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Multi-Objective Structural Optimization of Composite Wind Turbine Blade Using a Novel Hybrid Approach of Artificial Bee Colony Algorithm Based on the Stochastic Method 认领 引用 被引量：1
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作者 Ramazan Özkan Mustafa Serdar Genç Ìlker Kayali 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3349-3380,共32页
The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models.This paper presented a novel approach to the structural design of smallsca... The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models.This paper presented a novel approach to the structural design of smallscale turbine blades using the Artificial Bee Colony(ABC)Algorithm based on the stochastic method to optimize both mass and cost(objective functions).The study used computational fluid dynamics(CFD)and structural analysis to consider the fluid-structure interaction.The optimization algorithm defined several variables:structural constraints,the type of composite material,and the number of composite layers to form a mathematical model.The numerical modeling was performed using the Ansys Fluent software and its Fluid-Structure Interaction(FSI)module.The ANSYS Composite PrePost(ACP)advanced composite modeling method was utilized in the structural design of composite materials.This study showed that the structurally optimized small-scale turbine blades provided a sustainable solution with improved efficiency compared to traditional designs.Furthermore,using CFD,structural analysis,and material characterization techniques first considered in this study highlights the importance of considering structural behavior when optimizing turbine blade designs. 展开更多
关键词 Turbine blade modeling structural optimization composite artificial bee colony algorithm
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Improved Multi-objective Ant Colony Optimization Algorithm and Its Application in Complex Reasoning 认领 引用 被引量：4
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作者 WANG Xinqing ZHAO Yang +2 位作者 WANG Dong ZHU Huijie ZHANG Qing 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2013年第5期1031-1040,共10页
The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become... The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system. 展开更多
关键词 fault reasoning ant colony algorithm Pareto set multi-objective optimization complex system
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Design of high phase-sensitivity BlueP/TMDC heterostructure-based SPR biosensor using improved artificial bee colony algorithm 认领 引用
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作者 Chong Yue Mantong Chen +1 位作者 Yaopu Lang Qinggang Liu 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2025年第2期113-122,共10页
This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu... This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphoreneransition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×106 °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures. 展开更多
关键词 SPR Phase modulation Sensitivity Improved artificial bee colony algorithm BlueP/TMDC Heterostructure
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A Load-Balancing Routing Algorithm Based on Ant Colony Optimization and Reinforcement Learning for LEO Satellite Networks 认领 引用
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作者 Deng Xia Lin Wucheng +3 位作者 Hu Yingxin Hao Miaomiao Chang Le Huang Jiawei 《China Communications》 SCIE EI CSCD 2025年第12期281-294,共14页
Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human p... Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead. 展开更多
关键词 ant colony algorithm low earth orbit(LEO)satellite network reinforcement learning
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Bayesian-based ant colony optimization algorithm for edge detection 认领 引用
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作者 YU Yongbin ZHONG Yuanjingyang +6 位作者 FENG Xiao WANG Xiangxiang FAVOUR Ekong ZHOU Chen CHENG Man WANG Hao WANG Jingya 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期892-902,共11页
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t... Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task. 展开更多
关键词 ant colony optimization(ACO) Bayesian algorithm edge detection transfer function.
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Multi-group ant colony algorithm based on simulated annealing method 认领 引用 被引量：3
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作者 朱经纬 芮挺 +1 位作者 廖明 张金林 《Journal of Shanghai University(English Edition)》 2010年第6期464-468,共5页
To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulat... To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed. 展开更多
关键词 ant colony algorithm simulated annealing method multi-group candidate set update set
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An effective multi-level algorithm based on ant colony optimization for graph bipartitioning 认领 引用 被引量：3
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作者 冷明 郁松年 +1 位作者 丁旺 郭强 《Journal of Shanghai University(English Edition)》 2008年第5期426-432,共7页
Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph... Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed with its guidance function. During the refinement phase, the vertex gain is exploited as ant＇s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute （ANSI） C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks. 展开更多
关键词 rain-cut graph bipartitioning multi-level algorithm ant colony optimization （ACO）
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Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence 认领 引用 被引量：1
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作者 Ting Cai Chun Ye +5 位作者 Zhiwei Ye Ziyuan Chen Mengqing Mei Haichao Zhang Wanfang Bai Peng Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第10期1157-1175,共19页
The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challengi... The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper. 展开更多
关键词 Multi-label feature selection ant colony optimization algorithm dynamic redundancy high-dimensional data label correlation
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A multi-path routing algorithm of LEO satellite networks based on an improved ant colony system 认领 引用 被引量：1
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作者 王厚天 Zhang Qi +3 位作者 Xin Xiangjun Tao Ying Chen Dong Liu Naijin 《High Technology Letters》 EI CAS 2014年第3期253-260,共8页
Geography rectangle is used to reduce signaling overhead of the LEO satellite networks.Moreover,a multi-path routing algorithm based on an improved ant colony system(MPRA-AC) is proposed.Matrix indicating the importan... Geography rectangle is used to reduce signaling overhead of the LEO satellite networks.Moreover,a multi-path routing algorithm based on an improved ant colony system(MPRA-AC) is proposed.Matrix indicating the importance of the link between satellites is introduced into MPRA-AC in order to find the optimal path more quickly.Simulation results show that MPRA-AC reduces the number of iterations to achieve a satisfactory solution.At the same time,the packet delivery ratio of LEO satellite networks when running MPRA-AC and DSR-LSN(dynamic source routing algorithm for LEO satellite networks) is compared.The packet delivery ratio is about 7.9%lower when running DSR-LSN.Moreover,because of the mechanism of active load balancing of MPRA-AC,simulation results show that MPRA-AC outperforms DSR-LSN in link utilization when data packets are transmitted in the networks. 展开更多
关键词 ant colony algorithm low earth orbit （LEO） packet delivery ratio routing satellite networks
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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Improved multi-objective artificial bee colony algorithm for optimal power flow problem 认领 引用 被引量：1
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作者 马连博 胡琨元 +1 位作者 朱云龙 陈瀚宁 《Journal of Central South University》 SCIE EI CAS 2014年第11期4220-4227,共8页
The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting obj... The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness. 展开更多
关键词 cooperative artificial colony algorithm optimal power flow multi-objective optimization
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Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm 认领 引用 被引量：1
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作者 Qiangfei Cao Huilai Wang +1 位作者 Zijia Hui Lingyun Chen 《Energy Engineering》 EI 2024年第2期499-521,共23页
Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in t... Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability. 展开更多
关键词 Distributed generation distribution network life cycle cost multi-objective artificial bee colony algorithm voltage stability
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The Shortest Motion Path of Multi-robot Fish Formation Based on Ant Colony Algorithm and Fuzzy Control Mechanism 认领 引用
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作者 Susu Shan Zhijian Ji Junwei Gao 《控制工程期刊（中英文版）》 2013年第5期301-309,共9页
For the formation control of multi-robot fish system,the paper focuses on how to reach the specified location along the shortest path during the moving process.The problem is handled by a proposed strategy which combi... For the formation control of multi-robot fish system,the paper focuses on how to reach the specified location along the shortest path during the moving process.The problem is handled by a proposed strategy which combines the leader-follower framework with ant colony algorithm.The strategy will first search for the shortest path by using ant colony algorithm.Then the formation controller will be designed according to the principle of leader-follower algorithm and fuzzy control mechanism to make robotic fish keep formation and arrive at the designated location safely along the shortest path.The feasibility of the proposed method is verified by simulation,which indicates that the multiple robot fish system can move with formation and fluency on the one hand,and as well find the shortest motion path from a given starting point to the destination point on the other hand. 展开更多
关键词 Leader-follower Algorithm Formation Control Ant Colony Algorithm The Shortest Path Fuzzy Mechanism
A Multi-Objective Discrete Artificial Bee Colony Scheduling Algorithm with Referential Search Strategy 认领 引用
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作者 Xin Fan Hongyan Sang +2 位作者 Xujin Zhang Seyed Farshid Chini Song Chen 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第1期259-273,共15页
With the popularization of industrial intelligence,Automated Guided Vehicles(AGVs)have gradually become an efficient means of transportation in manufacturing workshops.Previous studies on this issue mainly considered ... With the popularization of industrial intelligence,Automated Guided Vehicles(AGVs)have gradually become an efficient means of transportation in manufacturing workshops.Previous studies on this issue mainly considered the transportation cost of AGVs,while ignoring the optimization of customer satisfaction.This paper studies the AGV scheduling problem with time and capacity constraints for material handling in an intelligent manufacturing workshop.To better reflect real production conditions and simultaneously minimize AGV carbon emissions while maximizing customer satisfaction,a Mixed-Integer Linear Programming(MILP)model is developed.A Multi-objective Discrete Artificial Bee Colony algorithm(MDABC)is proposed,which employs an adaptive selection strategy to ensure that different neighborhoods of solutions are fully explored.The reference search strategy is introduced to carry out in-depth search according to the effective information carried by high quality solutions.In addition,in order to avoid the algorithm falling into local optimality,a high-quality generation strategy is proposed.Comprehensive comparisons with state-of-the-art algorithms and statistical analyses demonstrate that the proposed MDABC achieves superior performance. 展开更多
关键词 Automated Guided Vehicle(AGV) scheduling Multi-objective Discrete Artificial Bee Colony algorithm(MDABC) green scheduling
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Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speed, Part Ⅰ–Aerodynamic optimization using genetic, bee colony and gradient descent algorithms 认领 引用 被引量：16
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作者 Andreea Koreanschi Oliviu Sugar Gabor +5 位作者 Joran Acotto Guillaume Brianchon Gregoire Portier Ruxandra Mihaela Botez Mahmoud Mamou Youssef Mebarki 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2017年第1期149-163,共15页
In this paper, an ‘in-house＇ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm＇s ... In this paper, an ‘in-house＇ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm＇s performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods,namely the artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated. 展开更多
关键词 Artificial bee colony Airfoil optimization Genetic algorithm Morphing wing Optimization
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Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm 认领 引用 被引量：21
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作者 Li Yuqing Wang Rixin Xu Minqiang 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2014年第3期678-687,共10页
This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy featu... This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances. 展开更多
关键词 Ant colony algorithm Planning and scheduling Rescheduling Spacecraft observing Uncertainty
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The Colony Predation Algorithm 认领 引用 被引量：40
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作者 Jiaze Tu Huiling Chen +1 位作者 Mingjing Wang Amir H.Gandomi 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第3期674-710,共37页
This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by anima... This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication. 展开更多
关键词 Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems
Algorithm for Low Altitude Penetration Aircraft Path Planning with Improved Ant Colony Algorithm 认领 引用 被引量：30
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作者 叶文 马登武 范洪达 《Chinese Journal of Aeronautics》 EI CAS 2005年第4期304-309,共6页
The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method... The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained. 展开更多
关键词 ant colony algorithm path planning keeping optimization adaptively adiusting low altitude penetration
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Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System 认领 引用 被引量：12
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作者 Lei Wang Xu-Hui Xia +2 位作者 Jian-Hua Cao Xiang Liu Jun-Wei Liu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2018年第6期106-117,共12页
The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission ... The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss. 展开更多
关键词 Remanufacturing service Information transmission Path optimization Ant colony algorithm Genetic algorithm
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Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm 认领 引用 被引量：27
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作者 Wang Yanxia Qian Longjun +1 位作者 Guo Zhi Ma Lifeng 《Journal of Systems Engineering and Electronics》 SCIE EI 2008年第5期939-944,共6页
A weapon target assignment(WTA)model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon a... A weapon target assignment(WTA)model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted.The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result.Ant colony algorithm has been successfully used in many fields,especially in combination optimization.The ant colony algorithm for this WTA problem is described by analyzing path selection,pheromone update,and tabu table update.The effectiveness of the model and the algorithm is demonstrated with an example. 展开更多
关键词 weapon target assignment ant colony algorithm optimization.
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