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22nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Yilmaz, Ali Riza Erkmen, Burcu Yavuz, Oguzhan Yildiz Tekn Univ Elekt & Haberlesme Muhendisligi Bolumu Istanbul Turkey Netas Telekomunikasyon AS Uluslararasi Ar Ge Dept Istanbul Turkey

ISBN: (纸本)9781479948741

In this work, a hardware implementation of differential evolution algorithm (DEA) on field-programmable gate array (FPGA) is realized. The implementation of DEA on FPGA is carried out to find minimum value of an equation with two unknowns. This study is a preparatory work for the training of neural networks with DEA on FPGA which is more complicated.

关键词： FPGA differential evolution algorithm optimization

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Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

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INTERNET OF THINGS 2023年 22卷

作者： Khaleel, Mustafa Ibrahim Univ Sulaimani Kurdistan Reg Govt Coll Sci Comp Dept Sulaimani 46001 Iraq

The job scheduling paradigms include dispatching Internet of Things (IoT) critical services onto processing nodes. Here most energy is consumed in finding suitable virtual machines (VMs) that can execute IoT tasks without resource fragments. Therefore, a significant problem is minimizing energy consumption through efficient task placement that leads to load balance and minimizes resource leakage. To resolve this problem, we proposed a dual-phase metaheuristic algorithm called CSSA-DE. First, we conduct a clustering approach to group computing nodes into effective clusters. Each node is trained at different utilization levels, and the one that can yield the highest Performance-to-Power Ratio (PPR) is selected as the mega cluster head (MCH). Then, we integrated the sparrow search algorithm (SSA) with the differential evolution (DE) algorithm to expand the high search efficiency of finding an appropriate pair task-VM combination. Further, the integration phase can exploit the count of overloaded and underloaded VMs, reducing resource fragments. The performance of CSSA-DE is highly competitive and relatively better in multiple cases compared to state-of-the-art algorithms.

关键词： Cloud data center Virtual machine Energy consumption Sparrow search algorithm differential evolution algorithm

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Coordinated Design of SSSC based Power System Stabilizer using differential evolution algorithm

Coordinated Design of SSSC based Power System Stabilizer usi...

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International Conference on Computational Intelligence and Communication Networks

作者： Sangram Keshori Mohapatra Bhabani Sankar Dash Department of Electrical Engineering C.V. Raman College of Engineering

ISBN: (纸本)9781509000777

In this paper, the contemporaneous coordinated design of power system stabilizer (PSS) and static synchronous series compensator (SSSC) based damping controller is presented. The coordinated design of remote speed deviation (Δω) based PSS with Δωbased SSSC based Proportional-Integral (PI) controller and Δω based PSS with Δω based SSSC lead lag (LL) controller with associated time delay is considered for the proposed analysis. The proposed systems are applied under different loadings and fault locations for effectiveness and robustness. The differential evolution (DE) algorithm is carried out to find out the optimal coordinated control parameters for both single and multi-machine power system. The performance of the proposed controllers are compared and analyzed, It is clear from that Δω based PSS with Δω based LL structured SSSC gives better response than with Δω based PSS with Δω based PI structured SSSC controller in terms of settling time and overshoot.

关键词： Power system stabilizer differential evolution algorithm Static synchronous series compensator Multi machine power system

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An Enhanced differential evolution algorithm Application to Economic Dispatch with Valve-Point Effects and System Losses Considerations

An Enhanced Differential Evolution Algorithm Application to ...

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International Universities Power Engineering Conference

作者： Thanathip Sum-Im Department of Electrical Engineering Faculty of Engineering Srinakharinwirot University

ISBN: (纸本)9781467396578

This paper presents an enhanced differential evolution algorithm (EnDEA) to solve economic dispatch (ED) problem of generating units considering valve-point effects and system losses. The aim of ED problem is to achieve optimal generation scheduling of power plants, which has minimum total production cost associated with technical and economical constraints. The efficiency and effectiveness of the proposed method is initially demonstrated via the analysis of 10-unit test system considering valve-point loading and system losses constraints. Furthermore, multiple fuels of thermal power plant are included and investigated in this ED problem. A detailed comparative study between a conventional differential evolution algorithm (CDEA) and the proposed EnDEA method is presented. Regarding the experimental results, the proposed technique obtained solutions with good accuracy, stable convergence characteristics, and satisfactory calculation time.

关键词： Power generation scheduling Economic dispatch differential evolution algorithm Valve-point effects System losses

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SDERP: Stable-aware differential evolution-based routing protocol for mobile wireless networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第4期38卷

作者： Mekkaoui, Abdelkader Smail, Omar Rebbah, Mohammed Univ Mustapha Stambouli Fac Exact Sci Comp Sci Dept Mascara Algeria

A mobile ad hoc network (MANET) is a network with non-wired communications and non-centralized administration where all its nodes are mobile. Firstly, one of the leading drawbacks of wireless networks is their energy consumption because the constituent nodes of the network have a restricted battery capacity yet greater consumption caused by the mobility of the nodes, their processing power, the frequent sending of data, and so on. Secondly, data packets may be lost due to traffic, noise, or the mobility of nodes, whose data transmission in real-time applications would be compromised by this loss. In order to resolve the problem of energy consumption and that of the loss of data packets for MANETs, we propose a new stable-aware differential evolution-based routing protocol (SDERP). We include a fitness function to select the best new path from all new paths issued at the end of this algorithm. This fitness function is based on energy constraints and link stability. The performance of our proposal is compared with OPAOMDV-EE, FT-AORP, EE-LB-AOMDV and AOMDV. The results show that our SDERP has better network performance than the other routing protocols. Specifically, it obtains a packet delivery ratio that is higher by up to 17%, an overhead ratio reduced by up to 33%, an energy efficiency better by up to 39%, and end-to-end delay reduced by up to 16%. In this paper, we propose a new routing solution named stable-aware differential evolution-based routing protocol (SDERP) for mobile ad hoc networks (MANETs). Our solution used a bio-inspired method derived from the family of evolutionary algorithms known as differential evolution. A fitness function is integrated to select the best paths. This fitness function is based on energy constraint and link stability. image

关键词： differential evolution algorithm energy constraint MANET multipath routing protocol stability constraint

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A mutation operator self-adaptive differential evolution particle swarm optimization algorithm for USV navigation

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FRONTIERS IN NEUROROBOTICS 2022年 16卷

作者： Gong, Yuehong Zhang, Shaojun Luo, Min Ma, Sainan Shandong Jiaotong Univ Sch Nav & Shipping Weihai Peoples R China Harbin Inst Technol Weihai Sch Informat Sci & Engn Weihai Peoples R China Zhejiang Jialan Ocean Elect Co Ltd Zhoushan Peoples R China

To keep the global search capability and robustness for unmanned surface vessel (USV) path planning, an improved differential evolution particle swarm optimization algorithm (DePSO) is proposed in this paper. In the optimization process, approach to optimal value in particle swarm optimization algorithm (PSO) and mutation, hybridization, selection operation in differential evolution algorithm (DE) are combined, and the mutation factor is self-adjusted. First, the particle population is initialized and the optimization objective is determined, the individual and global optimal values are updated. Then differential variation is conducted to produces new variables and cross over with the current individual, the scaling factor is adjusted adaptively with the number of iterations in the mutation process, particle population is updated according to the hybridization results. Finally, the convergence of the algorithm is determined according to the decision standard. Numerical simulation results show that, compared with conventional PSO and DE, the proposed algorithm can effectively reduce the path intersection points, and thus greatly shorten the overall path length.

关键词： differential evolution algorithm hybridization mutation particle swarm optimization unmanned surface vessel path planning scaling factor

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Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction

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ALEXANDRIA ENGINEERING JOURNAL 2024年 106卷 721-734页

作者： Bhatia, Aryan Sharma, Moolchand Alabdulkreem, Eatedal Alruwais, Nuha Saeed, Muhammad Kashif Yahya, Abdulsamad Ebrahim Maharaja Agrasen Inst Technol Dept Comp Sci & Engn GGSIPU Delhi India Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn POB 22459 Riyadh 11495 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Coll Comp & Informat Technol Dept Informat Technol Ar Ar Saudi Arabia

Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources. To address these challenges, machine learning-based methodologies have been adopted to reduce costs and save time. Unfortunately, the effectiveness of these methods has been limited due to the binary classification approach and the lack of empirically validated negative samples. The availability of abundant DTI datasets and protein structure data has enabled the development of new approaches, such as redefining the DTI problem as a regression task. Given this context, we propose an innovative deep-learning approach to predict binding affinities between drugs and targets. Our model, named the Convolution Self-Attention Network with Attention-based Bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), integrates convolutional neural network (CNN) blocks with self-attention mechanisms to create an attention-based bidirectional long short-term memory (BiLSTM) model, followed by fully connected layers. Due to the model's complexity, proper hyperparameter tuning is essential. To optimize this, we employ the differential evolution (DE) technique to select the most suitable hyperparameters. Experimental results demonstrate that the DE-based CSAN-BiLSTM-Att model outperforms previous approaches. Specifically, the model achieved a concordance index of 0.898 and a mean square error of 0.228 on the DAVIS dataset, and a concordance value of 0.971 with a mean square error of 0.014 on the KIBA dataset.

关键词： Convolution Self-Attention Network Attention mechanism Bidirectional LSTM differential evolution algorithm DAVIS & KIBA datasets Drug-target interaction Binding affinity Self-attention convolution neural network Self-attention convolution neural network

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Application Research of differential evolution Algoritm in Resistance Coefficient Identification of Heating Pipeline

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THERMAL ENGINEERING 2024年第6期71卷 534-543页

作者： Zhao, Bingwen Yan, Ruxue Jin, Yu Zheng, Hanyu Zhejiang Sci Tech Univ Keyi Coll Shaoxing 312369 Peoples R China Zhejiang Sci Tech Univ Coll Architectural Engn Hangzhou 310018 Peoples R China Shanghai Linxiang Environm Protect Corp Shanghai 200232 Peoples R China

The district heating system is an important heating mode in the northern cities of China. In recent years, the scale of the district heating system is expanding day by day, the pipe network structure is more and more complex. The problem of hydraulic imbalance of the pipe network is gradually emerging, therefore, it is urgent to establish an accurate and perfect hydraulic simulation model of heating network to assist operation management. Pipe network simulation modeling is one of the important prerequisites to solve the hydraulic imbalance problem of heating pipe network. However, with the increase of service time, the actual resistance coefficient of heating network becomes difficult to obtain, which is one of the key reasons for the low accuracy of pipe network simulation model. In order to overcome this difficulty, this paper proposes to use the resistance coefficient identification model based on the differential evolution algorithm (DEA) to identify the resistance coefficient of the heating pipe network. Based on graph theory, network matrix and the law of conservation of mass, the hydraulic model of the heating pipe network is built, and the nodal pressure method is used to solve the model. On the basis of comprehensive consideration of the mainstream intelligent algorithm, the differential evolution method is selected as the algorithm to identify the resistance coefficient of pipeline. In order to verify the identification effect, the feasibility of the model was verified by calculating the data of three different operating conditions of the practical engineering named "K district heating system". The results demonstrated that the relative errors of the identified resistance coefficients are all within 10, and 98% of the identified values are less than 5%.

关键词： district heating differential evolution algorithm identification of resistance coefficient pipeline hydraulic model optimization design

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DESIGN AND OPTIMIZATION OF A FULLY differential CMOS VARIABLE-GAIN LNA WITH differential evolution algorithm FOR WLAN APPLICATIONS

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2014年第9期23卷 1450124-1450124页

作者： Ziabakhsh, Soheyl Alavi-Rad, Hosein Ahandani, Morteza Alinia Yagoub, Mustapha C. E. Univ Quebec Ecole Technol Super Montreal PQ H3C 1K3 Canada Univ Langaroud Dept Elect Engn Langaroud Branch Gilan Iran Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

In this paper, we optimized the performance of a 2.4 GHz variable gain low-noise amplifier for WLAN applications which provides high dynamic range with relatively low power consumption. First, the differential evolution algorithm was used to optimize the width of input transistors, then the tunable on-chip switching stage method was applied to control the amplifier gain when the input signal increases. The optimization was performed in terms of gain, noise figure (NF), IIP3 and power dissipation. The LNA has achieved a variable gain from 16.55 to 20.45 dB with excellent NF between 1.63 and 1.74 dB. Furthermore, the proposed circuit achieves a third order input intercept point of 6.6 dBm. It consumes only 10 mW from a 1.5 V supply.

关键词： differential evolution algorithm Gain IIP3 low noise amplifier

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Neighbourhood-based small-world network differential evolution with novelty search strategy

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2023年第2期22卷 65-76页

作者： Huang, Ying Lai, Ling Wu, Huafang Gannan Normal Univ Sch Math & Comp Sci Ganzhou Jiangxi Peoples R China

differential evolution (DE) algorithms that focus too much on local search ability will lead to premature convergence, but excessive reliance on global search ability will cause poor search ability. Therefore, balancing global search ability and local search ability is a controversial research topic in DE. In this paper, we propose a DE algorithm based on small-world network topology and novelty search (NSWDE), which uses small-world network to construct dynamic neighbourhoods to change the dynamics of information exchange within populations. The novelty search strategy will continuously search for new behaviours in the search space. Additionally, we set different scaling factors for the two different drivers based on fitness and novelty to regulate the population diversity effectively. To verify the effectiveness of NSWDE, it is compared with several advanced DE algorithms on 18 benchmark test functions;the results show that NSWDE improves both robustness and effectiveness for complex optimisation test functions.

关键词： differential evolution algorithm small-world network novelty search topology structure parameter adaptation

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