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Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN
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作者 Imtiaz Ahmad Tariq Hussain +3 位作者 Babar Shah Altaf Hussain Iqtidar Ali Farman Ali 《Computers, Materials & Continua》 SCIE EI 2024年第6期3585-3629,共45页
Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embe... Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks. 展开更多
关键词 Wireless sensor network cluster head selection low energy adaptive clustering hierarchy accelerated particle swarm optimization
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Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach 被引量:1
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作者 Guijun Ma Zidong Wang +4 位作者 Weibo Liu Jingzhong Fang Yong Zhang Han Ding Ye Yuan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第7期1530-1543,共14页
The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging t... The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA. 展开更多
关键词 Deep transfer learning domain adaptation hyperparameter selection lithium-ion batteries(LIBs) particle swarm optimization state of health estimation(SOH)
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Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization
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作者 Zhonghao Qian Hanyi Ma +5 位作者 Jun Rao Jun Hu Lichengzi Yu Caoyi Feng Yunxu Qiu Kemo Ding 《Energy Engineering》 EI 2023年第9期2013-2027,共15页
The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved p... The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm. 展开更多
关键词 Offshore wind farms improved particle swarm optimization reactive power optimization adaptive weight asynchronous learning factor voltage stability
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Adaptive multi-feature tracking in particle swarm optimization based particle filter framework 被引量:7
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作者 Miaohui Zhang Ming Xin Jie Yang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第5期775-783,共9页
This paper proposes a particle swarm optimization(PSO) based particle filter(PF) tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state t... This paper proposes a particle swarm optimization(PSO) based particle filter(PF) tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance. 展开更多
关键词 particle filter particle swarm optimization adaptive weight adjustment visual tracking
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A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation 被引量:7
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作者 ZHOU Xiao-Jun YANG Chun-Hua +1 位作者 GUI Wei-Hua DONG Tian-Xue 《自动化学报》 EI CSCD 北大核心 2014年第7期1339-1347,共9页
关键词 粒子群优化算法 随机变量函数 突变 PSO算法 随机函数 收敛性分析 算法性能 人口密度
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A New Clustering Algorithm Using Adaptive Discrete Particle Swarm Optimization in Wireless Sensor Network 被引量:3
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作者 余朝龙 郭文忠 《Journal of Donghua University(English Edition)》 EI CAS 2012年第1期19-22,共4页
Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one... Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more. 展开更多
关键词 load balancing energy consumption balancing cluster head(CH) adaptive discrete particle swarm optimization (ADPSO)
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Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 被引量:3
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作者 顾文斌 唐敦兵 郑堃 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期559-567,共9页
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ... An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms. 展开更多
关键词 job-shop scheduling problem(JSP) hormone modulation mechanism improved adaptive particle swarm optimization(IAPSO) algorithm minimum makespan
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Adaptive stochastic resonance method for weak signal detection based on particle swarm optimization 被引量:6
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作者 XING Hongyan ZHANG Qiang LU Chunxia 《Instrumentation》 2015年第2期3-10,共8页
In order to solve the parameter adjustment problems of adaptive stochastic resonance system in the areas of weak signal detection,this article presents a new method to enhance the detection efficiency and availability... In order to solve the parameter adjustment problems of adaptive stochastic resonance system in the areas of weak signal detection,this article presents a new method to enhance the detection efficiency and availability in the system of two-dimensional Duffing based on particle swarm optimization.First,the influence of different parameters on the detection performance is analyzed respectively.The correlation between parameter adjustment and stochastic resonance effect is also discussed and converted to the problem of multi-parameter optimization.Second,the experiments including typical system and sea clutter data are conducted to verify the effect of the proposed method.Results show that the proposed method is highly effective to detect weak signal from chaotic background,and enhance the output SNR greatly. 展开更多
关键词 adaptive stochastic resonance two-dimensional Duffing oscillator weak signal detection particle swarm optimization
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Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation
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作者 Yan Wu Genqin Sun +4 位作者 Keming Su Liang Liu Huaijin Zhang Bingsheng Chen Mengshan Li 《Journal of Computer and Communications》 2017年第13期9-20,共12页
In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based o... In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems. 展开更多
关键词 Improved particle swarm optimization Algorithm Double POPULATIONS MULTI-OBJECTIVE adaptive Strategy CHAOTIC SEQUENCE
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Adaptive Particle Swarm Optimization Data Hiding for High Security Secret Image Sharing
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作者 S.Lakshmi Narayanan 《Computer Systems Science & Engineering》 SCIE EI 2022年第12期931-946,共16页
The main aim of this work is to improve the security of data hiding forsecret image sharing. The privacy and security of digital information have becomea primary concern nowadays due to the enormous usage of digital t... The main aim of this work is to improve the security of data hiding forsecret image sharing. The privacy and security of digital information have becomea primary concern nowadays due to the enormous usage of digital technology.The security and the privacy of users’ images are ensured through reversible datahiding techniques. The efficiency of the existing data hiding techniques did notprovide optimum performance with multiple end nodes. These issues are solvedby using Separable Data Hiding and Adaptive Particle Swarm Optimization(SDHAPSO) algorithm to attain optimal performance. Image encryption, dataembedding, data extraction/image recovery are the main phases of the proposedapproach. DFT is generally used to extract the transform coefficient matrix fromthe original image. DFT coefficients are in float format, which assists in transforming the image to integral format using the round function. After obtainingthe encrypted image by data-hider, additional data embedding is formulated intohigh-frequency coefficients. The proposed SDHAPSO is mainly utilized for performance improvement through optimal pixel location selection within the imagefor secret bits concealment. In addition, the secret data embedding capacityenhancement is focused on image visual quality maintenance. Hence, it isobserved from the simulation results that the proposed SDHAPSO techniqueoffers high-level security outcomes with respect to higher PSNR, security level,lesser MSE and higher correlation than existing techniques. Hence, enhancedsensitive information protection is attained, which improves the overall systemperformance. 展开更多
关键词 Image sharing separable data hiding using adaptive particle swarm optimization(SDHAPSO) SECURITY access control
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Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot
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作者 Rega Rajendra Dilip K. Pratihar 《Intelligent Control and Automation》 2011年第4期430-449,共20页
The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipula... The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected. 展开更多
关键词 MANIPULATOR OPTIMAL Structure adaptive CONTROLLER GENETIC Algorithm NEURAL Networks particle swarm optimization
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Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization
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作者 朱红求 《High Technology Letters》 EI CAS 2009年第3期267-271,共5页
To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning (CBR) with adaptive par- ticle ... To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning (CBR) with adaptive par- ticle swarm optimization (PSO). The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model. 展开更多
关键词 lead and zinc smelting permeability index prediction case-based reasoning (CBR) adaptive particle swarm optimization (PS0)
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Dynamical Adaptive Particle Swarm Algorithm and Its Application to Optimization of PID Parameters
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作者 Jimin Li Guolin Yu 《American Journal of Operations Research》 2012年第3期448-451,共4页
Based on a new adaptive Particle Swarm Optimization algorithm with dynamically changing inertia weight (DAPSO), It is used to optimize parameters in PID controller. Compared to conventional PID methods, the simulation... Based on a new adaptive Particle Swarm Optimization algorithm with dynamically changing inertia weight (DAPSO), It is used to optimize parameters in PID controller. Compared to conventional PID methods, the simulation shows that this new method makes the optimization perfectly and convergence quickly. 展开更多
关键词 particle swarm optimization DYNAMICAL adaptive PID Automatic REGULATION System
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Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy
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作者 Mengshan Li Liang Liu +4 位作者 Genqin Sun Keming Su Huaijin Zhang Bingsheng Chen Yan Wu 《Journal of Computer and Communications》 2017年第12期13-23,共11页
To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The se... To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum. 展开更多
关键词 particle swarm Algorithm CHAOTIC SEQUENCES SELF-adaptive STRATEGY MULTI-OBJECTIVE optimization
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Adaptive Parallel Particle Swarm Optimization Algorithm Based on Dynamic Exchange of Control Parameters
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作者 Masaaki Suzuki 《American Journal of Operations Research》 2016年第5期401-413,共14页
Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local s... Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance. 展开更多
关键词 swarm Intelligence particle swarm optimization Global optimization Metaheuristics adaptive Parameter Tuning
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Particle Swarm Optimization with Directed Mutation
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作者 王杰 李红文 《Journal of Donghua University(English Edition)》 EI CAS 2016年第5期774-780,共7页
In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate p... In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate premature convergence problem,this paper presents a new algorithm,which is called particle swarm optimization(PSO) with directed mutation,or DMPSO.The main idea of this algorithm is to "let the best particle(the smallest fitness of the particle swarm) become more excellent and the worst particle(the largest fitness of the particle swarm) try to be excellent".The new algorithm is tested on a set of eight benchmark functions,and compared with those of other four PSO variants.The experimental results illustrate the effectiveness and efficiency of the DMPSO.The comparisons show that DMPSO significantly improves the performance of PSO and searching accuracy. 展开更多
关键词 swarm fitness benchmark illustrate premature mutation searching mutation iteration trapped
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Improved particle swarm optimization algorithm for fuzzy multi-class SVM 被引量:17
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作者 Ying Li Bendu Bai Yanning Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第3期509-513,共5页
An improved particle swarm optimization(PSO) algorithm is proposed to train the fuzzy support vector machine(FSVM) for pattern multi-classification.In the improved algorithm,the particles studies not only from its... An improved particle swarm optimization(PSO) algorithm is proposed to train the fuzzy support vector machine(FSVM) for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar(SAR) target recognition of moving and stationary target acquisition and recognition(MSTAR) dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training. 展开更多
关键词 particle swarm optimization(PSO) fuzzy support vector machine(FSVM) adaptive mutation multi-classification.
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Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm 被引量:8
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作者 Yan Xiang Shu-yan Fu +2 位作者 Kai Zhu Hui Yuan Zhi-yuan Fang 《Water Science and Engineering》 EI CAS CSCD 2017年第1期70-77,共8页
Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam,... Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization (PSO) algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly. 展开更多
关键词 Monitoring model particle swarm optimization algorithm Earth rock dam Lagging effect TYPHOON Seepage pressure mutation factor Piezometric level
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Improved particle swarm optimization algorithm for multi-reservoir system operation 被引量:2
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作者 Jun ZHANG Zhen WU +1 位作者 Chun-tian CHENG Shi-qin ZHANG 《Water Science and Engineering》 EI CAS 2011年第1期61-73,共13页
In this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimizati... In this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm. 展开更多
关键词 particle swarm optimization self-adaptive exponential inertia weight coefficient multi-reservoir system operation hydroelectric power generation Minjiang Basin
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Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction
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作者 Jiu-Qiang Yang Nian-Tian Lin +3 位作者 Kai Zhang Yan Cui Chao Fu Dong Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2329-2344,共16页
Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the i... Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs. 展开更多
关键词 Multicomponent seismic data Deep learning adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction
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