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57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)

作者： Sahman, Mehmet Akif Cinar, Ahmet Cevahir Selcuk Univ Fac Technol Selcuk Univ Campus TR-42075 Konya Turkey

ISBN: (纸本)9781665485005

There are many meta-heuristic algorithms proposed by researchers, and the general feature of these algorithms is that they are modeled by being inspired by the intelligent behaviors of living things in nature, individually or as a group. Basic versions of meta-heuristic algorithms are frequently introduced to solve continuous optimization problems. However, due to their flexibility, they can be adjusted for use in various types of optimization problems. In this research, an improved version of the differential search algorithm (DSAX), which is a population-based meta-heuristic algorithm that has shown its success in solving optimization problems, is proposed to use in solving binary optimization problems. The DSAX has been tested on large-scale binary optimization problems. Moreover, it is compared with the XOR-Based version of the Tree-Seed algorithm, which is well known in the literature. According to the obtained results and the comparisons, it is seen that the DSAX algorithm proposed in this study is superior.

关键词： Meta-Heuristics differential search algorithm XOR logic gate Binary Optimization

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Interpretation of volcanic magnetic anomalies using differential search algorithm: case study from the Kula volcanic park, western Turkiye

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ACTA GEOPHYSICA 2023年第3期71卷 1203-1224页

作者： Ozyalin, Senol Dokuz Eylul Univ Engn Fac Dept Geophys Engn TR-35160 Izmir Turkey

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 Turkiye. The depths and widths of these magmatic bodies lying underneath these volcanic cones are about 450 m and 470 m, respectively.

关键词： Kula volcanic park Aeromagnetic data differential search algorithm Recent volcanic cones

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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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Region based multi-spectral fusion method for remote sensing images using differential search algorithm and IHS transform

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期189卷 116135-116135页

作者： Kurban, Tuba Gazi Univ Fac Engn Dept Comp Engn Ankara Turkey

Remote sensing applications such as clustering, classification, feature extraction, measurement and change detection need fused high-resolution images that contain complementary information coming from multispectral bands. To transfer as much information as possible from source images to the fused image, image fusion can be considered as an optimization problem. In this paper, a new, two-stage, multi-spectral, region based, optimal fusion method is proposed. Panchromatic and infrared bands of Landsat 8 satellite images are fused with K-medoids segmentation and differential search algorithm (DSA). The fused image is combined with the red, green, and blue (RGB) bands of the same area with intensity-hue-saturation (IHS) transform. Experiments carried out on test images indicate that developed method performed better than most of the state-of-theart fusion techniques in terms of both visual and numerical evaluations.

关键词： Optimization differential search algorithm Remote sensing Image fusion Multi-spectral imaging

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Ultrasound image segmentation with multilevel threshold based on differential search algorithm

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IET IMAGE PROCESSING 2019年第6期13卷 998-1005页

作者： Shao, Dangguo Xu, Chunrong Xiang, Yan Gui, Peng Zhu, Xiaofang Zhang, Chao Yu, Zhengtao Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China Wuhan Univ Sch Comp Sci & Technol Wuhan Hubei Peoples R China

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 F-m 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.

关键词： medical image processing image segmentation biomedical ultrasonics ultrasound image segmentation differential search algorithm diagnostic imaging special tissues multilevel threshold segmentation vivo US images good segmentation result

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Cross-cumulative residual entropy-based medical image registration via hybrid differential search algorithm

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2019年第4期29卷 701-710页

作者： Gui, Peng Ling, Wing-Kuen Zhang, Dengyi Xiang, Yan Shao, Dangguo Ma, Lei Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650504 Yunnan Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou Guangdong Peoples R China Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China

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.

关键词： cross-cumulative residual entropy differential search algorithm medical image registration

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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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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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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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differential search algorithm in Deep Neural Network for the Predictive Analysis of Xylitol Production in Escherichia Coli 17th

Differential Search Algorithm in Deep Neural Network for the...

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17th Asia Simulation Conference (AsiaSim)

作者： Yousoff, Siti Noorain Mohmad Baharin, Amirah Abdullah, Afnizanfaizal Univ Teknol Malaysia Fac Comp Synthet Biol Res Grp Johor Baharu 81310 Johor Malaysia

ISBN: (纸本)9789811065026;9789811065019

Xylitol is one of the bio-based chemical products that received well recognition and highly demanded from both food and pharmaceutical industries which led to various experiment to be carried out on various organism to produce xylitol. Recently, E. coli has become the spotlight to be one of the organisms that can be metabolically engineered to produce xylitol by using gene knockout strategy. However, gene knockout strategy required laborious, expensive and time-consuming when conducted in vivo. Motivated by this, in silico experiment has been done to simulate and manipulate the model of E. coli to construct a new E. coli model that will focus on producing xylitol by using Flux Balance Analysis (FBA). In this paper, a new hybrid method called DNNDSA is proposed which consists of both Deep Neural Network (DNN) method and differential search algorithm (DSA) to do the predictive analysis on a newly constructed model of E. coli to predict which gene knockout condition is the best to be applied in metabolic pathway of E. coli to improve the xylitol production.

关键词： Xylitol Escherichia Coli Flux Balance Analysis Deep Neural Network differential search algorithm

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