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Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm

COMPUTERS & GEOSCIENCES 2012年 46卷 229-247页

作者： Civicioglu, Pinar Erciyes Univ Dept Aircraft Elect & Elect Coll Aviat Kayseri Turkey

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 (phi) 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., Borkowski, 1989;Bowring, 1976;Fukushima, 2006;Heikkinen, 1982;Jones, 2002;Zhang, 2005;Borkowski, 1987;Shu, 2010 and Lin, 1995) 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. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Coordinate transformation Geocentric cartesian coordinates Geodetic coordinates differential search algorithm

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Improved differential search algorithm based dynamic resource allocation approach for cloud application

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NEURAL COMPUTING & APPLICATIONS 2019年第8期31卷 3431-3442页

作者： Ma, Anxiang Gao, Yan Huang, Liping Zhang, Bin Northeastern Univ Sch Comp Sci & Engn Shenyang Liaoning Peoples R China

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.

关键词： Cloud computing differential search algorithm Dynamic resource allocation Deep belief networks

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Parameter estimation by differential search algorithm from horizontal loop electromagnetic (HLEM) data

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JOURNAL OF APPLIED GEOPHYSICS 2018年 149卷 77-94页

作者： Alkan, Hilal Balkaya, Caglayan Suleyman Demirel Univ Grad Sch Nat & Appl Sci East Campus TR-32260 Isparta Turkey Suleyman Demirel Univ Engn Fac Dept Geophys Engn West Campus TR-32260 Isparta Turkey

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. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Inversion differential search algorithm Metaheuristic Electromagnetic Slingram

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Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

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APPLIED SOFT COMPUTING 2017年 52卷 160-175页

作者： Guha, Dipayan Roy, Provas Kumar Banerjee, Subrata Natl Inst Technol Durgapur Dept Elect Engn Durgapur W Bengal India Kalyani Govt Engn Coll Kalyani W Bengal India

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. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Automatic generation control differential search algorithm Proportional integral derivative controller with derivative filter Sensitivity analysis Transient responses

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Adaptive differential search algorithm with multi-strategies for global optimization problems

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NEURAL COMPUTING & APPLICATIONS 2019年第12期31卷 8423-8440页

作者： Chu, Xianghua Gao, Da Chen, Jiansheng Cui, Jianshuang Cui, Can Xu, Su Xiu Qin, Quande Shenzhen Univ Dept Management Sci Coll Management Shenzhen Peoples R China Shenzhen Univ Inst Big Data Intelligent Management & Decis Shenzhen Peoples R China Univ Sci & Technol Beijing Sch Econ & Management Beijing Peoples R China Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ USA Jinan Univ Inst Phys Internet Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China

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 Levy 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.

关键词： Swarm intelligence differential search algorithm Gradient search Levy flight

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An implementation of differential search algorithm (DSA) for inversion of surface wave data

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JOURNAL OF APPLIED GEOPHYSICS 2014年 111卷 334-345页

作者： Song, Xianhai Li, Lei Zhang, Xueqiang Shi, Xinchun Huang, Jianquan Cai, Jianchao Jin, Si Ding, Jianping China Univ Geosci Inst Geophys & Geomat Hubei Subsurface Multiscale Imaging Lab SMIL Wuhan 430074 Hubei Peoples R China China Univ Geosci Inst Geophys & Geomat Subsurface Imaging & Sensing Lab Wuhan 430074 Hubei Peoples R China

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. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Swarm intelligence Genetic algorithms differential search algorithm Surface waves Dispersion curves

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Optimal reactive-power dispatch using differential search algorithm

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ELECTRICAL ENGINEERING 2017年第1期99卷 213-225页

作者： Abaci, Kadir Yamacli, Volkan Mersin Univ Elect & Elect Engn Dept Ciftlikkoy Campus TR-33434 Yenisehir Mersin Turkey

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.

关键词： Optimal reactive-power dispatch Optimization differential search algorithm Heuristic methods

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Optimal Power Flow Using differential search algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2014年第15期42卷 1683-1699页

作者： Bouchekara, Houssem Rafik El-Hana Abido, Mohamed Ali Univ Constantine 1 Dept Elect Engn LEC Constantine Elect Engn Lab Constantine 25000 Algeria King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

-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.

关键词： power system optimization optimal power flow voltage profile metaheuristic differential search algorithm voltage stability

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Using differential search algorithm for solving optimal frequency regulation problem in interconnected power system

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2017年第3期9卷 182-195页

作者： Kouba, Nour El Yakine Menaa, Mohamed Hasni, Mourad Boudour, Mohamed USTHB Lab Elect & Ind Syst LSEI BP 32 El Alia Algiers 16111 Algeria

In order to improve the frequency stability and control in an interconnected power system including various power generating units, the automatic generation control (AGC) needs to be more robust and efficient. On the other hand, the difficulties in AGC are not only to design a robust controller but it also to optimise its parameters effectively for a robust optimal solution. In this paper, an application of a new efficient optimisation method called the differential search algorithm (DSA) was proposed to solve the AGC problem. The proposed approach was applied to reach the proportional-integral-derivative (PID) controller parameters. This latter was used to maintain the frequency at nominal value using the integral time multiplied by absolute error (ITAE) as fitness function. To show the robustness of the proposed strategy, a four area interconnected electrical network was investigated for the simulation. The obtained DSA results were compared to other approaches available in literature. The simulation results of this research show that DSA strategy is able to solve AGC problem and achieve less frequency and tie-line power flow deviations than those determined by other methods used in this paper.

关键词： automatic generation control AGC load frequency control LFC interconnected power system optimal control differential search algorithm DSA

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Silicon on Insulator C-VTFET Based Design of low Complexity Sparse Quadrature Mirror Filter Using differential search algorithm

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SILICON 2022年第17期14卷 11545-11560页

作者： Singh, Hitendra Dwivedi, Atul Kumar Nagaria, Deepak Bundelkhand Inst Engn & Technol Dept Elect & Commun Engn Jhansi 284128 Uttar Pradesh India

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 firm 1.1 eV to 0.7 eV. The highest I-ON current and minimum I(OFF )current is reported for the proposed device is I (3.62 x 10(-4) A/mu m) and (1.58 x 10(-18) A/mu m) respectively. The ON/OFF current ratio out recorded as similar to 10(13) 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 H-0(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

关键词： Silicon-on-insulator complementary VTFET Heterojunction differential search algorithm Evolutionary optimization algorithms Quadrature mirror filter

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