimization algorithms. The above analysis validated the proposed device are well incorporated for the execution to design low power filter design. [ABSTRACT FROM AUTHOR]

In this paper, new and efficient heuristic algorithm, Differential Search Algorithm (DSA), is used for optimal choice and allocation of shunt Flexible AC transmission systems (FACTS) devices, Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM). In the proposed optimal location method, SVC and STATCOM devices are located randomly and dynamically with objective of minimizing the real power loss, improving the voltage profile and enhancing the voltage stability. The best solution sets for objectives aforementioned are achieved by minimizing each objective functions by the proposed approach. In order to evaluate the proposed methodology, it has been tested on IEEE 30 bus system and compared to another widely used optimization algorithm, Artificial Bee Colony (ABC) algorithm. The validity of the proposed optimization algorithm is proven by the results and comparative analysis with other method. [ABSTRACT FROM AUTHOR]

Due to the complex structure of observation based traffic accident data and the absence of an analytic model to define their characteristics, different models are required. Accident characteristics have been modeled for different road segments with two different methods: evolutionary data clustering method and resilient neural networks. In the first method, observation data was clustered using an evolutionary search-based clustering algorithm. The first method is based on determining whether observation based test data have the conditions of a possible death or injury accident based on the distance to the cluster centers obtained. The second one is a regression method that predicts whether an accident will cause death or injury according to observation based traffic data in test road segments by using resilient neural networks. Experiment results show that data analysis methods are very effective in determining the existence of the conditions that may cause accidents resulting in death or injury. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) have been widely applied to monitor objects in harsh and hazardous environments. The limited energy resource in WSN is a major problem. Energy efficiency is required for WSN to operate for a long time. The cluster in WSN is a strategy that can help to extend the life of the network. However, the optimal cluster formation and cluster head (CH) selection is a non-deterministic polynomial-time hard problem. Besides, many clustering-based protocols build clusters after defining the cluster heads among all sensor nodes on the network. This process triggers inefficient energy consumption. Furthermore, the unbalanced load between CH and cluster members (CMs) also affects the network lifetime. This paper proposes an advanced clustering protocol based on a modified differential search algorithm to save energy consumption and extend network life. To form clusters, this protocol determines the optimal number of clusters. In this proposed protocol, CH selection takes place at the cluster level. The process of cluster formation and CH selection is proposed by utilizing a modified differential search algorithm. Furthermore, the rotation system and the determination of relay nodes are proposed to control the load distribution. The proposed protocol's performance is compared with various data gathering protocols for WSN, namely LEACH, PEGASIS, LEACH-GA, and HFAPSO. The experimental results show that the proposed protocol can increase network lifetime in terms of First Node Dies (FND), Half of Nodes Die (HND), and Last Node Dies (LND). On average, the MDSAC protocol increases FND by 1596 rounds, 2456 rounds, 1211 rounds, and 1060 rounds compared to LEACH, PEGASIS, LEACH-GA, and HFAPSO. The MDSAC protocol also increases the average HND by 5764 rounds, 4918 rounds, 5221 rounds, and 4308 rounds compared to other comparison protocols. Even the MDSAC protocol managed to improve the average LND of 7784 rounds, 8739 rounds, 9001 rounds, and 7161 rounds, compared to the four comparison protocols. These results highlight the contribution of the proposed approach in maintaining network lifetime in WSN as one of the important challenges in WSN research and development. [ABSTRACT FROM AUTHOR]

As one of the most important components in rotating machinery, rolling bearings are fragile due to their harsh working environment. The empirical wavelet transform (EWT) method has been used to identify bearing faults by constructing an adaptive filter bank and decomposing the vibration signal into different modes. However, the EWT method separates different parts based on the local extremum of the Fourier spectrum. This segmentation strategy is highly susceptible to other components such as gears, and sometimes the fault characteristics of bearings cannot be detected. To overcome the above drawbacks, frequency phase space empirical wavelet transform (FPSEWT) method is proposed in this paper, which divides the spectrum into several parts and uses the Teager energy distribution as a reference for dividing the Fourier spectrum. Then, the differential search (DS) algorithm is applied to automatically identify the optimal right and left boundaries of the sensitive frequency band. The effectiveness of the proposed approach is verified by both simulated signals and experimental data. The fault feature ratio (FFR) values of sensitive components increased from 8.08% to 18.67%, which indicate that the proposed method can successfully extract fault symptoms of rolling element bearings in the presence of environmental disturbance.

Distribution Network reconfiguration (DNR) and Optimal Capacitor Placement (OCP) are well known techniques to reduce power loss and maintain stable voltage levels in Distribution network. Minimization of power loss has immense effect on cost, quality and reliability of supply. Following the present trends of power trading methods, this paper explores the necessity of reconfiguration of the network and Optimal Capacitor Placement under wheeling transaction, incorporating market dispatching considerations. Wheeling transactions will add additional loss which is taken into consideration while modelling. Distribution System Operator (DSO) plays responsible role and knows how to reconfigure the network for the optimal operation of the entire system. DSO optimizes the objective function (minimization of power loss) by satisfying constraints of all the participants that includes technical as well as commercial. By its nature, DNR or Feeder Reconfiguration (FR) and OCP are a complex multi constrained optimization problem. The chosen optimization algorithm should improve the effective operation of DSO. Differential Search Algorithm (DSA) is adapted to solve this optimization problem due to its simple structure and proven faster convergence. The proposed methodology was tested on standard IEEE 33-bus distribution system. Simulation results are presented and compared with existing popular methodologies. [ABSTRACT FROM AUTHOR]

Image registration is the process of overlaying images of the same scene taken at different times by different sensors from different viewpoints. The cross‐cumulative residual entropy (CCRE)‐based medical image registration could achieve a high precision and a strong robustness performance. However, the optimization problem formulated by CCRE consists of some local extrema, especially for noise images. In order to address these difficulties, this article proposes a new optimization algorithm named hybrid differential search algorithm (HDSA) to optimize CCRE. As HDSA consists of simple control parameters, it is independent of the initial searching point. In addition, HDSA ameliorated the search method and the iterative conditions. As a result, the optimization process is more stable and efficient. Image registration experiments of HDSA are performed and compared with the conventional differential search algorithm (DSA) and adaptive differential evolution with optional external archive (JADE). The results show that HDSA does not only overcome the difficulties of sticking in the local extrema but also enhances the precision of registration. It is effective, robust, and fast for both the single‐mode rigid medical image registration and the multispectral‐mode rigid medical image registration. [ABSTRACT FROM AUTHOR]

Differential search (DSA) is a recently proposed evolutionary algorithm mimicking the Brownian motion-like random movement existing in living beings. Though it has displayed promise for global optimization, the original DSA suffers from relatively poor search capability, especially for exploitation. In this study, an augmented DSA (ADSA) is proposed by integrating memetic framework with multiple strategies. In ADSA, a sub-gradient strategy is combined to improve local exploitation, and the dynamic Lévy flight technique is developed to strengthen the global exploration. Moreover, a mutation operator based on differential search is used to increase swarm diversity. An intelligent selection method is implemented to adaptively adjust the strategies based on historical performance. To fully benchmark the proposed algorithm, 35 test functions of various properties in 30-D and 100-D are adopted in numerical experiments. Seven canonical optimization algorithms are involved for experimental comparison. In addition, two real-world problems are also tested to verify ADSA's practical applicability. Numerical results reveal the efficiency and effectiveness of ADSA. [ABSTRACT FROM AUTHOR]

The performance of a service-based system (SBS) in a cloud environment may not satisfy service-level agreement (SLA) constraints when the system load changes. To improve the profits of resource providers and satisfy the global SLA, it is necessary to dynamically allocate proper resource for SBS based on the forecasted system load. By analyzing the complex workflow of the SBS, this paper proposes improved differential search algorithm-based dynamic resource allocation approach which adopts an active mechanism to respond to the change of system load so as to ensure the timely response to change. The dynamic resource allocation model based on costs optimization and SLA constraint is then proposed. The improved differential search algorithm is designed to solve the dynamic resource allocation model. This paper proposes a load forecasting approach based on deep belief networks (DBNs) in order to accurately forecast the load to support dynamic resource allocation. Experimental results show that the approach performs well in terms of the quality of the solution compared with other related approaches. [ABSTRACT FROM AUTHOR]

Ultrasound (US) image segmentation plays a very important role in diagnostic imaging. In order to extract special tissues from images, this study proposes a new method for segmentation of US images. The proposed method uses multilevel threshold segmentation which is based on Otsu and differential search algorithm. Testing in simulation US images shows that the proposed algorithm has a better result of segmentation than the three existing methods, including region growing, the active contour model and k‐means technique. The proposed method gets the highest Fm values and the smallest area errors in experiments. Vivo US images are also tested by the proposed method and it achieves a good segmentation result. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

Differential Search (DS) algorithm is a new meta-heuristic for solving real-valued numerical optimization. This paper introduces a new method based on DS for solving Resource Constrained Project Scheduling Problem (RCPSP). The RCPSP is aimed to schedule a set of activities at minimal duration subject to precedence constraints and the limited availability of resources. The proposed method is applied to PSPLIB case studies and its performance is evaluated in comparison with some of state of art methods. Experimental results show that the proposed method is effective. Also, it is among the best algorithms for solving RCPSP.

Abstract This paper presents differential search algorithm in order to solve reliability optimization problem of radial distribution network. Remote control switches have been optimally allocated to improve reliability at a compromised cost. A multi-objective problem has been formulated and solved using differential search algorithm. The test systems considered in this paper are an 8 bus radial distribution network and a 33 bus radial distribution network. Simulation results obtained using differential search algorithm when applied to the test cases, have been compared with those obtained by particle swarm optimization. Differential search algorithm has been found to provide superior results as compared to particle swarm optimization. [ABSTRACT FROM AUTHOR]

A novel blind signal separation algorithm based on differential search was proposed for solving the high cal-culated amount problem in blind signal separation algorithm based on bio-inspired optimization. The temporal predict-ability of signal was used as the objective function and the differential search algorithm was used for solving it. The source signal component separated was wiped off using deflation method and all the source signals could be recovered successfully by repeating the separation process. Simulation results show that the algorithm can achieve blind separation from mixed signals efficiently with very high separation precision and very low computing time.

We present an efficient inversion tool for parameter estimation from horizontal loop electromagnetic (HLEM) data using Differential Search Algorithm (DSA) which is a swarm-intelligence-based metaheuristic proposed recently. The depth, dip, and origin of a thin subsurface conductor causing the anomaly are the parameters estimated by the HLEM method commonly known as Slingram . The applicability of the developed scheme was firstly tested on two synthetically generated anomalies with and without noise content. Two control parameters affecting the convergence characteristic to the solution of the algorithm were tuned for the so-called anomalies including one and two conductive bodies, respectively. Tuned control parameters yielded more successful statistical results compared to widely used parameter couples in DSA applications. Two field anomalies measured over a dipping graphitic shale from Northern Australia were then considered, and the algorithm provided the depth estimations being in good agreement with those of previous studies and drilling information. Furthermore, the efficiency and reliability of the results obtained were investigated via probability density function. Considering the results obtained, we can conclude that DSA characterized by the simple algorithmic structure is an efficient and promising metaheuristic for the other relatively low-dimensional geophysical inverse problems. Finally, the researchers after being familiar with the content of developed scheme displaying an easy to use and flexible characteristic can easily modify and expand it for their scientific optimization problems. [ABSTRACT FROM AUTHOR]

Optimal reactive-power dispatch (ORPD) is one of the most difficult optimization problems regarding to power-system operation which is heavily related to system stability. To overcome ORPD problem, in general, the total real-power loss is reduced by determining the power-system control parameters, such as generator voltages, tap ratios of tap-changer transformers, and compensated reactive power, optimally. In this paper, a simple and efficient heuristic search method based on the differential search algorithm (DSA) has been presented and used to solve the ORPD problem in power systems. The ORPD problem was overcome on the IEEE 30 bus and IEEE 57 bus test systems by achieving optimal solution sets of system control variables for the objective functions regarding to power-loss minimization, voltage-profile improvement, and voltage-stability enhancement. By comparing with the several optimization methods, the performance of DSA is tested and verified on the aforementioned power system. Optimization results of different cases and objective functions indicate the implementability of the proposed algorithm in terms of solving the ORPD problem as well as other single- and multi-objective optimization problems regarding to power systems. [ABSTRACT FROM AUTHOR]

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem. [ABSTRACT FROM AUTHOR]

In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems. [ABSTRACT FROM AUTHOR]

In this paper, a simple and efficient nature inspired search method based on differential search algorithm (DSA) has been presented and used for optimal power flow (OPF) problem in power systems. By using the proposed DSA method, the power system parameters such as real power generations, bus voltages, load tap changer ratios and shunt capacitance values are optimized for the certain objective functions. Different types of single-objective and multi-objective functions on IEEE 9-bus, IEEE 30-bus and IEEE 57-bus power systems are used to test and verify the efficiency of the proposed DSA method. By comparing with several optimization methods, the results obtained by using the proposed DSA method are presented in detail. The results achieved in this work illustrate that the DSA method can successfully be used to solve the non-linear and non-convex problems related to power systems. [ABSTRACT FROM AUTHOR]

The main challenges of clustering techniques are to tune the initial cluster centres and to avoid the solution being trapped in the local optima. In this paper, a new metaheuristic algorithm, Differential Search (DS), is used to solve these problems. The DS explores the search space of the given dataset to find the near-optimal cluster centres. The cluster centre-based encoding scheme is used to evolve the cluster centres. The proposed DS-based clustering technique is tested over four real-life datasets. The performance of DS-based clustering is compared with four recently developed metaheuristic techniques. The computational results are encouraging and demonstrate that the DS-based clustering provides better values in terms of precision, recall and G-Measure. [ABSTRACT FROM AUTHOR]

In this paper an improved over current relay coordination protection technique is proposed. The proposed technique eliminates the sympathetic/nuisance tripping in an interconnected distribution system. Nuisance trippings are eliminated by incorporating the additional selectivity constraints in conventional over current relay coordination problem. Whenever the fault is cleared only from one end of the line in bidirectional fault feed lines, transient network configuration comes into existence. Transient network configuration causes the redistribution of fault in the distribution system and leads to further nuisance tripping of relays. This problem in this paper is solved by minimising the operating time gap between primary relays located at near end and far end of a faulty line to the best minimum possible value. The differential search algorithm is applied for optimization of highly non-linear over current relay coordination problem in this paper. The result presented in this paper shows that the proposed over current relay coordination technique is immune against the sympathetic/nuisance tripping and operating time difference between primary relays at near end and far end is also minimised within acceptable time margin successfully. [ABSTRACT FROM AUTHOR]

This paper proposes differential search (DS) algorithm to solve distribution system reliability optimization problem. Optimum number and location of remote control switch (RCS) have been found, in order to enhance reliability of a radial distribution system. A multi-objective formulation has been considered with a view to enhance reliability at a compromised cost. DS algorithm utilizes the Brownian-like random-walk movement used by an organism to migrate. Simulation results obtained by DS algorithm have been compared with that of particle swarm optimization (PSO), differential evolutionary algorithm (DE), genetic algorithm (GA), ant colony optimization (ACO) and gravitational search algorithm (GSA). Results show that DS algorithm provides considerably superior performance, in terms of quality of solution obtained and computational efficiency. [ABSTRACT FROM AUTHOR]

This article proposes a differential search algorithm to solve a distribution system reliability optimization problem. Optimum failure rate and repair time of different distributor segments of a distribution network have been calculated to enhance reliability of radial distribution system, both without distributed generation and incorporating distributed generation as standby unit. The differential search algorithm, a recently developed optimization algorithm having improved exploration and exploitation ability, has been implemented to solve the reliability optimization problem in search for superior quality solution. The differential search algorithm mimics the Brownian-like random-walk movement used by an organism for its migration. Simulation results establish that the proposed approach outperforms other existing optimization techniques in terms of quality of solution obtained and computational efficiency as applied to optimize failure rate and repair time of a radial distribution network. [ABSTRACT FROM PUBLISHER]

A control vector parameterization (CVP)-based hybrid algorithm, HAPSODSA-CVP, is proposed to solve the nonlinear chemical dynamic optimization problems, where adaptive particle swarm optimization (APSO) is applied to enhance the global search ability, while differential search algorithm (DSA) is used to improve the local exploitation ability. Three well-known classic nonlinear chemical dynamic optimization problems are tested as illustration, and detailed comparisons are carried out among PSO-CVP, APSO-CVP, and HAPSODSA-CVP approaches. The research results not only demonstrate the efficiency of the HAPSODSA-CVP approach for this kind of dynamic optimization problems, but also its superiority to both APSO-CVP and PSO-CVP approaches in terms of accuracy as well as convergence rate. [ABSTRACT FROM AUTHOR]

In this paper, a novel Differential Search Algorithm (DSA) approach is proposed to solve multiobjective optimization problems, called Multiobjective Differential Search Algorithm (MODSA). MODSA utilizes the concept of Pareto dominance to determine the direction of a super-organism and it maintains non-dominated solutions in the external repository. This approach also uses the external repository of super-organisms that is used to guide other super-organisms. It guides the artificial organisms to search towards non-crowding and external regions of Pareto front. The performance of proposed approach is evaluated against the other well-known multiobjective optimization algorithms over a set of multiobjective benchmark test functions. Experimental results reveal that the MODSA outperforms the other competitive algorithms for benchmark test functions. [ABSTRACT FROM AUTHOR]

This paper proposes a new multi-level methodology based on the optimal reactive power planning. The developed methodology is designed to solve the problem of the non-feasibility solution of the fuel cost minimization problem (for a given operating point) where the classical method such as interior point method (IPM) is applied. The proposed solution to solve this problem is based on the application of the optimal reactive power planning problem considering voltage stability as the initial solution of the fuel cost minimization problem. To improve the latter the load voltage deviation problem is applied to improve the system voltage profile. For à good result improvement, the reactive power planning problem and the load voltage deviation minimization problems are solved using a new optimization method namely the Differential Search Algorithm (DSA). Moreover, the fuel cost minimization problem is solved using IPM. To identify the candidate placements of compensation devices for the optimal reactive power planning problem, a new voltage stability index namely: The Fast Voltage Stability Index (FVSI) is used. The methodology has been tested with the equivalent Algerian power system network, and the simulation results show the effectiveness of the proposed approach to improve the reactive power planning problem and to minimize the system voltage deviation. [ABSTRACT FROM AUTHOR]

Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this work, we proposed and implemented a new Rayleigh wave dispersion curve inversion scheme based on differential search algorithm (DSA), one of recently developed swarm intelligence-based algorithms. DSA is inspired from seasonal migration behavior of species of the living beings throughout the year for solving highly nonlinear, multivariable, and multimodal optimization problems. The proposed inverse procedure is applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface S-wave velocity profiles. To evaluate calculation efficiency and stability of DSA, four noise-free and four noisy synthetic data sets are firstly inverted. Then, the performance of DSA is compared with that of genetic algorithms (GA) by two noise-free synthetic data sets. Finally, a real-world example from a waste disposal site in NE Italy is inverted to examine the applicability and robustness of the proposed approach on surface wave data. Furthermore, the performance of DSA is compared against that of GA by real data to further evaluate scores of the inverse procedure described here. Simulation results from both synthetic and actual field data demonstrate that differential search algorithm (DSA) applied to nonlinear inversion of surface wave data should be considered good not only in terms of the accuracy but also in terms of the convergence speed. The great advantages of DSA are that the algorithm is simple, robust and easy to implement. Also there are fewer control parameters to tune. [ABSTRACT FROM AUTHOR]

—In this article, a new nature-inspired metaheuristic technique called the differential search algorithm is proposed to solve the optimal power flow problem. The proposed differential search algorithm has been developed and tested under normal and contingency power system conditions. To show the effectiveness of the proposed method, it has been demonstrated on the standard IEEE 30-bus and IEEE 118-bus test systems with different objectives that reflect performance indices of the power system. Obtained results using the proposed technique indicate that the proposed differential search algorithm provides an effective, a robust, and a high-quality solution for the optimal power flow problem. The comparisons of the proposed differential search algorithm results with those reported in the literature reveal the potential and superiority of the proposed algorithm in terms of the optimal solution quality and robustness. [ABSTRACT FROM AUTHOR]

This paper presents an application of Differential Search (DS) algorithm for solving non-convex economic dispatch (ED) problems with the valve loading effects. ED is one of the most challenging problems of power system since it is difficult to determine the optimum generation scheduling to meet the particular load demand with the minimum fuel costs while all constraints are satisfied. In addition, the practical ED problems which are involving non-convex objective functions with equality and inequality constraints including the practical operation constraints of generators such as ramp rate limit, prohibited operating zones and generation limits make it harder to solve the global optimum using conventional methods. In this paper, DS is adopted to find the global optimum results of ED. To demonstrate the effectiveness and feasibility of DS in solving ED, four well known ED test systems with non-convex solution features have been tested and compared with some of the most recently published ED solution methods in literature. The results of this research show that DS is able to find more economical solution than those determined by other methods. [ABSTRACT FROM AUTHOR]

Abstract: In order to solve numerous practical navigational, geodetic and astro-geodetic problems, it is necessary to transform geocentric cartesian coordinates into geodetic coordinates or vice versa. It is very easy to solve the problem of transforming geodetic coordinates into geocentric cartesian coordinates. On the other hand, it is rather difficult to solve the problem of transforming geocentric cartesian coordinates into geodetic coordinates as it is very hard to define a mathematical relationship between the geodetic latitude (φ) and the geocentric cartesian coordinates (X, Y, Z). In this paper, a new algorithm, the Differential Search Algorithm (DS), is presented to solve the problem of transforming the geocentric cartesian coordinates into geodetic coordinates and its performance is compared with the performances of the classical methods (i.e., ) and Computational-Intelligence algorithms (i.e., ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA–ES). The statistical tests realized for the comparison of performances indicate that the problem-solving success of DS algorithm in transforming the geocentric cartesian coordinates into geodetic coordinates is higher than those of all classical methods and Computational-Intelligence algorithms used in this paper. [Copyright &y& Elsevier]