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Energy Efficient Clustering and Sink Mobility Protocol Using Hybrid Golden Jackal and Improved Whale Optimization Algorithm for Improving Network Longevity in WSNs
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作者 S B Lenin R Sugumar +2 位作者 J S Adeline Johnsana N Tamilarasan R Nathiya 《China Communications》 2025年第3期16-35,共20页
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability... Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches. 展开更多
关键词 Cluster Heads(CHs) Golden Jackal optimization algorithm(GJOA) Improved Whale optimization algorithm(IWOA) unequal clustering
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Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm 被引量:1
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作者 Zhaodong Wu Yasong Luo Shengliang Hu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期529-540,共12页
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t... Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources. 展开更多
关键词 Electronic countermeasure Offboard active decoy USV cluster Jamming formation optimization Improved PSO algorithm
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Optimization of Nesting Systems in Shipbuilding:A Review
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作者 Sari Wanda Rulita Gunawan Muzhoffar Dimas Angga Fakhri 《哈尔滨工程大学学报(英文版)》 2025年第1期152-175,共24页
This review article provides a comprehensive analysis of nesting optimization algorithms in the shipbuilding industry,emphasizing their role in improving material utilization,minimizing waste,and enhancing production ... This review article provides a comprehensive analysis of nesting optimization algorithms in the shipbuilding industry,emphasizing their role in improving material utilization,minimizing waste,and enhancing production efficiency.The shipbuilding process involves the complex cutting and arrangement of steel plates,making the optimization of these operations vital for cost-effectiveness and sustainability.Nesting algorithms are broadly classified into four categories:exact,heuristic,metaheuristic,and hybrid.Exact algorithms ensure optimal solutions but are computationally demanding.In contrast,heuristic algorithms deliver quicker results using practical rules,although they may not consistently achieve optimal outcomes.Metaheuristic algorithms combine multiple heuristics to effectively explore solution spaces,striking a balance between solution quality and computational efficiency.Hybrid algorithms integrate the strengths of different approaches to further enhance performance.This review systematically assesses these algorithms using criteria such as material dimensions,part geometry,component layout,and computational efficiency.The findings highlight the significant potential of advanced nesting techniques to improve material utilization,reduce production costs,and promote sustainable practices in shipbuilding.By adopting suitable nesting solutions,shipbuilders can achieve greater efficiency,optimized resource management,and superior overall performance.Future research directions should focus on integrating machine learning and real-time adaptability to further enhance nesting algorithms,paving the way for smarter,more sustainable manufacturing practices in the shipbuilding industry. 展开更多
关键词 Cutting plate Nesting algorithms Nesting optimization Shipbuilding efficiency algorithmic optimization
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Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks
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作者 P.Vinoth Kumar K.Venkatesh 《China Communications》 SCIE CSCD 2024年第10期113-131,共19页
Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach ess... Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic algorithms.This adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par with the competitive CH selection schemes under different number of data transmission rounds.The statistical analysis of the proposed HSWOA-DCP scheme also confirmed its energy stability with respect to ANOVA test. 展开更多
关键词 CLUSTERING energy stability network lifetime seagull optimization algorithm(SEOA) whale optimization algorithm(WOA) wireless sensor networks(WSNs)
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Hybrid Prairie Dog and Beluga Whale Optimization Algorithm for Multi-Objective Load Balanced-Task Scheduling in Cloud Computing Environments
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作者 K Ramya Senthilselvi Ayothi 《China Communications》 SCIE CSCD 2024年第7期307-324,共18页
The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource pr... The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time. 展开更多
关键词 Beluga Whale optimization algorithm(BWOA) cloud computing Improved Hopcroft-Karp algorithm Infrastructure as a Service(IaaS) Prairie Dog optimization algorithm(PDOA) Virtual Machine(VM)
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Topological optimization of ballistic protective structures through genetic algorithms in a vulnerability-driven environment
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作者 Salvatore Annunziata Luca Lomazzi +1 位作者 Marco Giglio Andrea Manes 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第10期125-137,共13页
Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulne... Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulnerability is to introduce protective structures to intercept and possibly stop threats.However,this type of solution can lead to a significant increase in weight,affecting the performance of the aircraft.For this reason,it is crucial to study possible solutions that allow reducing the vulnerability of the aircraft while containing the increase in structural weight.One possible strategy is to optimize the topology of protective solutions to find the optimal balance between vulnerability and the weight of the added structures.Among the many optimization techniques available in the literature for this purpose,multiobjective genetic algorithms stand out as promising tools.In this context,this work proposes the use of a in-house software for vulnerability calculation to guide the process of topology optimization through multi-objective genetic algorithms,aiming to simultaneously minimize the weight of protective structures and vulnerability.In addition to the use of the in-house software,which itself represents a novelty in the field of topology optimization of structures,the method incorporates a custom mutation function within the genetic algorithm,specifically developed using a graph-based approach to ensure the continuity of the generated structures.The tool developed for this work is capable of generating protections with optimized layouts considering two different types of impacting objects,namely bullets and fragments from detonating objects.The software outputs a set of non-dominated solutions describing different topologies that the user can choose from. 展开更多
关键词 Topological optimization Protective structure Genetic algorithm SURVIVABILITY VULNERABILITY
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Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs
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作者 N Tamilarasan SB Lenin +1 位作者 P Mukunthan NC Sendhilkumar 《China Communications》 SCIE CSCD 2024年第9期159-178,共20页
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw... In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches. 展开更多
关键词 Adaptive Grasshopper optimization algorithm(AGOA) Cluster Head(CH) network lifetime Teaching-Learning-based optimization algorithm(TLOA) Wireless Sensor Networks(WSNs)
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Application of Adaptive Whale Optimization Algorithm Based BP Neural Network in RSSI Positioning
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作者 Duo Peng Mingshuo Liu Kun Xie 《Journal of Beijing Institute of Technology》 EI CAS 2024年第6期516-529,共14页
The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A a... The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm. 展开更多
关键词 wireless sensor network received signal strength neural network whale optimization algorithm adaptive weight factor extended Kalman filter
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HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA 被引量:18
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作者 罗德林 杨忠 +2 位作者 段海滨 吴在桂 沈春林 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第1期20-26,共7页
Combining the heuristic algorithm (HA) developed based on the specific knowledge of the cooperative multiple target attack (CMTA) tactics and the particle swarm optimization (PSO), a heuristic particle swarm opt... Combining the heuristic algorithm (HA) developed based on the specific knowledge of the cooperative multiple target attack (CMTA) tactics and the particle swarm optimization (PSO), a heuristic particle swarm optimization (HPSO) algorithm is proposed to solve the decision-making (DM) problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts (FAs) to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem. 展开更多
关键词 air combat decision-making cooperative multiple target attack particle swarm optimization heuristic algorithm
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Combining the genetic algorithms with artificial neural networks for optimization of board allocating 被引量:2
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作者 曹军 张怡卓 岳琪 《Journal of Forestry Research》 SCIE CAS CSCD 2003年第1期87-88,共2页
This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in boa... This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum. 展开更多
关键词 Artificial neural network Genetic algorithms Back propagation model (BP model) optimization
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APPROXIMATION TECHNIQUES FOR APPLICATION OF GENETIC ALGORITHMS TO STRUCTURAL OPTIMIZATION 被引量:1
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作者 金海波 丁运亮 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2003年第2期147-154,共8页
Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex str... Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model. 展开更多
关键词 approximation techniques segment approximation model genetic algorithms structural optimization sensitivity analysis
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Optimization of Linear Antenna Arrays Based on Genetic Algorithms
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作者 王宏建 高本庆 刘瑞祥 《Journal of Beijing Institute of Technology》 EI CAS 2002年第2期180-183,共4页
The methods of moment and genetic algorithm (GA) are combined to optimize the Yagi Uda antenna array and Log periodic dipole antenna (LPDA) array. The element lengths and spacing are optimized for the Yagi Uda arra... The methods of moment and genetic algorithm (GA) are combined to optimize the Yagi Uda antenna array and Log periodic dipole antenna (LPDA) array. The element lengths and spacing are optimized for the Yagi Uda array; while the ratio factor of spacing to length as well as the ratio of length to diameter of the elements are optimized for LPDA array. The results show that the main parameters, such as gain and pattern, have been improved apparently; and the high back lobe level of LPDA can be reduced greatly, therefore, GA is a very competent method for optimizing the linear array as well as in other fields. 展开更多
关键词 GAIN front to back ratio genetic algorithm optimization Yagi Uda antenna Log periodic dipole antenna
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NEW LIMITED MEMORY SYMMETRIC RANK ONE ALGORITHM FOR LARGE-SCALE UNCONSTRAINED OPTIMIZATION
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作者 刘浩 倪勤 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第3期235-239,共5页
A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one (SSR1) update with the limited memory and nonmonotone line search technique. In this algorithm, th... A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one (SSR1) update with the limited memory and nonmonotone line search technique. In this algorithm, the descent search direction is generated by inverse limited memory SSR1 update, thus simplifying the computation. Numerical comparison of the algorithm and the famous limited memory BFGS algorithm is given. Comparison results indicate that the new algorithm can process a kind of large-scale unconstrained optimization problems. 展开更多
关键词 optimization large scale systems symmetric rank one update nonmonotone line search limitedmemory algorithm
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Multi-surrogate framework with an adaptive selection mechanism for production optimization 被引量:1
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作者 Jia-Lin Wang Li-Ming Zhang +10 位作者 Kai Zhang Jian Wang Jian-Ping Zhou Wen-Feng Peng Fa-Liang Yin Chao Zhong Xia Yan Pi-Yang Liu Hua-Qing Zhang Yong-Fei Yang Hai Sun 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期366-383,共18页
Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing researc... Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem.A surrogate model that has demonstrated success in one scenario may not perform as well in others.In the absence of prior knowledge,finding a promising surrogate model that performs well for an unknown reservoir is challenging.Moreover,the optimization process often relies on a single evolutionary algorithm,which can yield varying results across different cases.To address these limitations,this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism(MSFASM)to tackle production optimization problems.MSFASM consists of two stages.In the first stage,a reduced-dimensional broad learning system(BLS)is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period.In the second stage,the multi-objective algorithm,non-dominated sorting genetic algorithm II(NSGA-II),is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models.A novel optimal point criterion is utilized in this stage to select the Pareto solutions,thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator.The two stages are combined using sequential transfer learning.From the two most important perspectives of an evolutionary algorithm and a surrogate model,the proposed method improves adaptability to optimization problems of various reservoir types.To verify the effectiveness of the proposed method,four 100-dimensional benchmark functions and two reservoir models are tested,and the results are compared with those obtained by six other surrogate-model-based methods.The results demonstrate that our approach can obtain the maximum net present value(NPV)of the target production optimization problems. 展开更多
关键词 Production optimization Multi-surrogate models Multi-evolutionary algorithms Dimension reduction Broad learning system
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Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm 被引量:12
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作者 Chandan Guria Kiran K Goli Akhilendra K Pathak 《Petroleum Science》 SCIE CAS CSCD 2014年第1期97-110,共14页
A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered f... A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered for optimization.Several multi-objective optimization problems involving twoand three-objective functions were formulated and solved to fix optimal drilling variables.The important objectives are:(i) maximizing drilling depth,(ii) minimizing drilling time and (iii) minimizing drilling cost with fractional drill bit tooth wear as a constraint.Important time dependent decision variables are:(i) equivalent circulation mud density,(ii) drill bit rotation,(iii) weight on bit and (iv) Reynolds number function of circulating mud through drill bit nozzles.A set of non-dominated optimal Pareto frontier is obtained for the two-objective optimization problem whereas a non-dominated optimal Pareto surface is obtained for the three-objective optimization problem.Depending on the trade-offs involved,decision makers may select any point from the optimal Pareto frontier or optimal Pareto surface and hence corresponding values of the decision variables that may be selected for optimal drilling operation.For minimizing drilling time and drilling cost,the optimum values of the decision variables are needed to be kept at the higher values whereas the optimum values of decision variables are at the lower values for the maximization of drilling depth. 展开更多
关键词 Drilling performance rate of penetration abnormal pore pressure genetic algorithm multi-objective optimization
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A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems 被引量:8
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作者 Mohamed Hamdy Anh-Tuan Nguyen +1 位作者 Jan L.M. Hensen 侯恩哲 《建筑节能》 CAS 2016年第6期4-4,共1页
Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms hav... Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. 展开更多
关键词 Multi-objective optimization algorithmS EXPERIMENTATION Building simulation Comparison
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Power optimization of gas pipelines via an improved particle swarm optimization algorithm 被引量:4
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作者 Zheng Zhiwei Wu Changchun 《Petroleum Science》 SCIE CAS CSCD 2012年第1期89-92,共4页
In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization mod... In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization model for gas pipelines is developed and an improved particle swarm optimization algorithm is applied. Based on the testing of the parameters involved in the algorithm which need to be defined artificially, the values of these parameters have been recommended which can make the algorithm reach efficiently the approximate optimum solution with required accuracy. Some examples have shown that the relative error of the particle swarm optimization over ant colony optimization and dynamic programming is less than 1% and the computation time is much less than that of ant colony optimization and dynamic programming. 展开更多
关键词 Gas pipeline operation optimization particle swarm optimization algorithm
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Optimization of perforation distribution for horizontal wells based on genetic algorithms 被引量:4
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作者 Wang Zhiming Wei Jianguang +2 位作者 Zhang Jian Gong Bin Yan Haiyun 《Petroleum Science》 SCIE CAS CSCD 2010年第2期232-238,共7页
Early water breakthrough and a rapid increase in water cut are always observed in high- permeability completion intervals when perforations are uniformly distributed in the wellbore in heterogeneous reservoirs. Optimi... Early water breakthrough and a rapid increase in water cut are always observed in high- permeability completion intervals when perforations are uniformly distributed in the wellbore in heterogeneous reservoirs. Optimization of perforating parameters in partitioned sections in horizontal intervals helps homogenize the inflow from the reservoir and thus is critically important for enhanced oil recovery. This paper derives a coupled reservoir-wellbore flow model based on inflow controlling theory. Genetic algorithms are applied to solving the model as they excel in obtaining the global optimum of discrete functions. The optimized perforating strategy applies a low perforation density in high- permeability intervals and a high perforation density in low-permeability intervals. As a result, the inflow profile is homogenized and idealized. 展开更多
关键词 Well completion perforation optimization genetic algorithms PARTITION horizontal well
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A Method for Crude Oil Selection and Blending Optimization Based on Improved Cuckoo Search Algorithm 被引量:7
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作者 Yang Huihua Ma Wei +2 位作者 Zhang Xiaofeng Li Hu Tian Songbai 《China Petroleum Processing & Petrochemical Technology》 SCIE CAS 2014年第4期70-78,共9页
Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a ... Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming(MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and transforms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We applied the Improved Cuckoo Search(ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efficiency. The blending solution can provide a reference for refineries to find the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials. 展开更多
关键词 CRUDE OIL similarity CRUDE OIL SELECTION BLENDING optimization MIXED-INTEGER nonlinear programming CuckooSearch algorithm
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Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm 被引量:9
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作者 Wang Shijin Li Qingyun +1 位作者 Cao Xi Li Haiyun 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2016年第4期469-478,共10页
Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective funct... Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%. 展开更多
关键词 air route network planning three area avoidance optimization of air route network node adaptive ant colony algorithm grid environment
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