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MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems 认领 引用 被引量：2
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作者 Rashmi Sharma Ashok Pal +4 位作者 Nitin Mittal Lalit Kumar Sreypov Van Yunyoung Nam Mohamed Abouhawwash 《Computers, Materials & Continua》 SCIE EI 2024年第3期3489-3510,共22页
This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic ... This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms. 展开更多
关键词 Multi-objective optimization genetic algorithm ant lion optimizer metaheuristic
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Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search 认领 引用 被引量：1
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作者 Soukaina Mjahed Khadija Bouzaachane +2 位作者 Ahmad Taher Azar Salah El Hadaj Said Raghay 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期459-494,共36页
This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised ... This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO. 展开更多
关键词 Ant lion optimization binary clustering clustering algorithms Higgs boson feature extraction dimensionality reduction elbow criterion genetic algorithm particle swarm optimization
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Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems 认领 引用
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作者 R. Satheeshkumar R. Shivakumar 《Circuits and Systems》 2016年第9期2357-2383,共27页
This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune ... This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices. 展开更多
关键词 Load Frequency Control (LFC) Multi-Area Power System Proportional-Integral (PI) Controller Ant Lion Optimization (ALO) Bat Algorithm (BAT) Genetic Algorithm (GA) Particle Swarm Optimization (PSO)
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基于粒子群和蚁狮混合优化算法的Jiles-Atherton磁滞模型参数辨识 认领 引用 被引量：1
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作者 叶建盈 刘磊 +3 位作者 林波 陈颖婷 黄光华 舒一展 《电器与能效管理技术》 2026年第2期12-18,共7页
Jiles-Atherton(J-A)磁滞模型具有参数较少、物理意义清晰等优点,在电磁材料磁特性模拟研究中得到了广泛应用。针对J-A磁滞模型参数辨识中精度低、耗时久的问题,提出了一种结合粒子群优化(PSO)与蚁狮优化(ALO)的混合优化算法。在算法初... Jiles-Atherton(J-A)磁滞模型具有参数较少、物理意义清晰等优点,在电磁材料磁特性模拟研究中得到了广泛应用。针对J-A磁滞模型参数辨识中精度低、耗时久的问题,提出了一种结合粒子群优化(PSO)与蚁狮优化(ALO)的混合优化算法。在算法初期,利用PSO算法的全局搜索能力,可迅速定位J-A磁滞模型参数全局最优值的大致区间;随后,在算法进入深入搜索阶段时,引入ALO算法,通过蚂蚁的随机游走、轮盘赌选择机制及精英保留策略,能够在限定的搜索空间内实现高精度的收敛,从而迅速锁定模型参数的全局最优解。仿真与实验验证表明,该混合算法在模型参数辨识上展现出快速收敛特性和高精度性能,且模拟磁滞曲线与实测数据高度一致,验证了其实用性和有效性。 展开更多
关键词 粒子群优化算法 Jiles-Atherton磁滞模型 参数辨识 蚁狮优化算法
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Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance 认领 引用 被引量：3
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作者 V.G.Saranya S.Karthik 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期127-150,共24页
Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node... Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE). 展开更多
关键词 Enhanced ant colony optimization mayfly optimization algorithm wireless sensor networks cluster head base station(BS)
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Optimizing force aggregation:SATC-ALO and SOM hybrid clustering model 认领 引用
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作者 ZHANG Zhenxing YANG Rennong +1 位作者 ZHANG Ying SONG Qi 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期604-615,共12页
To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map... To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map(SOM)network.The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences,adaptive tent chaos search,tournament selection,and logistic chaos sequences.Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network.The model is resolved using SATC-ALO and SOM independently,with experiments demonstrating that SOM achieves fast and accurate grouping,while SATC-ALO offers higher precision but requires longer computational runtime,making it more suitable for hybrid approaches.Both methods are validated as practical solutions for force aggregation tasks. 展开更多
关键词 force aggregation fuzzy inference hybrid calculating method self-adaptive tent chaos search ant lion optimizer(SATC-ALO)algorithm self organizing maps network(SOM)
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基于改进VMD及ConvNeXt的小电流接地系统单相接地故障选线方法 认领 引用 被引量：9
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作者 张浩 张大海 +2 位作者 刘乃毓 吴奎忠 侍哲 《高电压技术》 EI CAS CSCD 北大核心 2025年第2期730-741,I0021,共12页
对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模... 对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模态分解算法,通过蚁狮算法自动寻优选取合适的分解次数和惩罚因子,计算分解得到的各分量的分布熵,将其中的噪声分量筛选去除,将其余有效分量进行线性重构得到降噪后的零序电流信号;其次,将经过降噪处理后的一维零序电流信号经格拉姆角场转换为二维图像,制备故障选线数据集;然后,引入预训练的ConvNeXt模型,根据该研究数据模型特征,在其已有权重基础上对模型参数进行对应微调,从而提高模型精度并形成最终的选线模型;最后引入绝对平均误差、均方根误差作为评价指标验证所提降噪算法有效性。分别在加入噪声与否的前提下,将所提模型与3种选线模型相比较。实验结果表明该模型的准确率最高、抗噪性方面更好,其中该研究算法准确率达到了99.82%并且在不同噪声条件下都能维持91%以上的准确率,高于其他选线模型,克服了传统故障选线方法准确率低、抗噪性差的问题。 展开更多
关键词 故障选线 蚁狮优化算法 变分模态分解 分布熵 格拉姆角场 Conv Ne Xt
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多策略协同的改进小龙虾优化算法及其工程应用 认领 引用
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作者 张晓丽 杨璨 +2 位作者 宋晶 朱贵富 聂佳磊 《小型微型计算机系统》 CSCD 北大核心 2026年第5期1156-1165,共10页
本文提出了一种多策略协同的改进小龙虾优化算法(ICOA),以解决原始COA算法多样性不足、收敛速度慢、易陷入局部最优等问题.首先,采用Logistic-Tent混沌映射替代随机初始化,提升初始解质量;其次,在迭代初期引入镜像反射学习机制,利用对... 本文提出了一种多策略协同的改进小龙虾优化算法(ICOA),以解决原始COA算法多样性不足、收敛速度慢、易陷入局部最优等问题.首先,采用Logistic-Tent混沌映射替代随机初始化,提升初始解质量;其次,在迭代初期引入镜像反射学习机制,利用对称性扩展解空间以加速收敛;此外,在避暑阶段融合透镜成像的自适应反向学习以增强算法跳出局部最优的能力;最后,结合遗传算法的垂直交叉操作,提高种群多样性以强化全局搜索能力.在实验部分,基于CEC2014测试函数,分别开展对比实验和消融实验验证算法性能的提升.研究证实,所提多策略协同机制有效克服了原始COA的缺陷,在收敛速度、精度和鲁棒性方面均有显著提升. 展开更多
关键词 小龙虾优化算法 混沌映射 镜像反射学习 自适应反向学习 随机节点交配的垂直交叉操作
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Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage 认领 引用 被引量：4
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作者 Junhui Li Yuqing Zhang +4 位作者 Can Chen Xiaoxiao Wang Yinchi Shao Xingxu Zhu Cuiping Li 《Energy Engineering》 EI 2024年第9期2389-2408,共20页
Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the ... Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning. 展开更多
关键词 Zoning control two-stage planning site selection and capacity determination optimized scheduling improved ant lion algorithm
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基于蚁狮优化算法的新能源并网电压振荡抑制 认领 引用
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作者 李胜男 徐志 +1 位作者 张明晖 张涛 《信息技术》 2026年第4期53-57,62,共5页
由于新能源并入电网后,电压振荡信号与正常信号易出现混叠现象,使得输出电压受到小扰动分量的影响,导致电压振荡抑制效果不好,稳定裕度较低。为此,提出基于蚁狮优化算法的新能源并网电压振荡自适应抑制方法。在平衡点处分析新能源的输... 由于新能源并入电网后,电压振荡信号与正常信号易出现混叠现象,使得输出电压受到小扰动分量的影响,导致电压振荡抑制效果不好,稳定裕度较低。为此,提出基于蚁狮优化算法的新能源并网电压振荡自适应抑制方法。在平衡点处分析新能源的输出小信号,获取新能源侧电压的小扰动量,建立包含新能源电网的运行模型,利用电网实时电压信号设计电压振荡控制器,引入蚁狮优化算法优化控制器,获得最佳控制参数,实现电网电压振荡抑制。实验结果表明,所提方法对新能源接入下的电网进行电压振荡抑制效果更好,稳定裕度更高。 展开更多
关键词 蚁狮优化算法 新能源并网 电压振荡 控制器
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基于对数惯性权重的改进蚁狮优化算法 认领 引用
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作者 罗雪莹 《计算机时代》 2026年第1期18-22,共5页
针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对... 针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对惯性权重的非线性调整,从而更好地平衡算法的全局勘探和局部开采能力。同时,在算法的位置更新中,通过引入对数惯性权重策略来优化蚁狮个体的位置更新过程,降低算法陷入局部收敛的可能性,进而加快收敛速度。本文使用3个经典的测试函数来测试LALO的寻优性能。与已有的群智能算法相比,LALO加快了算法的收敛速度,提高了收敛精度和稳定性。 展开更多
关键词 对数函数 惯性权重 蚁狮优化算法 收敛速度 稳定性 收敛精度
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改进蚁狮优化算法优化BP神经网模型的高程拟合方法 认领 引用
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作者 宋海艳 《地理空间信息》 2026年第4期28-31,共4页
针对复杂地形条件下GNSS高程异常拟合模型精度不高的问题,提出一种改进蚁狮优化算法以优化BP神经网络的建模方法。该方法通过引入非线性动态权重调整机制与和动态比例因子调节策略,改进蚁狮优化算法的搜索能力,从而实现对BP神经网络中... 针对复杂地形条件下GNSS高程异常拟合模型精度不高的问题,提出一种改进蚁狮优化算法以优化BP神经网络的建模方法。该方法通过引入非线性动态权重调整机制与和动态比例因子调节策略,改进蚁狮优化算法的搜索能力,从而实现对BP神经网络中神经元间的权值与阈值的全局搜索与局部优化,有效克服了BP神经网络易陷入梯度下降缓慢和局部极值的缺陷。基于实际工程数据,分别对比了BP神经网络、最小二乘支持向量机、标准ALO-BP方法以及所提出的改进方法在高程异常拟合精度上的表现。实验结果表明,方法所获得的高程异常拟合值与实测高程高度吻合度更高,在拟合精度和稳定性方面均优于其他方法,表现出良好的工程应用前景。 展开更多
关键词 非线性动态权重机制 动态比例系数 蚁狮优化算法 BP神经网络
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Ant Lion Algorithm for Optimized Controller Gains for Power Quality Enrichment of Off-grid Wind Power Harnessing Units 认领 引用 被引量：4
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作者 Kodakkal Amritha Veramalla Rajagopal +1 位作者 Kuthuri Narasimha Raju Sabha Raj Arya 《Chinese Journal of Electrical Engineering》 EI CSCD 2020年第3期85-97,共13页
The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the v... The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current. 展开更多
关键词 Wind power harnessing unit induction generator admittance based control algorithm ant lion optimization algorithm voltage and frequency control battery energy storage system
基于改进ALO算法在含DG配电网规划中的应用研究 认领 引用
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作者 张磊 凌恒宇 《电气应用》 2026年第3期51-58,共8页
针对现有含分布式电源配电网规划方法存在的综合成本较高问题,建立以最小全寿命周期成本为优化目标的含分布式电源配电网规划模型,并通过改进的蚁狮优化算法求解该模型。蚁狮优化算法通过三个方面的优化(优化轮盘赌策略、优化步长和参... 针对现有含分布式电源配电网规划方法存在的综合成本较高问题,建立以最小全寿命周期成本为优化目标的含分布式电源配电网规划模型,并通过改进的蚁狮优化算法求解该模型。蚁狮优化算法通过三个方面的优化(优化轮盘赌策略、优化步长和参数动态调整)提高了种群多样性和优化效率。通过试验验证了该规划方法的优越性和有效性。结果表明,与常规规划方法相比,所提方法具有最低的全寿命周期成本。与未连接分布式电源时相比,最小节点电压从约0.909(pu)增加到约0.958(pu),有效降低了网络损耗,使系统更加稳定。 展开更多
关键词 分布式电源 配电网规划 全寿命周期成本 蚁狮优化算法 参数动态调整
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基于改进蚁狮算法优化BP的轴承故障诊断 认领 引用 被引量：5
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作者 王妍 于浩文 +2 位作者 凌丹 梁恩豪 王新发 《计算机集成制造系统》 EI CSCD 北大核心 2025年第4期1259-1271,共13页
为了准确高效地对滚动轴承的健康状态进行诊断,提出一种基于改进蚁狮优化(IALO)算法优化BP神经网络的滚动轴承故障诊断模型。在IALO算法中,采用变异算子,增强了种群的多样性;采用动态比例系数和非线性动态权重,平衡了迭代过程中不同时... 为了准确高效地对滚动轴承的健康状态进行诊断,提出一种基于改进蚁狮优化(IALO)算法优化BP神经网络的滚动轴承故障诊断模型。在IALO算法中,采用变异算子,增强了种群的多样性;采用动态比例系数和非线性动态权重,平衡了迭代过程中不同时期游走的权重,降低了算法陷入局部极值的可能性。基准函数测试结果表明,与其他算法相比,IALO算法具有更好的优化性能。另外,为了改善BP神经网络的分类性能,利用IALO算法优化BP神经网络的权值和阈值,构建滚动轴承故障诊断模型。帕德伯恩轴承数据集的实验结果表明,采用IALO算法优化后的BP模型具有较好的故障诊断性能。 展开更多
关键词 轴承故障诊断 蚁狮优化算法 动态比例系数 非线性动态权重 BP神经网络
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融合多策略的改进鹈鹕优化算法 认领 引用 被引量：6
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作者 李智杰 赵铁柱 +3 位作者 李昌华 介军 石昊琦 杨辉 《控制工程》 CSCD 北大核心 2025年第7期1184-1197,1206,共14页
针对鹈鹕优化算法在寻优过程中存在的种群多样性降低、收敛速度下降、易陷入局部最优等问题,融合多种策略对其进行改进,提出了改进鹈鹕优化算法(improved pelican optimization algorithm,IPOA)。首先,利用帐篷(tent)混沌映射和折射反... 针对鹈鹕优化算法在寻优过程中存在的种群多样性降低、收敛速度下降、易陷入局部最优等问题,融合多种策略对其进行改进,提出了改进鹈鹕优化算法(improved pelican optimization algorithm,IPOA)。首先,利用帐篷(tent)混沌映射和折射反向学习策略初始化鹈鹕种群,在增加种群多样性的同时为算法寻优能力的提升打下基础;然后,在鹈鹕逼近猎物阶段引入非线性惯性权重因子以提高算法的收敛速度;最后,引入樽海鞘群算法的领导者策略以协调算法的全局搜索能力和局部寻优能力。实验测试了单一改进策略的改进效果,并将IPOA与其他9种优化算法进行了对比。实验结果证明了各改进策略的有效性和IPOA的优越性和鲁棒性。 展开更多
关键词 鹈鹕优化算法 帐篷混沌映射 折射反向学习 非线性惯性权重因子 樽海鞘群算法
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基于聚类优化算法的多无人艇协同任务规划 认领 引用
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作者 余文瞾 乔靖超 +2 位作者 杜哲 邢著楷 万芯源 《系统工程与电子技术》 EI CSCD 北大核心 2025年第11期3708-3720,共13页
针对多无人艇(unmanned surface vessel, USV)集群协同任务分配及安全路径规划问题,面向USV设计运动转艏约束条件与评价指标,提出基于密度的噪声应用空间聚类(density-based spatial clustering of applications with noise, DBSCAN)的... 针对多无人艇(unmanned surface vessel, USV)集群协同任务分配及安全路径规划问题,面向USV设计运动转艏约束条件与评价指标,提出基于密度的噪声应用空间聚类(density-based spatial clustering of applications with noise, DBSCAN)的改进分工蚁群任务分配算法。路径规划方面,先应用改进人工势场(artificial potential field,APF)法,后考虑USV操纵约束,通过三次Hermite曲线与USV操纵模型约束其航速与切向量。仿真实验表明,在搜索角约束下,3艘USV能够比较有效地减少转向,预设路径无交叉碰撞;三次Hermite曲线优化后的路径能够避开障碍物,且使USV艏向角变化趋于平缓,满足其运动约束。因此,所提方法能够给出更符合USV操纵特性的分配与规划方案。 展开更多
关键词 多无人艇任务分配 路径规划 基于密度的噪声应用空间聚类 蚁群优化算法 三次Hermite曲线
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无人艇编队避碰路径规划与重规划 认领 引用 被引量：2
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作者 刘伊婕 姜斌 +2 位作者 马亚杰 李文博 刘成瑞 《系统工程与电子技术》 EI CSCD 北大核心 2025年第6期1964-1974,共11页
针对无人艇(unmanned surface vessel,USV)在障碍地图中的避碰路径规划问题,提出一种基于改进蚁群算法的静态全局路径规划避碰方法,并面向未知障碍给出一种局部路径重规划方案。运用栅格法对障碍环境进行建模;通过设计复合启发函数,提... 针对无人艇(unmanned surface vessel,USV)在障碍地图中的避碰路径规划问题,提出一种基于改进蚁群算法的静态全局路径规划避碰方法,并面向未知障碍给出一种局部路径重规划方案。运用栅格法对障碍环境进行建模;通过设计复合启发函数,提出一种信息素动态给予机制,引入混沌优化算子,解决传统蚁群算法易落入局部最优解和收敛性差的问题;基于鱼群效应提出一种局部路径重规划方案,解决USV编队在遭遇未知障碍时的路径重规划问题。对由5艘USV组成的分布式编队系统进行仿真实验,验证了所提方法对编队避碰问题的有效性。 展开更多
关键词 无人艇编队 路径规划 路径重规划 改进蚁群算法 混沌优化
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蚁狮优化算法综述 认领 引用
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作者 李承泽 李庚松 +1 位作者 刘艺 刘坤 《计算机仿真》 2025年第6期16-24,共9页
优化问题广泛存在于现实需求中,通常难以在多项式时间内获得精确解,采用进化算法搜索近似解是解决此类问题的主要方法。蚁狮优化是近年提出的一种新型进化算法。方法模拟蚁狮诱捕蚂蚁的觅食行为,主要步骤包括蚂蚁随机游走、蚁狮设置陷... 优化问题广泛存在于现实需求中,通常难以在多项式时间内获得精确解,采用进化算法搜索近似解是解决此类问题的主要方法。蚁狮优化是近年提出的一种新型进化算法。方法模拟蚁狮诱捕蚂蚁的觅食行为,主要步骤包括蚂蚁随机游走、蚁狮设置陷阱、蚂蚁滑入陷阱、蚁狮捕食蚂蚁与蚁狮重建陷阱,具有易于实现、扩展性强、灵活度高等优点,受到了广泛的研究与应用。介绍蚁狮优化算法的运行流程,将其改进方式分为策略优化方法和问题特定方法并进行总结,概述蚁狮优化算法的实际应用场景,展望未来的研究方向。 展开更多
关键词 蚁狮优化算法 进化算法 复杂优化 全局搜索 工程应用
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基于变分模态分解和改进频率增强分解变压器的有色金属价格预测 认领 引用
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作者 王瑞 宋琦 +1 位作者 刘文慧 摆玉龙 《西北师范大学学报（自然科学版）》 CAS 2025年第1期51-60,I0004,共10页
准确预测有色金属价格对于决策者、投资者和研究人员具有重要意义.为了提高预测精度,文中提出了一种新型混合预测模型,称为(EVMD-ICEEMDAN-RFEDformer,EIRF).首先,使用变分模态分解(variational mode decomposition,VMD)将原始价格分解... 准确预测有色金属价格对于决策者、投资者和研究人员具有重要意义.为了提高预测精度,文中提出了一种新型混合预测模型,称为(EVMD-ICEEMDAN-RFEDformer,EIRF).首先,使用变分模态分解(variational mode decomposition,VMD)将原始价格分解为多个分量,同时使用改进的蚁狮搜索算法(modified ant lion optimization,MALO)对VMD的两个参数进行优化.其次,采用改进的带有自适应噪声的完全集成经验模式分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)进一步分解VMD产生的残差序列,从中提取有价值的信息.然后将所有分解的子序列输入到改进的频率增强分解变压器(reinforced frequency enhanced decomposition transformer,RFEDformer)中.最后,合并RFEDformer的预测并得出最终结果.为了验证模型的可靠性,文中利用了伦敦金属交易所的锡、铜和镍价格数据制定了3个不同的实验,并与12个对比模型进行了比较.结果表明混合模型在3个数据集上都取得了良好的性能. 展开更多
关键词 有色金属价格预测 蚁狮优化算法 二次分解 RFEDformer模型 Sophia优化器 IKMSE损失函数
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