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Parametric Optimization Design of Aircraft Based on Hybrid Parallel Multi-objective Tabu Search Algorithm 认领 引用 被引量：13
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作者 邱志平 张宇星 《Chinese Journal of Aeronautics》 SCIE EI CAS 2010年第4期430-437,共8页
For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search ... For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm. 展开更多
关键词 aircraft design conceptual design multi-objective optimization tabu search genetic algorithm Pareto optimal
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An Improved Cuckoo Search Algorithm for Multi-Objective Optimization 认领 引用 被引量：2
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作者 TIAN Mingzheng HOU Kuolin +1 位作者 WANG Zhaowei WAN Zhongping 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2017年第4期289-294,共6页
The recently proposed Cuckoo search algorithm is an evolutionary algorithm based on probability. It surpasses other algorithms in solving the multi-modal discontinuous and nonlinear problems. Searches made by it are v... The recently proposed Cuckoo search algorithm is an evolutionary algorithm based on probability. It surpasses other algorithms in solving the multi-modal discontinuous and nonlinear problems. Searches made by it are very efficient because it adopts Levy flight to carry out random walks. This paper proposes an improved version of cuckoo search for multi-objective problems（IMOCS）. Combined with nondominated sorting, crowding distance and Levy flights, elitism strategy is applied to improve the algorithm. Then numerical studies are conducted to compare the algorithm with DEMO and NSGA-II against some benchmark test functions. Result shows that our improved cuckoo search algorithm convergences rapidly and performs efficienly. 展开更多
关键词 multi-objective optimization evolutionary algorithm Cuckoo search Levy flight
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Quantum walk search algorithm for multi-objective searching with iteration auto-controlling on hypercube 认领 引用 被引量：1
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作者 Yao-Yao Jiang Peng-Cheng Chu +1 位作者 Wen-Bin Zhang Hong-Yang Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第4期157-162,共6页
Shenvi et al.have proposed a quantum algorithm based on quantum walking called Shenvi-Kempe-Whaley(SKW)algorithm,but this search algorithm can only search one target state and use a specific search target state vector... Shenvi et al.have proposed a quantum algorithm based on quantum walking called Shenvi-Kempe-Whaley(SKW)algorithm,but this search algorithm can only search one target state and use a specific search target state vector.Therefore,when there are more than two target nodes in the search space,the algorithm has certain limitations.Even though a multiobjective SKW search algorithm was proposed later,when the number of target nodes is more than two,the SKW search algorithm cannot be mapped to the same quotient graph.In addition,the calculation of the optimal target state depends on the number of target states m.In previous studies,quantum computing and testing algorithms were used to solve this problem.But these solutions require more Oracle calls and cannot get a high accuracy rate.Therefore,to solve the above problems,we improve the multi-target quantum walk search algorithm,and construct a controllable quantum walk search algorithm under the condition of unknown number of target states.By dividing the Hilbert space into multiple subspaces,the accuracy of the search algorithm is improved from pc=(1/2)-O(1)to pc=1-O(1).And by adding detection gate phase,the algorithm can stop when the amplitude of the target state becomes the maximum for the first time,and the algorithm can always maintain the optimal number of iterations,so as to reduce the number of unnecessary iterations in the algorithm process and make the number of iterations reach tf=(π/2)(?). 展开更多
关键词 multi-objective quantum walk search algorithm accurate probability
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Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm 认领 引用 被引量：7
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作者 高贵兵 张国军 +2 位作者 黄刚 朱海平 顾佩华 《Journal of Central South University》 SCIE EI CAS 2012年第2期433-442,共10页
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency... The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II. 展开更多
关键词 material distribution routing problem multi-objective optimization evolutionary algorithm local search
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Even Search in a Promising Region for Constrained Multi-Objective Optimization 认领 引用 被引量：9
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作者 Fei Ming Wenyin Gong Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期474-486,共13页
In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However,... In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs. 展开更多
关键词 Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems
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A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions 认领 引用 被引量：4
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作者 YANG Yun WU Jianfeng +2 位作者 SUN Xiaomin LIN Jin WU Jichun 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2012年第1期246-255,共10页
In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under va... In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources. 展开更多
关键词 seawater intrusion multi-objective optimization niched Pareto tabu search combined with genetic algorithm niched Pareto tabu search genetic algorithm
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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems 认领 引用
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作者 Farouq Zitouni Saad Harous +4 位作者 Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja  Kherchouche 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期219-265,共47页
Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengt... Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios. 展开更多
关键词 Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning
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Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study 认领 引用 被引量：2
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作者 Yan Zhen-yu, Kang Li-shan, Lin Guang-ming ,He MeiState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, ChinaSchool of Computer Science, UC, UNSW Australian Defence Force Academy, Northcott Drive, Canberra, ACT 2600 AustraliaCapital Bridge Securities Co. ,Ltd, Floor 42, Jinmao Tower, Shanghai 200030, China 《Wuhan University Journal of Natural Sciences》 EI CAS 2003年第S1期195-201,共7页
Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search an... Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators. 展开更多
关键词 multi-objective evolutionary algorithm convergence property analysis search operator selection operator Markov chain
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A Novel Hybrid Sine Cosine-Flower Pollination Algorithm for Optimized Feature Selection 认领 引用
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作者 Sumbul Azeem Shazia Javed +3 位作者 Farheen Ibraheem Uzma Bashir Nazar Waheed Khursheed Aurangzeb 《Computers, Materials & Continua》 SCIE EI 2026年第5期1916-1930,共15页
Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t... Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented. 展开更多
关键词 Classification algorithms feature selection process flower pollination algorithm hybrid model metaheuristics multi-objective optimization search algorithm sine cosine algorithm
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A Parallel Search System for Dynamic Multi-Objective Traveling Salesman Problem 认领 引用
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作者 Weiqi Li 《Journal of Mathematics and System Science》 2014年第5期295-314,共20页
This paper introduces a parallel search system for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. This dynamic setting of the problem is very u... This paper introduces a parallel search system for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. This dynamic setting of the problem is very useful for routing in ad-hoc networks. The proposed search system first uses parallel processors to identify the extreme solutions of the search space for each ofk objectives individually at the same time. These solutions are merged into the so-called hit-frequency matrix E. The solutions in E are then searched by parallel processors and evaluated for dominance relationship. The search system is implemented in two different ways master-worker architecture and pipeline architecture. 展开更多
关键词 dynamic multi-objective optimization traveling salesman problem parallel search algorithm solution attractor.
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An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations 认领 引用 被引量：6
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作者 Mohamed Abdel-Basset Reda Mohamed +3 位作者 Mohamed Abouhawwash Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam 《Computers, Materials & Continua》 SCIE EI 2021年第9期2961-2977,共17页
Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for med... Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others. 展开更多
关键词 Magnetic resonance imaging brain image segmentation artificial jellyfish search algorithm ranking method local minima Otsu method
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Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses 认领 引用 被引量：10
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作者 Wen-Jing Hong Peng Yang Ke Tang 《International Journal of Automation and computing》 CSCD 2021年第2期155-169,共15页
Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged a... Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables.More recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision variables.As a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past decade.This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles.From the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables.From the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based approaches.Several future research directions are also discussed. 展开更多
关键词 Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability
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Optimization Design of Halbach Permanent Magnet Motor Based on Multi-objective Sensitivity 认领 引用 被引量：5
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作者 Shuangshuang Zhang Wei Zhang +2 位作者 Rui Wang Xu Zhang Xiaotong Zhang 《CES Transactions on Electrical Machines and Systems》 CSCD 2020年第1期20-26,共7页
The halbach permanent magnet synchronous motor(HPMSM)combines the advantages of permanent magnet motors and halbach arrays,which make it very suitable to act as a robot joint motor,and it can also be used in other fie... The halbach permanent magnet synchronous motor(HPMSM)combines the advantages of permanent magnet motors and halbach arrays,which make it very suitable to act as a robot joint motor,and it can also be used in other fields,such as electric vehicles,wind power generation,etc.At first,the sizing equation is derived and the initial design dimensions are calculated for the HPMSM with the rated power of 275W,based on which the finite element parametric model of the motor is built up and the key structural parameters that affect the total harmonic distortion of air-gap flux density and output torque are determined by analyzing multi-objective sensitivity.Then the structure parameters are optimized by using the cuckoo search algorithm.Last,in view of the problem of local overheating of the motor,an improved stator slot structure is proposed and researched.Under the condition of the same outer dimensions,the electromagnetic performance of the HPMSM before and after the improvement are analyzed and compared by the finite element method.It is found that the improved HPMSM can obtain better performances. 展开更多
关键词 Halbach permanent magnet synchronous motor multi-objective sensitivity cuckoo search algorithm electromagnetic characteristics finite element analysis
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基于MOJS算法和BP神经网络的剩磁式单稳态操动机构结构及励磁电路优化设计 认领 引用
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作者 李培源 刘晓明 +1 位作者 陈海 郑佳圆 《真空科学与技术学报》 CAS CSCD 北大核心 2026年第1期91-100,共10页
为提升真空断路器中剩磁式单稳态操动机构的性能,建立了磁滞特性与驱动电路暂态响应耦合的场-路联合有限元分析模型。构建了以操动机构结构参数与励磁电路参数为输入层,合闸保持力、合闸末平均速度及分闸响应时间为输出层的反向传播神... 为提升真空断路器中剩磁式单稳态操动机构的性能,建立了磁滞特性与驱动电路暂态响应耦合的场-路联合有限元分析模型。构建了以操动机构结构参数与励磁电路参数为输入层,合闸保持力、合闸末平均速度及分闸响应时间为输出层的反向传播神经网络(BPNN)预测模型。误差分析结果表明,该预测模型能够通过严谨的统计验证,准确刻画输入变量与输出响应之间的定量关系。此外,采用多目标水母搜索(MOJS)算法与BPNN预测模型相结合的优化方法,对操动机构进行了结构优化。优化后样机的实验验证结果显示:合闸保持力提升了12.4%,合闸末平均速度降低了17.6%,分闸响应时间缩短了4.5%,验证了所提方法的有效性与优化效果。 展开更多
关键词 单稳态剩磁式操动机构 真空断路器 多目标水母优化算法 反向传播神经网络
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基于A*和水母复合算法的无人机三维路径规划 认领 引用
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作者 张勰 何福锋 《科学技术与工程》 EI 北大核心 2026年第9期3994-4005,共12页
针对无人机在复杂三维地形中的路径规划效率不高及可行性不足等问题,聚焦复杂自然地形场景的路径规划问题,以城市环境与动态障碍物场景作为未来研究方向,提出了一种结合A*算法与水母搜索算法的自适应混合优化方法。该方法引入自适应参... 针对无人机在复杂三维地形中的路径规划效率不高及可行性不足等问题,聚焦复杂自然地形场景的路径规划问题,以城市环境与动态障碍物场景作为未来研究方向,提出了一种结合A*算法与水母搜索算法的自适应混合优化方法。该方法引入自适应参数调整机制,有效提升了路径规划的效率和结果的可行性。利用航天飞机雷达地形测绘使命(shuttle radar topography mission, SRTM)实测地形数据及典型仿真环境开展实验,对比A*算法、粒子群优化算法、蚁群优化算法和鲸鱼优化算法等常用优化算法。实验结果表明,该方法在路径长度、计算时间及内存占用等多项指标上均取得显著优势。具体表现为路径整体更优化,规划速度更快,资源消耗更低,且在复杂地形环境下能够保证较高的路径可行性和安全性。此外,所采用的多约束动态协同策略及高效混合规划器架构进一步增强了算法对于不同地形和复杂约束情境的适应能力。研究结果为无人机在复杂环境下的自主路径规划提供了新的工程实现思路,具有重要的应用价值和推广前景。 展开更多
关键词 无人机 三维路径规划 水母搜索算法 A*算法 SRTM地形数据
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基于改进水母搜索算法的微电网群优化调度模型 认领 引用
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作者 张玉 林意杰 +1 位作者 曾繁敏 王胜 《桂林理工大学学报》 CAS 北大核心 2026年第2期260-267,共8页
针对传统优化算法搜索能力不足、易陷入局部最优、难以获得全局最优解的问题,提出了一种基于改进水母搜索(enhanced jellyfish search,EJS)算法的微电网群优化调度模型。该模型在种群初始化阶段引入Tent混沌映射策略以增强种群多样性,... 针对传统优化算法搜索能力不足、易陷入局部最优、难以获得全局最优解的问题,提出了一种基于改进水母搜索(enhanced jellyfish search,EJS)算法的微电网群优化调度模型。该模型在种群初始化阶段引入Tent混沌映射策略以增强种群多样性,在水母种群被动运动阶段中采用Lévy飞行步长对水母的位置进行更新,以提高全局搜索能力和收敛效率。仿真算例表明:改进水母搜索算法模型的总成本与粒子群优化、水母搜索、海鸥优化、正余弦优化等算法相比,分别降低了4.57%、3.15%、3.67%、3.57%;改进水母搜索算法模型在经济性、环保性及收敛效率等方面表现更佳,具有较好的应用前景。 展开更多
关键词 微电网群 优化 水母搜索算法 Tent混沌映射 Lévy飞行
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求解UCAV航路规划问题的改进水母算法 认领 引用
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作者 肖辉辉 段艳明 《计算机技术与发展》 2026年第8期137-146,共10页
为解决水母搜索优化算法(JS)的洋流运动缺乏多样性、群内运动缺乏引导性及时间控制机制线性变化部分不利于平衡洋流运动和群内运动,造成搜索速度慢、稳定性差及易早熟的问题,构建了一种基于随机榜样学习的改进水母搜索优化算法(RELIJS)... 为解决水母搜索优化算法(JS)的洋流运动缺乏多样性、群内运动缺乏引导性及时间控制机制线性变化部分不利于平衡洋流运动和群内运动,造成搜索速度慢、稳定性差及易早熟的问题,构建了一种基于随机榜样学习的改进水母搜索优化算法(RELIJS)。首先,利用随机榜样学习机制对洋流运动进行改进,增加种群的多样性,提高算法的勘探能力;其次,在群内运动的主动运动部分引入一种基于最优个体引导的变异机制,并通过时间控制策略与原有的随机变异策略相结合,构建一种双变异机制的主动运动策略,提高算法的搜索速度;再次,将余弦函数探索因子融入被动运动,重构一种新的被动运动策略,使算法具有更好的局部开采能力,提高算法的求解精度;最后,改进时间控制机制中的线性变化部分,构造出一种完全非线性变化的时间控制机制,更好地平衡算法的探测与开采能力。选用19个基准测试优化函数,将RELIJS算法与9个先进的优化算法从平均值偏差、方差、Wilcoxon秩和检验方面进行对比分析。相较于对比算法,RELIJS在11个测试函数上获得最优结果,收敛精度平均提高了15.93%~88.50%,优化能力最强。对UCVA航路规划问题求解,RELIFS能够更有效地避开威胁区域。结果显示RELIJS的收敛精度和速度等性能具有明显优势。 展开更多
关键词 水母搜索优化算法 随机榜样学习 余弦函数探索因子 双变异机制 时间控制机制 无人飞行器 航路规划
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Application research of improved sparrow search strategy in multi-objective scheduling of cloud tasks 认领 引用
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作者 Luo Zhiyong Yu Haixin +2 位作者 Teng Wenyao Jiang Hao Sun Guanglu 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2025年第3期46-59,114,共14页
In cloud computing, efficient multi-objective task scheduling, aiming at minimizing makespan, energy consumption,and load variance,remains a critical challenge due to the non-deterministic polynomial( NP)-completeness... In cloud computing, efficient multi-objective task scheduling, aiming at minimizing makespan, energy consumption,and load variance,remains a critical challenge due to the non-deterministic polynomial( NP)-completeness of the problem and the limitations of traditional algorithms like premature convergence. In this paper,a multi-strategy improved sparrow search algorithm( MISSA) was proposed to address these issues. MISSA integrates specular reflection learning for initial population optimization,nonlinear adaptive decay weights to balance global exploration and local exploitation,and an innovative strategy based on T-distribution mutation to enhance population diversity. Experimental results on benchmark functions and real cloud task scheduling scenarios using CloudSim demonstrate that MISSA outperforms comparative algorithms such as sparrow search algorithm( SSA),boosted sparrow search algorithm( BSSA),and genetic algorithm-grey wolf optimizer( GA-GWO),achieving significant reductions in makespan,energy consumption,and load variance. MISSA provides an effective solution for intelligent resource allocation in heterogeneous cloud environments,showcasing robust performance in complex multi-objective optimization tasks. 展开更多
关键词 cloud computing task scheduling multi-objective improved sparrow search algorithm
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计及制造成本的纯电动工程机械动力总成优化设计 认领 引用
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作者 黄淇 黄潇辉 +2 位作者 曹华军 曾浩 鄢万斌 《重庆大学学报》 CAS CSCD 北大核心 2026年第1期1-16,共16页
为提高电动工程机械的动力性和经济性,加快工程机械电气化,降低非道路移动源的碳排放,提出了一种计及制造成本的电动工程机械动力总成优化设计方法。以纯电动轮式装载机为案例,采用模糊TOPSIS法进行考虑广义成本的动力总成优化部件选择... 为提高电动工程机械的动力性和经济性,加快工程机械电气化,降低非道路移动源的碳排放,提出了一种计及制造成本的电动工程机械动力总成优化设计方法。以纯电动轮式装载机为案例,采用模糊TOPSIS法进行考虑广义成本的动力总成优化部件选择,使用改进的多目标水母搜索算法,以特定客户需求工况运行成本、动力性能及狭义制造成本为目标,对选定的部件参数进行优化;建立Matlab/Simulink仿真模型对优化结果进行验证。结果表明,改进后的水母算法具有一定的优越性,在特定客户需求工况下电机工作效率提升了0.214%、0.190%、0.150%,最高车速加速时间分别减少1.798、2.231、1.006 s,制造成本降低了3.129%、5.043%、3.946%,有效提高了电动工程机械的动力性和经济性。 展开更多
关键词 电动工程机械 动力总成 制造成本 水母搜索算法 参数优化
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深海无隔水管钻进泥浆举升泵故障诊断研究 认领 引用
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作者 周凡淋 李昌平 +1 位作者 张恒瑞 秦如雷 《制造业自动化》 2026年第7期77-86,共10页
无隔水管泥浆循环钻井(RMR)系统是深海油气资源高效开发的关键技术。作为该系统泥浆上返的核心动力单元,泥浆举升泵的健康状态直接影响钻井作业的连续性与安全性。然而,在深海高压、双泵切换等复杂工况下,泵内固液两相流动的强非线性特... 无隔水管泥浆循环钻井(RMR)系统是深海油气资源高效开发的关键技术。作为该系统泥浆上返的核心动力单元,泥浆举升泵的健康状态直接影响钻井作业的连续性与安全性。然而,在深海高压、双泵切换等复杂工况下,泵内固液两相流动的强非线性特征与瞬态流量冲击易引发轴向力过大、扭矩异常等典型故障,传统基于经验或单一机理的诊断方法难以满足高精度与实时性的工程需求。针对泥浆举升泵在复杂工况下故障特征难提取及诊断精度低的问题,提出一种基于水母搜索算法优化轻量级梯度提升机(Jellyfish Search-LightGBM,JS-LightGBM)的故障诊断方法。该方法首先基于Fluent-EDEM流固耦合方法构建多级泥浆举升泵的仿真模型,结合正交试验设计,对泥浆密度、粘度、转速、流量、湍流强度及湍流黏度比等关键参数进行多工况仿真,构建包含“正常、轴向力过大、扭矩过大”三类状态的故障数据集,并结合合成少数类过采样技术(SMOTE)缓解类别不平衡问题;在此基础上,利用水母搜索算法对LightGBM的学习率、叶子数量、树深度等关键超参数进行自适应优化,以构建高精度、高效率的故障分类模型。实验结果表明,在十折交叉验证条件下,JS-LightGBM方法在测试集上的故障诊断准确率达到95.06%,较GBDT、JS-XGBoost、JSCatBoost及LightGBM等方法均有明显提升,且单次平均计算时间最短,表现出良好的计算效率与泛化能力。 展开更多
关键词 无隔水管泥浆循环钻井系统 泥浆举升泵 故障诊断 LightGBM 水母搜索算法
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