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Determination of optimum cross-section of earth dams using ant colony optimization algorithm

SCIENTIA IRANICA 2019年第3期26卷 1104-1121页

作者： Rezaeeian, A. Davoodi, M. Jafari, M. K. Islamic Azad Univ Sci & Res Branch Dept Civil Engn Tehran Iran IIEES POB 19537-14476 Tehran Iran

Earth dams are one of the most important and expensive civil engineering structures to which a considerable amount of budget is allocated. Their construction costs mainly correspond to the size of embankments, which in turn depends on their cross sectional area. Therefore, reductions in cross-sectional areas of earth dams may cause a decrease in embankment volumes, leading to a significant reduction in the construction costs of these structures. On the other hand, it is almost impossible to obtain optimum cross section in earth dams with desired stability and acceptable operational dimensions using traditional design methods. In this paper, ant colony optimization algorithm (ACO), a well-known and powerful metaheuristic method for tackling problems in geotechnical engineering, was used to solve this complicated problem. The results showed that applying the ideal and optimum slope and berm arrangements resulting from ACO in designing embankments and earth dams with different heights could lead to a decrease in embankment volumes, compared to those without any berms or those with berms resulting from usual designs with trial and error. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Earth dams Slope stability Artificial intelligence ant colony optimization algorithm Control variable

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

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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

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Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning

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KNOWLEDGE-BASED SYSTEMS 2024年 288卷

作者： Cui, Junguo Wu, Lei Huang, Xiaodong Xu, Dengpan Liu, Chao Xiao, Wensheng China Univ Petr East China Coll Mech & Elect Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Marine Geophys Prospecting & Exp Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore

As a widely used path planning algorithm, the ant colony optimization algorithm (ACO) has evolved into a welldeveloped method within the realm of optimization algorithms and has been extensively applied across various fields. In this study, a multi-strategy adaptable ant colony optimization (MsAACO) is proposed to alleviate the insufficient and inefficient convergence of ACO, employing four-design improvements. First, a directionguidance mechanism is proposed to improve the performance of node selection. Second, an adaptive heuristic function is introduced to decrease the length and number of turns of the optimal path solutions. Moreover, the deterministic state transition probability rule was employed to promote the convergence speed of ACO. Finally, nonuniform pheromone initialization was utilized to enhance the ability of ACO to select advantageous regions. Subsequently, the major parameters of the strategies were optimized and their effectiveness was validated. MsAACO was proposed by combining these four strategies with ACO. To verify the advantages of MsAACO, five representative environment models were employed, and comprehensive experiments were conducted by comparing them with existing approaches, including the A* algorithm, variants of ACO, Dijkstra's algorithm, jump point search algorithm, best-first search, breadth-first search, trace algorithm, and other excellent algorithms. The experimental statistical results demonstrate that MsAACO can efficiently generate smoother optimal path-planning solutions with lower length and turn times and improve the convergence efficiency and stability of ACO compared to other algorithms. The generated results of MsAACO verified its superiority in solving the pathplanning problem of mobile robots.

关键词： Path planning ant colony optimization algorithm Directional mechanism Adaptive updating

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

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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

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

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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

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

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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

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

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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

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A PARALLEL ant colony optimization algorithm BASED ON FINE-GRAINED MODEL WITH GPU-ACCELERATION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2009年第11A期5卷 3707-3716页

作者： Li, Jianming Hu, Xiangpei Pang, Zhanlong Qian, Kunming Dalian Univ Technol Sch Elect & Informat Engn Dalian 116023 Liaoning Prov Peoples R China Dalian Univ Technol Inst Syst Engn Dalian 16023 Liaoning Prov Peoples R China

Fine-grained parallel ant colony optimization algorithm (FGACO), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a FGACO method based on GP U-acceleration, which maps parallel ACO algorithm to GPU through the compute unified device architecture (CUDA). The analytical results demonstrate that, the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGACO solution.

关键词： ant colony optimization algorithm Parallel process CPU CUDA Fine-grained

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Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine

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ANALYTICA CHIMICA ACTA 2011年第2期703卷 163-171页

作者： Li, Zhan-Chao Zhou, Xuan Dai, Zong Zou, Xiao-Yong Sun Yat Sen Univ Sch Chem & Chem Engn Guangzhou 510275 Guangdong Peoples R China Guangdong Pharmaceut Univ Sch Chem & Chem Engn Guangzhou 510006 Guangdong Peoples R China

Protein methylation is involved in dozens of biological processes and plays an important role in adjusting protein physicochemical properties, conformation and function. However, with the rapid increase of protein sequence entering into databanks, the gap between the number of known sequence and the number of known methylation annotation is widening rapidly. Therefore, it is vitally significant to develop a computational method for quick and accurate identification of methylation sites. In this study, a novel predictor (Methy_SVMIACO) based on support vector machine (SVM) and improved ant colony optimization algorithm (IACO) is developed to identify methylation sites. The IACO is utilized to find the optimal feature subset and parameter of SVM, while SVM is employed to perform the identification of methylation sites. Comparison of the IACO with conventional ACO shows that the IACO converges quickly toward the global optimal solution and it is more useful tool for feature selection and SVM parameter optimization. The performance of Methy_SVMIACO is evaluated with a sensitivity of 85.71%, a specificity of 86.67%, an accuracy of 86.19% and a Matthew's correlation coefficient (MCC) of 0.7238 for lysine as well as a sensitivity of 89.08%, a specificity of 94.07%, an accuracy of 91.56% and a MCC of 0.8323 for arginine in 10-fold cross-validation test. It is shown through the analysis of the optimal feature subset that some upstream and downstream residues play important role in the methylation of arginine and lysine. Compared with other existing methods, the Methy_SVMIACO provides higher Acc, Sen and Spe, indicating that the current method may serve as a powerful complementary tool to other existing approaches in this area. The Methy_SVMIACO can be acquired freely on request from the authors. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Methylation Support vector machine ant colony optimization algorithm Feature selection

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

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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

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