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Cloud Computing Demand Elasticity algorithm based on ant colony algorithm

RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2021年第1期14卷 37-43页

作者： Liu, Chunyu Mu, Fengrui Zhang, Weilong Cangzhou Normal Univ Coll Comp Sci & Engn Cangzhou 061000 Hebei Peoples R China Hebei Jiaotong Vocat & Tech Qual Management Ctr Shijiazhuang 050035 Hebei Peoples R China

Background: In the recent era of technology, the traditional ant colony algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, the results show that the improved method has great application value in improving network efficiency, balancing network load, prolonging network life and increasing network operating profit.

关键词： Cloud computing ant colony algorithm elasticity of demand theory amazon Elastic Compute Cloud (Amazon EC2) Platform as a Service (PaaS) ant colony algorithm (ACO)

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Improved ant colony algorithm Based on Binarization in Computer Text Recognition

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 1084-1093页

作者： Li, Zhen Criminal Invest Police Univ China Sch Forens Sci & Technol Shenyang 110854 Peoples R China

Pheromones, path selection, and probability transfer functions are the main factors that affect the performance of computer text recognition. The path selection function is the most important factor affecting the recognition rate. In response to the difficulties in path selection and slow algorithm convergence in the text recognition, an edge detection algorithm based on improved ant colony optimization algorithm is proposed. The strong denoising performance of the ant colony optimization algorithm reduces the interference of textured backgrounds. The edge extraction effect is analyzed in the connected domain to overcome complex effects. Finally, the improved Otsu binarization algorithm is used to recognize the text. According to the results, the proposed method could effectively preserve the edge information of characters in images. The positioning effect of the text area was good. The accuracy rate reached around 85%. The tuned threshold improved the binarization effect. The text recognition rate of the improved ant colony algorithm proposed in the research has generally reached 80%, with good text positioning accuracy and recognition rate, which has great practical significance in computer text recognition.

关键词： Binarization ant colony algorithm text recognition edge detection Otsu algorithm

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Task allocation optimization model in mechanical product development based on Bayesian network and ant colony algorithm

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JOURNAL OF SUPERCOMPUTING 2021年第12期77卷 13963-13991页

作者： Liu, Taotao Duan, Guijiang Beihang Univ BUAA Sch Mech Engn & Automat Beijing 100191 Peoples R China

A task allocation optimization model in mechanical product development was proposed. First, Bayesian network was introduced and used to describe quality characteristic (QC) relations of mechanical products. The quality characteristic importance was then analyzed based on the Bayesian network. Weights were assigned to QCs according to the QCs importance. QCs are generally manufactured by different types of manufacturing resources, such as machine tools, cutting tools, measuring tools, and fixtures. For convenience, these manufacturing resources were encapsulated as manufacturing cells. QCs were considered as tasks to be completed by manufacturing cells. Numerous tasks and manufacturing cells were involved in mechanical product development;therefore, the core problem was to allocate these tasks to manufacturing cells in an optimal way to achieve better product quality. Time and cost were considered as the main constraints. The ant colony algorithm was used to solve the task allocation optimization problem. A numerical case study was also presented to indicate the effectiveness of the proposed model.

关键词： ant colony algorithm Bayesian network Mechanical products Task allocation

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Quality Evaluation Approach for Prefabricated Buildings Using ant colony algorithm and Simulated Annealing algorithm to Optimize the Projection Pursuit Model

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BUILDINGS 2023年第9期13卷

作者： Wang, Qun Xu, Xizhen Ding, Xiaoxin Chen, Tiebing Deng, Ronghui Shenzhen Polytech Univ Sch Construct Engn Shenzhen 518055 Peoples R China Jilin Jianzhu Univ Sch Econ & Management Changchun 130119 Peoples R China

There are problems with an inadequate quality assurance system and non-standard construction organization and administration while creating prefabricated buildings. There are currently fewer quality assessments employing prefabricated component combinations as the research focus, and the quality evaluation methodology is more subjective. We propose a method for evaluating the quality of prefabricated buildings using an ant colony algorithm and a simulated annealing algorithm to optimize the projection pursuit model: firstly, create a prefabricated building quality index system;secondly, questionnaires were distributed, tested for reliability and validity to avoid the influence of questionnaire subjectivity on the results, and structural equation modeling was used to calculate the weights of the quality influencing factors;thirdly, quantify the quality factors of prefabricated components by using the quality function development method, and construct a quality optimization model for the prefabricated component combinations;fourthly, use the ant colony algorithm to solve the quality optimization model to obtain a set of prefabricated component combinations to satisfy the quality requirements;and lastly, use a simulated annealing to optimize the projected pursuit method for evaluating the quality of prefabricated component combination solutions. The results show that (1) The use of optimization algorithms can successfully avoid the issue of a more subjective evaluation approach and increase the efficiency and accuracy of evaluation. (2) Residential Comfort (RC), Usage Durability (UD) and Structural Reliability (SR) have a substantially negative association, but Residential Comfort (RC) and Installation Stability (IS) have strong positive correlations. (3) Based on the magnitude of the vector of the ideal projection direction of the quality indicators, it was determined that the Installation Stability (IS) indicator had the greatest influence on the evaluation of the pr

关键词： prefabricated building quality evaluation ant colony algorithm simulated annealing algorithm projection pursuit method

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Location and path planning for urban emergency rescue by a hybrid clustering and ant colony algorithm approach

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Yang, Bing Wu, Lunwen Xiong, Jian Zhang, Yuxin Chen, Lidong Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Peoples R China Natl Univ Def Technol Beijing Inst Adv Study Beijing 100000 Peoples R China

Rescue station setup and rescue path planning are two important tasks in urban emergency rescue. The former task ensures rescue response capability and the latter task provides effective rescue solutions. When emergencies occur in cities, evacuees are distributed along the urban road network. Rescue resources refer to rescue vehicles whose available number and capacity are both limited. With the constraints of rescue resources and the number of rescues, this paper aims to simultaneously optimize the tasks of rescue station setup and rescue path planning. In the addressed scenario, the priority of each evacuee is quantified as a weight value that is used as the main optimization objective. To solve the problem, a comprehensive urban emergency rescue planning approach is proposed. The proposed approach consists of components of road network processing, road network weight calculation, rescue station setup and rescue path planning. For the setup of rescue stations, this paper employs a clustering method to provide a set of high-quality candidate rescue stations for subsequent path planning based on the locations of evacuees and the road network structure. For rescue path planning, an improved ant colony optimization algorithm is developed. The proposed method is called the planning algorithm with clustering and improved ant colony optimization (PA-C-IACO). The proposed PA-C-IACO redefines the degree of heuristic and pheromone concentration increments for transfer between intersections in the ant colony algorithm and incorporates a reward mechanism during the pheromone update process. Experimental results on six different size datasets show that PA-C-IACO outperforms state-of-the-art algorithms and shows good robustness and feasibility.(c) 2023 Published by Elsevier B.V.

关键词： Urban emergency rescue Planning of location and path Clustering ant colony algorithm

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An Adaptive ant colony algorithm Based on Local Information Entropy to Solve Distributed Constraint Optimization Problems

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第9期37卷 2359013-2359013页

作者： Shi, Meifeng Xiao, Shichuan Feng, Xin Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China

As a meta-heuristic algorithm, the ant colony algorithm has been successfully used to solve various combinatorial optimization problems. However, the existing algorithm that takes the power of ants to solve distributed constraint optimization problems (ACO_DCOP) is easy to fall into local optima. To deal with this issue, this paper presents an adaptive ant colony algorithm based on local information entropy to solve distributed constraint optimization problems, named LIEAD. In LIEAD, the local information entropy is introduced to help agents adaptively select the pheromone update strategy and value selection strategy, which improves the convergence speed and the quality of the solution. Moreover, a restart mechanism is designed to break the accumulation state of pheromone, which increases the population diversity and helps the algorithm jump out of the local optima. The extensive experimental results indicate that LIEAD can significantly outperform ACO_DCOP and is competitive with the state-of-the-art DCOPs algorithms.

关键词： ant colony algorithm distributed constraint optimization problems local information entropy pheromone DCOPs algorithms

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Imaging lens design with the aid of an ant colony algorithm

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JOURNAL OF OPTICS-INDIA 2021年第4期50卷 644-655页

作者： Liu, Wenlin Feng, Yanze Lv, Zhiwei Shi, Yuetong Liu, Zixiang Bai, Chenghao Qin, Hua Shandong Univ Technol Sch Phys & Optoelect Engn Zibo 255000 Shandong Peoples R China

In modern optical design, the calculation of optical merit function based on the ray tracing would be impossible without the aid of a computer. Finding the optimal value of merit function by optimization techniques would also be impossible without the aid of a computer. With the rapid development of computer technology and the emergence of novel optimization techniques, it is a beneficial attempt to apply new optimization techniques to optical design with the aid of high-performance computers, so as to realize intelligent and quick design of the image acquisition lens of high precision. In this paper, the optimization technique of ant colony is employed to design an iris image capture lens consisting of two aspheric lenses with the aid of a desktop computer of a 64-bit processor and dual-core CPU, and the detailed design steps are presented. Imaging performance of the designed image acquisition lenses is quantitatively evaluated by using Zemax software, and the analysis shows that high-resolution image with MTF > 0.4 at the Nyquist frequency of 223 lp/mm below 0.8 field of view (FOV) and optical distortion of less than 0.2% below 0.5 FOV can be acquired, demonstrating a great potential of the ant colony optimization algorithm in quickly designing high-quality optical lenses. We also apply the ACA to the benchmark problems for lens design problem and demonstrated that this method can provide high-quality solution.

关键词： Lens design ant colony algorithm Ray tracing Merit function

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Optimisation and application research of ant colony algorithm in vehicle routing problem

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2021年第2期13卷 177-193页

作者： Niu, Lede Xiong, Liran Yunnan Normal Univ Sch Tourism & Geog Sci Kunming 650500 Yunnan Peoples R China Yunnan Normal Univ Res Ctr Opening Southwest China & Frontier Secur Kunming 650500 Yunnan Peoples R China

In this paper, an improved ant colony algorithm based on ant system is proposed in order to solve vehicle routing problem. When choosing the path, the 2-opt method is used to explore the reasonable selection of the parameters of the algorithm for vehicle routing problem taking the path savings among customers as heuristic information. The performance of ant colony algorithm is affected by the information heuristic factor alpha, expectation heuristic factor beta and pheromone volatile factor rho. The method breaks through empirically setting the ant colony algorithm parameter values. By calculation, the optimal parameters of the ant colony algorithm in solving the vehicle routing problem are: alpha epsilon [1.0, 1.7], beta epsilon [4.5, 8.5], rho epsilon [0.5, 0.6]. At last, an exploration is established to find the optimal solution by combining three parameters and the ant colony algorithm will have a better effect in the actual optimisation problem.

关键词： ant colony algorithm vehicle routing problem parameter selection

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ANALYSIS ON PATH OPTIMIZATION OF AGRICULTURAL WAREHOUSE LOGISTICS HANDLING ROBOT BASED ON POTENTIAL FIELD ant colony algorithm

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INMATEH-AGRICULTURAL ENGINEERING 2024年第2期73卷 784-795页

作者： Wang, Yunyun Xie, Mingzhe Wuhan Univ Technol Sch Management Wuhan Hubei Peoples R China Ningbo Univ Technol Sch Econ & Management Ningbo Zhejiang Peoples R China

In the layout of modern agricultural warehouse logistics handling industry, it was an inevitable way to realize industrial upgrading by replacing people with mobile robots. Aiming at the problems that the existing obstacle avoidance control algorithm of agricultural handling robot was easy to fall into local optimal solution, and the operation process of agricultural warehouse logistics handling robot was prone to collision, the obstacle avoidance control of agricultural warehouse logistics handling robot was studied. In addition, a control algorithm based on improved potential field ant colony was proposed. The moving trajectory of the agricultural warehouse logistics handling robot during the handling process was studied, and the spatial kinematics equation of the robot was given. The ant colony algorithm was used to optimize the classical artificial potential field algorithm to improve the global optimization ability and balance the interaction between gravity and repulsion. In the aspect of local area obstacle avoidance of agricultural storage and handling robots, the artificial potential field was optimized twice based on the strategy gradient algorithm. By analyzing the probability of the next action command, the randomness of the travel path selection when multiple robots work at the same time was improved. After testing, the path of the proposed control algorithm was the shortest, and under the condition of complex path planning, the number of collisions between robots was also significantly less than that of the traditional obstacle avoidance control algorithm. The practical application could meet the needs of improving the efficiency of warehouse logistics management.

关键词： Artificial potential field ant colony algorithm warehouse handling robot obstacle avoidance strategy gradient algorithm

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The Influence of ant colony algorithm on the Power Distribution Scheme of Thermal Power Plants

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2024年第2期22卷 12-20页

作者： Liao, Huihui Anhui Technical College of Industry and Economy Hefei230051 China

The power distribution scheme of thermal power plants can balance the power flow voltage and current in the station area to ensure the stability of power grid operation, and the changes in power distribution scheme such as new energy, dwarf, wind power, wind power, etc., to ensure the quality of power supply of the whole power grid, but in the process of thermal power grid distribution, there are interference factors such as a large amount of data, difficulty in data analysis, and stability of new energy power generation, which affect the effectiveness of the entire distribution scheme In the mediation process of new energy sources such as photovoltaics, the stability of voltage and current is improved, kept between 90% ~ 95%, and the current of the power grid is effectively constrained to avoid the problem of excessive current and voltage, and the accuracy of power distribution is improved, and the improvement rate reaches 10~20%. Compared with the previous algorithms, it is found that the algorithm can shorten the power distribution time of thermal power plants and keep it between 5~6 seconds. Therefore, the ant colony algorithm can optimize the power distribution scheme of thermal power plants and support the grid connection of new energy. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： ant colony algorithm Energy Balance Power Distribution Thermal Power Plant

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