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Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path

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MICROMACHINES 2021年第1期12卷 88-88页

作者： Xin, Yupeng Li, Yuanheng Li, Wenhui Wang, Gangfeng Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Aeronaut & Astronaut Taiyuan 030024 Peoples R China Changan Univ Sch Construct Machinery Key Lab Expressway Construct Machinery Shaanxi Pr Xian 710064 Peoples R China

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

关键词： smart manufacturing complex structural parts processing sequence planning corner milling ant colony optimization algorithm

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Routing optimization of Two-Echelon Two-Way Logistics in Rural Areas Based on E-Commerce Environment

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TRANSPORTATION RESEARCH RECORD 2024年

作者： Hong, Wei Du, Chen Xu, Shuling Pu, Xujin Jiangnan Univ Sch Business Wuxi Peoples R China Jiangnan Univ Food Safety Res Base Jiangsu Prov Wuxi Peoples R China Jiangnan Univ Inst Food Safety Risk Management Wuxi Peoples R China

This is a study of the two-echelon two-way logistics vehicle routing problem in rural areas while accounting for government subsidies to alleviate the high distribution costs associated with "long transportation chain + low consumption density" in rural logistics. To address the issue, an improved adaptive ant colony optimization (IACO) algorithm was developed and simulations were run to validate it. The experimental findings demonstrate that, in comparison with the conventional adaptive ant colony optimization algorithm method, the IACO algorithm offers superior optimization capabilities. Finally, a case study was conducted in a rural area of China to demonstrate that the two-echelon two-way logistics network outperforms the current single-echelon logistics network by shortening the total driving distance and reducing distribution costs. This research can inform the optimization of rural logistics networks and provide a reference for the rural e-commerce logistics industry.

关键词： rural e-commerce ant colony optimization algorithm two-way logistics network two-echelon vehicle routing problem

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Elitist-Mutated ant System Versus Max-Min ant System: Application to Pipe Network optimization Problems

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SCIENTIA IRANICA TRANSACTION A-CIVIL ENGINEERING 2009年第4期16卷 286-296页

作者： Afshar, M. H. Iran Univ Sci & Technol Dept Civil Engn Tehran Iran

The ant colony optimization algorithm (ACOA) is a new class of stochastic search algorithm proposed for the solution of combinatorial optimisation problems. Different versions of ACOA are developed and used with varying degrees of success. The Max-Min ant System (MMAS) is recently proposed as a remedy for the premature convergence problem often encountered with ACOAs using elitist strategies. The basic concept behind MMAS is to provide a logical balance between exploitation and exploration. The method, however, introduces some additional parameters to the original algorithm, which should be tuned for the best performance of the method adding to the computational requirement of the algorithm. An alternative method to MMAS is proposed in this paper and applied to pipe network optimization problem. The method uses a simple but effective mechanism, namely Pheromone Trail Replacement (PTR), to make sure that the global best solution path has always the maximum trail intensity. This mechanism introduces enough exploitation into the method and more importantly enables one to exactly predict the number of global best solutions at each iteration of the algorithm without requiring calculation of the cost of the solutions created. The sub-colony of repeated global best solutions of the iterations is then mutated, such that a predefined number of solutions survive the mutation process. Two different mutation mechanisms, namely deterministic and stochastic mutation processes, are introduced and used. The first one uses a one bit mutation with a probability of one on some members of the sub-colony, while the second one uses a uniform mutation on the whole sub-colony. The probability of mutation in the second mutation process is adjusted at each iteration, so that the required number of global-best solutions survives the mutation. The method is shown to produce results comparable to the MMAS algorithm, while requiring less free parameter tuning. The application of the method to a

关键词： Mutated ant colony optimization algorithm Pipe networks Optimal design

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A new solution method of ant colony-based logistic center area layout problem

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2018年第6期43卷 83-83页

作者： Zarali, Fulya Yazgan, Harun Resit Delice, Yilmaz Erciyes Univ Develi Vocat High Sch Logist Program TR-38300 Kayseri Turkey Sakarya Univ Fac Engn Dept Ind Engn TR-54100 Adapazari Sakarya Turkey

In today's world, logistic centers not only play an important roles in sustaining the nation's economy, they also significantly contribute to the economic and social development of the regions in which they are located. The layout of the center is crucial in ensuring that such important centers are both efficient and productive. To achieve this, this study focuses on the development of a logistic center layout that is integrated with the ant colony optimization algorithm. To this end, the logistic center area layout was developed by applying the developed algorithm to an actual logistic center planned to be constructed. The efficiency of the suggested algorithm was tested in accordance with the benchmark problems in the literature. In addition, a case study was carried out to illustrate the effectiveness of the proposed approach. The obtained results revealed that the suggested algorithm provided more efficiency than other layouts.

关键词： Logistic logistic center area layout ant colony optimization algorithm optimization

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Metaheuristic approach to optimal pathfinding in wireless charging of sensor nodes using autonomous bots

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Procedia Computer Science 2020年 171卷 2147-2156页

作者： Dinesh Kumar G Sarath. R. Menon M Pradeep Ramanathan R

Wireless Sensor Nodes play a prominent role in many military and civilian applications. They are used to collect data for various disciplines from diverse environments. The main drawback of this technology is the decrease in energy of the wireless sensor nodes as time progresses. A significant amount of works has been done and justified in using an autonomous bot to charge the wireless sensor nodes. There are a lot of difficulties to overcome to implement the concept of using an autonomous bot to charge the independent wireless sensor nodes. One such cumbersome issue is the scheming of an optimal path for the autonomous bot to charge these wireless sensor nodes efficiently. In this paper, we present a comparative study of various methods ant colony optimization algorithm, Simulated Annealing and Tabu search to compute the tour of the Autonomous bot to recharge the nodes, considering the tour length and the time taken to compute the tour length. The result of this study gives which algorithm is best suited for a given network with a given number of nodes with respect to tour length and time taken to compute the tour.

关键词： Wireless Sensor Nodes Wireless charging Mobile charger Autonomous bot Travelling Salesman Problem Metaheuristic Kruskal’s algorithm ant colony optimization algorithm Simulated Annealing Tabu search

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Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

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Journal of Applied Mathematics and Physics 2023年第9期11卷 2630-2643页

作者： Qi Zhang Qiaozhen Zhang School of Mathematical Sciences Nankai University Tianjin China The Key Laboratory of Pure Mathematics and Combinatorics Ministry of Education Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin Laboratory for Economic Behaviors and Policy Simulation School of Statistics and Data Science Nankai University Tianjin China

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

关键词： Electric Material Handling Vehicles Battery Swap Station Location Scheduling Scheme NSGA-II algorithm ant colony optimization algorithm

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Task Offloading Scheduling in Mobile Edge Computing Networks

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Procedia Computer Science 2021年 184卷 322-329页

作者： Zhonglun Wang Peifeng Li Shuai Shen Kun Yang Quzhou Research Institute of University of Electronic Science and Technology of China Quzhou Zhejiang China University of Electronic Science and Technology of China Chengdu 611731 China School of Computer Science and Electronic Engineering University of Essex Colchester CO4 3SQ U.K.

In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user equipments (UEs) in computing. We aim to jointly minimize the system energy consumption and maximize the number of offloaded tasks by optimizing the task scheduling between UEs and BSs. A multiple-objective and mix-integer problem is formulated, which is difficult to solve. To tackle the problem, we combine the ant colony optimization (ACO) algorithm with load balancing, and propose an efficient algorithm. The simulation demonstrates the effectiveness of the proposed algorithm.

关键词： Energy minimization offloading number maximization mobile edge computing load balancing ant colony optimization algorithm

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Proposing a new model for location - routing problem of perishable raw material suppliers with using meta-heuristic algorithms

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HELIYON 2019年第12期5卷 e03020-e03020页

作者： Yaghoubi, Ali Akrami, Farideh Raja Univ Dept Engn Qazvin Iran Ghazali Higher Educ Inst Dept Ind Management Qazvin Iran

In the last three decades, an integrated approach to optimize logistics system is considered as one of the most important aspects of optimizing supply chain management. This approach involves the ties between locations of facility, allocation of suppliers/customers, structure of transportation routes and inventory control. The aim of this paper is to investigate the ordering planning of a supply chain with multi supplier, multi distribution center, multi customer and one perishable raw material. This paper provides a mathematical model taking in consideration the limitation of raw material corruptibility (perishable material) which belongs to the category of NP-hard problems. To solve the proposed model, the ant colony optimization algorithm (ACO) and Particle Swarm optimization algorithm (PSO) are employed. In order to improve performances of ACO and PSO parameters, a Taguchi experimental design method was applied to set their proper values. Besides, to evaluate the performance of the proposed model, an example of the dairy industry is analyzed by using MATLAB R 2015a. To validate the proposed meta-heuristic algorithms, the results of them were compared with together. The results of the comparison show that ACO is greater than PSO in speed convergence rate and the number of solutions iterations.

关键词： Systems engineering Industrial engineering Mathematical modeling Computational intelligence Process modeling Industry management Supply chain management ant colony optimization algorithm Particles swarm optimization algorithm Perishable raw material

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QoS-based Semantic Micro Services Discovery and Composition using ACO algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第6期10卷 159-168页

作者： Essayah, Ahmed Youssfi, Mohamed Bouattane, Omar Mansouri, Khalifa Illoussamen, Elhocein Hassan II Univ Casablanca ENSET Mohammedia Lab SSDIA Mohammadia 28999 Morocco

In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths. In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective. The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system. Learners' requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph. Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs. In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination. Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.

关键词： Semantic micro service quality of service learning path e-learning platform service discovery and composition ant colony optimization algorithm

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ACO and SVM Selection Feature Weighting of Network Intrusion Detection Method

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2015年第4期9卷 141-152页

作者： Wang Xingzhu Univ Arts & Sci Furong Coll Hunan Hunan Changde 415000 Peoples R China

Feature selection and classifier design is the key to network intrusion detection. In order to improve network intrusion detection rate for feature selection problem, this paper proposed a network intrusion detection method (ACO-FS -SVM) combining ant colony algorithm to select the features with a feature weighting SVM First, the use of support vector machine classification accuracy and feature subset dimension construct a comprehensive fitness weighting index. Then use the ant colony algorithm for global optimization and multiple search capabilities to achieve optimal solutions feature subset search feature. And then selected the key feature of network data and calculated information gain access to various features weights and heavy weights to build support vector machine classifier based on the characteristics of network attacks right. At last, refine the final design of the local search methods to make the feature selection results without redundant features while improve the convergence resistance, and verify the data set by KDD1999 effectiveness of the algorithm. The results show that ACO-FS-SVM can effectively reduce the dimension of features, and have improved network intrusion detection accuracy and detection speed.

关键词： Feature Selection Feature Weighting ant colony optimization algorithm Support Vector Machines Network Intrusion Detection

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