检索结果-内蒙古大学图书馆

Knowledge-driven ant colony optimization algorithm for vehicle routing problem in instant delivery peak period

APPLIED SOFT COMPUTING 2023年 145卷

作者： Hou, Ying Guo, Xinyu Han, Honggui Wang, Jingjing Beijing Univ Technol Fac Informat Technol Engn Res Ctr Digital Community Beijing Lab Urban Mass TransitMinist Educ Beijing Peoples R China Beijing Key Lab Computat Intelligence & Intelligen Beijing Peoples R China

Instant delivery is an important part of urban logistics distribution, which realizes point-to-point distribution between merchants and customers. During the peak period of orders, instant delivery is a large-scale variable NP-hard combinatorial optimization problem, which increases the difficulty and complexity of scheduling greatly. To solve the large-scale vehicle routing problem of instant delivery in peak periods, a knowledge-driven ant colony optimization (KDACO) algorithm is proposed in this paper. First, the knowledge base is established to guide evolutionary search, including the knowledge of order priority and the feature knowledge of feasible schemes. Second, the pheromone supplementation strategy is designed based on the knowledge of order priority, enhancing the guiding ability of the pheromone table. Third, the adaptive evolutionary operator is designed based on the feature knowledge of feasible schemes, improving the optimization efficiency of the algorithm. Finally, numerical experiments on extensive classical datasets show that the proposed KDACO can obtain superior performance to other state-of-the-art algorithms in the instant delivery peak period. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： ant colony optimization algorithm Vehicle routing problem Instant delivery Peak period

来源：评论

学校读者我要写书评

暂无评论

An improved heuristic mechanism ant colony optimization algorithm for solving path planning

引用

KNOWLEDGE-BASED SYSTEMS 2023年第1期271卷

作者： Liu, Chao Wu, Lei Xiao, Wensheng Li, Guangxin Xu, Dengpan Guo, Jingjing Li, Wentao China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Offshore Geophys & Explorat Equi Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore Macao Polytech Univ Fac Appl Sci Taipa 999078 Macao Peoples R China CNOOC Safety & Technol Serv Co Ltd Tianjin 300450 Peoples R China

With the development of artificial intelligence algorithms, researchers are attracted to intelligent path planning due to its broad applications and potential development. The ant colony optimization (ACO) algorithm is one of the most widely used methods to solve path planning. However, the traditional ACO has some shortcomings such as low search efficiency, easy stagnation, etc. In this study, a novel variant of ACO named improved heuristic mechanism ACO (IHMACO) is proposed. The IHMACO contains four improved mechanisms including adaptive pheromone concentration setting, heuristic mechanism with directional judgment, improved pseudo-random transfer strategy, and dynamic adjustment of the pheromone evaporation rate. In detail, the adaptive pheromone concentration setting and heuristic mechanism with directional judgment are presented to enhance the purposiveness and reduce turn times of planned path. The improved pseudo-random transfer strategy and dynamic adjustment of the pheromone evaporation rate are introduced to enhance search efficiency and global search ability, further avoiding falling into local optimum. Subsequently, a series of experiments are conducted to test effectiveness of the four mechanisms and verify the performance of the presented IHMACO. Compared with 15 existing approaches for solving path planning, including nine variants of ACO and six commonly used deterministic search algorithms. The experimental results indicate that the relative improvement percentages of the proposed IHMACO in terms of the path turn times are 33.33%, 83.33%, 35.29%, 38.46%, and 38.46% respectively, demonstrating the superiority of IHMACO in terms of the availability and high-efficiency.

关键词： Path planning Artificial intelligence algorithm ant colony optimization algorithm Heuristic mechanism Pheromone evaporation

来源：评论

学校读者我要写书评

暂无评论

Inversion of one-dimensional parameters of horizontal multi-layer soil model based on dynamic state electromagnetic field theory and ant colony optimization algorithm

引用

INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2023年第6期36卷

作者： Li, Zhong-Xin Wang, Ke-Chao China Elect Power Res Inst Dept High Voltage Beijing Peoples R China China Elect Power Res Inst Beijing Peoples R China

Building a reliable soil model is a prerequisite for accurately calculating the ground potential distribution of the grounding grid. Research shows that the soil electrical parameters are frequency-dependent, so frequency domain inversion of soil parameters should consider the soil inhomogeneity and the frequency characteristic of parameters. A method for retrieving parameters of horizontal multilayer soil model is proposed. The theoretical formula of apparent complex resistivity with considering dynamic state electromagnetic field theory is derived from Green's function. Discrete dynamic state complex image method is introduced to solve the infinite integral in Green's function. ant colony optimization algorithm is applied to optimize the inversion process of soil parameters. In the first stage, applicability of this method is confirmed by interpreting field data under DC field. DC parameters including DC resistivity, thickness and number of layer are determined. The performance of different optimization algorithms in terms of computational efficiency is compared at this stage. In the second stage, soil resistivity and relative dielectric constant are retrieved by inverting frequency domain experimental data. Different representations based on quasi-static electromagnetic field and dynamic state electromagnetic field theories are taken into account. The frequency characteristic of soil parameters is concluded by discussing the inversion results. In the study of parameter inversion of horizontal multi-layer soil model, our method has shown strong performance in computational efficiency and accuracy of inversion results.

关键词： ant colony optimization algorithm discrete dynamic state complex image method dynamic state electromagnetic field Green's function frequency characteristics of soil parameters horizontal multi-layer soil model

来源：评论

学校读者我要写书评

暂无评论

Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Wu, Lei Huang, Xiaodong Cui, Junguo Liu, Chao Xiao, Wensheng China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Lab Offshore Geophys & Explorat Equipmen Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore City 639798 Singapore

As the key point for auto-navigation of mobile robot, path planning is a research hotspot in the field of robot. Generally, the ant colony optimization algorithm (ACO) is one of the commonly used approaches aiming to solve the problem of path planning of mobile robot. Nevertheless, the traditional ACO has the shortcomings such as slow convergence speed, inefficiency and easily fall into local optimal values. Thus, a novel variant of ACO is proposed in this study. In detail, a new heuristic mechanism with orientation information is firstly introduced to add direction guidance during the iteration process, further to advance the convergence speed of algorithm. Secondly, an improved heuristic function is presented to enhance the purposiveness and reduce the number of turn times of planned path. Then, an improved state transition probability rule is introduced to improve the search efficiency significantly and increase the swarm diversity. Moreover, a new method for unevenly distributing initial pheromone concentration is proposed to avoid blind searching. After integrating the four improvements, the new variation of ACO called modified adaptive ant colony optimization algorithm (MAACO) is formed. Subsequently, parameter optimization of MAACO is carried out. For verifying the effectiveness of the proposed MAACO, a series of experiments are conducted based on five static space environment modes and one dynamic environment mode. Comparing with 13 existing approaches for solving the problem of path planning of mobile robot, including several variants of ACO and two commonly used algorithms (A* algorithm and Dijkstra algorithm), the experimental results demonstrate the merits of MAACO in terms of decreasing the path length, reducing the number of turn times, and promoting the convergence speed. In detail, in all the static simulation experiments, the proposed MAACO generates the shortest path length with a standard deviation of zero, and achieves the least number of turn ti

关键词： Path planning ant colony optimization algorithm Pheromone updating Heuristic function State transition probability

来源：评论

学校读者我要写书评

暂无评论

Computer Network Communication Security Encryption System Based on ant colony optimization algorithm

引用

Procedia Computer Science 2023年 228卷 38-46页

作者： Chunrong Zhou Zhenghong Jiang School of Big Data Chongqing Vocational College of Transportation Jiangjin 402247 Chongqing China

With the increasing number of computer network users, the problem of network communication security has become more and more serious. Encryption technology, as one of the key technologies for securing communication, has important research and application values. However, the security of conventional encryption methods is gradually challenged, and at the same time, traditional encryption algorithms require a large amount of computing resources, which makes it difficult to realize real-time encryption and decryption operations. Therefore, how to improve the confidentiality and efficiency of computer network communication security encryption system has become a current urgent problem to be solved. This paper firstly discussed the analysis of its application in the computer network communication security encryption system based on the ant colony optimization (ACO) algorithm. Through the theoretical analysis of the ACO algorithm in terms of bioheuristic optimization algorithm and the analysis of the characteristics of the algorithm, the optimization scheme of key distribution based on the ACO algorithm was discussed. Then, the differences between the ACO algorithm and the traditional encryption algorithm in terms of encryption duration, key distribution time, encryption strength and random number generation speed were further analyzed and verified by experimental data. The data results showed that the key distribution of the encryption system using the ACO algorithm had higher security and better performance compared with the traditional key distribution algorithm. By counting and comparing the data, it was found that the ACO algorithm showed very obvious advantages in terms of encryption and decryption time, key distribution time, encryption strength and random number generation. This indicates that the ACO algorithm has very important application prospects in computer network communication security encryption systems.

关键词： Computer Network Communication System Analysis Secure Encryption ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Path Planning of AGV Based on Improved ant colony optimization algorithm 33

Research on Path Planning of AGV Based on Improved Ant Colon...

引用

33rd Chinese Control and Decision Conference (CCDC)

作者： Sun, Jiuxiang Yu, Ya'nan Xin, Ling Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266042 Peoples R China

ISBN: (纸本)9781665440899

Path planning is a key problem in the motion control of mobile robot. In order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency, this paper puts forward a matrix yard storage mode, which is transformed into grid map model, and then uses ant colony optimization algorithm to plan the path of AGV. Aiming at the shortcomings of traditional ant colony optimization algorithm (ACO) in global path planning, such as slow convergence speed and weak optimization ability, an improved ant colony path planning algorithm is proposed. Firstly, the grid map is established, and the fruit fly optimization algorithm (FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm, and then the ant colony optimization algorithm is used for global path planning. At the same time, in order to solve the problem of many path turning angles and large cumulative turning angles in the planning, the path smoothing is carried out. The simulation results show that the improved algorithm has fewer turns and smoother path, and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.

关键词： ant colony optimization algorithm Fruit fly optimization algorithm matrix yard storage mode Smoothing

来源：评论

学校读者我要写书评

暂无评论

Improved dynamic adaptive ant colony optimization algorithm to solve pipe routing design

引用

KNOWLEDGE-BASED SYSTEMS 2022年 237卷 107846-107846页

作者： Liu, Chao Wu, Lei Huang, Xiaodong Xiao, Wensheng China Univ Petr Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr Natl Engn Lab Offshore Geophys & Explorat Equipmen Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore

With the purpose of finding a satisfactory pipe path between the starting point and target point, pipe routing design (PRD) has been applied in many industry fields. The research of two-dimensional PRD is the foundation of solving complex RPD problems, and has widely applications in factory layout, facilities installation, and so on. The ant colony optimization (ACO) algorithm is one of the most widely used approaches to solve PRD. However, the traditional ACO has drawbacks such as slow convergence speed, easy to fall into local optimum and low efficiency. In this study, an improved dynamic adaptive ACO (IDAACO) is proposed. The IDAACO includes four novel mechanisms which are the heuristic strategy with direction information, adaptive pseudorandom transfer strategy, improved local pheromone updating mechanism and improved global pheromone updating mechanism. Then, a series of experiments are carried out to verify the effectiveness of the four proposed mechanisms included by IDAACO. Subsequently, the IDAACO is compared with several existing approaches for solving PRD, and the experimental results confirm the advantages of IDAACO in terms of the practicality and high-efficiency. Finally, the IDAACO is used to solve the PRD problem for semi-submersible production platform in oil and gas industry. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Pipe routing design ant colony optimization algorithm Adaptive pseudorandom transfer strategy Pheromone updating Semi-submersible production platform

来源：评论

学校读者我要写书评

暂无评论

Optimal Coordination of PSS and SSSC Controllers in Power System Using ant colony optimization algorithm

引用

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第4期31卷 2250060-2250060页

作者： Kamarposhti, Mehrdad Ahmadi Colak, Ilhami Iwendi, Celestine Band, Shahab S. Ibeke, Ebuka Islamic Azad Univ Dept Elect Engn Jouybar Branch Jouybar Iran Nisantasi Univ Fac Engn & Architectures Dept Elect & Elect Engn Istanbul Turkey Univ Bolton Sch Creat Technol Bolton England Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ Rd Touliu 64002 Yunlin Taiwan Robert Gordon Univ Sch Creat & Cultural Business Aberdeen Scotland

Volatility leads to disruption in synchronism between generators of a continuous system. The frequency of the volatility is usually between a few tenths of Hz to several Hz. This volatility is sometimes divided into two types, local and interregional. Local volatility is the low-frequency volatility of a power plant unit or units of a power plant relative to the grid whereas interregional volatility is the volatility of the units of one area relative to the units of another area. The worst kind of low-frequency volatility occurs when the power system in a region has a short three-phase connection to the earth, creating a complete instability of the grid and operating protective systems. One of the ways to improve the dynamic stability and steady-state of the power system is to use power system stabilizers and FACTS devices in the system. In this paper, the stabilization of the power system stabilizers (PSSs) and SSSC is done using the ant colony algorithm. Studies on a four-machine system with the three-phase error were performed in two scenarios and finally compared with the PSO method. The simulation results show that the proposed method produced more accurate performance.

关键词： PSS SSSC cost reduction FACTS devices low-frequency volatility ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

The hybrid method based on ant colony optimization algorithm in multiple factor analysis of the environmental impact of solar cell technologies

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第6期17卷 6342-6354页

作者： Dong, Bo Luzin, Alexey Gura, Dmitry Liaoning Univ Sch Innovat & Entrepreneurship Chongshan Rd 66 Shenyang 110000 Liaoning Peoples R China Sechenov First Moscow State Med Univ Dept Chem Trubetskaya St 8 Moscow 119991 Russia Kuban State Technol Univ Dept Cadastre & Geoengn Moscow Str2 Bldg B Krasnodar 350072 Russia Kuban State Agr Univ Dept Geodesy 13 Kalinina Str Krasnodar 350044 Russia

The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hybrid optimization method based on particle swarm and ant colony optimization algorithms to solve the problems of PV module toxicity. The Weibull distribution function was used to measure the service life of PV modules under a variety of failure scenarios. The simulation results show that PV modules that were guaranteed to have the service life of 25-30 years mostly last 20-25 years. The toxicity coefficient and the use of a hybrid method suggest that the time period when a solar module exhibits a maximum efficiency with a minimal environmental footprint ranges from 15 to 20 years. It was established that this interval corresponds to the level at which the amount of waste does not exceed the amount of energy generated with a minimum number of failures. The proposal will be effective in predicting the performance of solar systems. This approach can be improved in terms of cost and benefit and employed in the future research on renewable energy and ecosystems.

关键词： ant colony optimization algorithm new technologies Particle Swarm optimization algorithm PV panel waste solar energy

来源：评论

学校读者我要写书评

暂无评论

Applying genetic algorithm and ant colony optimization algorithm into marine investigation path planning model

引用

SOFT COMPUTING 2020年第11期24卷 8199-8210页

作者： Liang, Ye Wang, Lindong Shaanxi Normal Univ Sch Geog & Tourism Xian 710000 Shaanxi Peoples R China Beijing Normal Univ Fac Geog SciChina State Key Lab Earth Surface Pr Engn Ctr Desertificat & Blownsand ControlMinist Key Lab Environm Change & Nat DisasterMinist Edu Beijing 100000 Peoples R China

Marine resources are vital to the development of a country. Marine investigation can obtain more marine resources and acquire more marine environmental information. A common method used in the marine investigation consumes a large amount of both time and money. Thus, the scientific path planning is important for improving the efficiency and reducing the costs of the marine investigation. Currently, the most commonly used algorithms for path planning are the genetic algorithm (GA) and the ant colony optimization algorithm (ACOA). Through continuous iterations, the initial solutions obtained by GA and ACOA gradually approach the optimal solutions. However, the final solutions of both algorithms are often suboptimal solutions or local optimal solutions. In particular, in terms of the marine investigation path planning that involves enormous stations, both GA and ACOA are prone to premature and local optimal solutions, leading to the stagnation of the searching. Therefore, in order to solve these problems and save the costs of marine investigation, the ACOA and GA are combined to propose a hybrid algorithm for the further improvement in the quality of the solutions. Through the experiments and software implementation, the proposed hybrid algorithm is proved of high effectiveness and robustness, which could obtain the optimal path for single or multiple research vessels, thereby saving the time and costs of marine investigation path planning.

关键词： Genetic algorithm Marine path planning ant colony optimization algorithm Hybrid algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：