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Journal articles on the topic 'Population based ACO'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Alam, Md Amirul, Abdul Shukor Juraimi, M. Y. Rafii, Azizah Abdul Hamid, and Farzad Aslani. "Screening of Purslane (Portulaca oleraceaL.) Accessions for High Salt Tolerance." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/627916.

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Purslane (Portulaca oleraceaL.) is an herbaceous leafy vegetable crop, comparatively more salt-tolerant than any other vegetables with high antioxidants, minerals, and vitamins. Salt-tolerant crop variety development is of importance due to inadequate cultivable land and escalating salinity together with population pressure. In this view a total of 25 purslane accessions were initially selected from 45 collected purslane accessions based on better growth performance and subjected to 5 different salinity levels, that is, 0.0, 10.0, 20.0, 30.0, and 40.0 dS m−1NaCl. Plant height, number of leaves

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Wang, Chunyu, Fan Zhang, Yue Deng, Chao Gao, Xianghua Li, and Zhen Wang. "An adaptive population control framework for ACO-based community detection." Chaos, Solitons & Fractals 138 (September 2020): 109886. http://dx.doi.org/10.1016/j.chaos.2020.109886.

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Tan, Daniel J., Caroline J. Lodge, Adrian J. Lowe, et al. "Bronchodilator reversibility as a diagnostic test for adult asthma: findings from the population-based Tasmanian Longitudinal Health Study." ERJ Open Research 7, no. 1 (2021): 00042–2020. http://dx.doi.org/10.1183/23120541.00042-2020.

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Bronchodilator reversibility (BDR) is often used as a diagnostic test for adult asthma. However, there has been limited assessment of its diagnostic utility. We aimed to determine the discriminatory accuracy of common BDR cut-offs in the context of current asthma and asthma–COPD overlap (ACO) in a middle-aged community sample.The Tasmanian Longitudinal Health Study is a population-based cohort first studied in 1968 (n=8583). In 2012, participants completed respiratory questionnaires and spirometry (n=3609; mean age 53 years). Receiver operating characteristic (ROC) curves were fitted for curre

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Lu, Litao. "Key Technologies of Software Engineering Based on T-ACO Algorithm." Mobile Information Systems 2022 (September 12, 2022): 1–14. http://dx.doi.org/10.1155/2022/4429373.

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The main core of software engineering key technologies is the development of software services, ensuring the scientificity, security, and stability of the application software engineering system. At present, China’s economic development urgently needs the support of software engineering technology. Based on the T-ACO algorithm, the scientificity of software engineering and the accuracy of data have been significantly improved compared with traditional software engineering technology. It plays an important role in promoting the follow-up software engineering technology. In order to effectively

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Chen, Guangqiao, Jun Gao, and Daozheng Chen. "Research on Vehicle Routing Problem with Time Windows Based on Improved Genetic Algorithm and Ant Colony Algorithm." Electronics 14, no. 4 (2025): 647. https://doi.org/10.3390/electronics14040647.

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The Vehicle Routing Problem with Time Window (VRPTW) is of crucial importance in modern societies, where the aim is to optimize resource utilization, reduce costs and address constraints such as time and vehicle capacity. Traditional genetic algorithms often face premature convergence and slow speed in solving such problems. This paper proposes an Improved Genetic Ant Colony Optimization (IGA-ACO) algorithm to efficiently solve VRPTW. The algorithm combines the strengths of a genetic algorithm with the Generalized Variable Neighborhood Search (GVNS) and Ant Colony Optimization (ACO), aiming to

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Huang, Qing, Heru Xue, Jiangping Liu, and Xinhua Jiang. "A New Method for Spectral Wavelength Selection Based on Multiple Linear Regression Combined with Ant Colony Optimization and Genetic Algorithm." Journal of Spectroscopy 2022 (November 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/2440518.

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Wavelength selection is one of the key steps in quantitative spectral analysis, which reduces the computation time while also improving the prediction accuracy of the model. In this paper, we propose a wavelength selection algorithm based on the ant colony optimization (ACO), in which the absolute value of the regression coefficient of the multiple linear regression (MLR) model is used as the basis for evaluating the importance of wavelengths, and the absolute value of the regression coefficient after full wavelength MLR modeling is used as the initial pheromone value of the ant colony optimiz

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Jo, Yong Suk. "Status of Studies Investigating Asthma–Chronic Obstructive Pulmonary Disease Overlap in Korea: A Review." Tuberculosis and Respiratory Diseases 85, no. 2 (2022): 101–10. http://dx.doi.org/10.4046/trd.2021.0140.

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There is a considerable number of individuals who exhibit features of both asthma and chronic obstructive pulmonary disease (COPD), defined as asthma-COPD overlap (ACO). Many studies have reported that these patients have a greater burden of symptoms, including cough and dyspnea, and experience more exacerbations and hospitalizations than those with non-ACO COPD or asthma. Although diagnostic criteria for ACO have not yet been clearly established, their clinical significance remains to be determined. As interest in ACO grows, related studies have been conducted in South Korea as well. The pres

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Yang, Lin, Qinghua Chen, Junjie Mu, Tangying Liu, Xiaoxiao Li, and Shuxiang Cai. "Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm." Biomimetics 10, no. 5 (2025): 285. https://doi.org/10.3390/biomimetics10050285.

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Ship replenishment path planning is a critical problem in the field of maritime logistics. This study proposes a novel synergistic hybrid optimization algorithm (SHOA) that effectively integrates ant colony optimization (ACO), the Clarke–Wright algorithm (CW), and the genetic algorithm (GA) to solve the capacitated multi-ship replenishment path planning problem (CMSRPPP). The proposed methodology employs a three-stage optimization framework: (1) initial path generation via parallel execution of the CW and ACO; (2) population initialization for the GA by strategically combining optimal solution

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L. C., Kho, Kasihmuddin M. S. M., Mansor M. A., and Sathasivam S. "Propositional Satisfiability Logic via Ant Colony Optimization in Hopfield Neural Network." Malaysian Journal of Mathematical Sciences 16, no. 1 (2022): 37–53. http://dx.doi.org/10.47836/mjms.16.1.04.

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Minimizing the cost function that corresponds to propositional logic is vital to ensure the learning phase of HNN can occur optimally. In that regard, optimal and non-biased algorithm is required to ensure HNN will always converge to global solution. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to solve various combinatorial optimization problems. ACO simulates the behaviour of the real ants that forage for food and communication of ants through pheromone density. In this work, ACO will be used to minimize the cost function that corresponds to the logical r

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Samadzadegan, Farhad, and Hadiseh Hasani. "Determination Optimum SVMs Classifiers for Hyperspectral Imagery Based on Ant Colony Optimization." Key Engineering Materials 500 (January 2012): 792–98. http://dx.doi.org/10.4028/www.scientific.net/kem.500.792.

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Referring to robustness of SVMs in high dimensional space, they are reliable tools for classification of hyperspectral imagery. However their performance is directly affected by two aspects: parameter determination and optimum feature subset selection. According to capacity of population based meta-heuristic optimization algorithm such as Ant Colony Optimization (ACO), they can find optimum or near optimum solution in complex optimization problems. This paper evaluates the potential of Binary ACO (BACO) in parameter determination, feature selection and both of them simultaneously in SVMs based

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Widians, Joan Angelina, Retantyo Wardoyo, and Sri Hartati. "A Hybrid Ant Colony and Grey Wolf Optimization Algorithm for Exploitation-Exploration Balance." Emerging Science Journal 8, no. 4 (2024): 1642–54. http://dx.doi.org/10.28991/esj-2024-08-04-023.

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The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) are well-known nature-inspired algorithms. ACO is a metaheuristic search algorithm that takes inspiration from the behavior of real ants. In contrast, GWO is a grey wolf population-based heuristic algorithm. The important procedure in optimization is exploration and exploitation. ACO has excellent global and local search capabilities, and the exploration process is performed better than the exploitation process. In the case of regular, GWO is a greatly competitive algorithm compared to other common meta-heuristic algorithms, as it

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Rajaković, Nikola. "Implementacija algoritma kolonije mrava za optimalnu rekonfiguraciju distributivne mreže." Energija, ekonomija, ekologija 22, no. 1-2 (2020): 50–57. http://dx.doi.org/10.46793/eee20-1-2.050m.

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The paper analyzes the possibility of application of Ant Colony Optimization (ACO) algorithm for reconfiguration of distribution network with the aim of active power minimiza- tion. ACO is a population-based meta-heuristic technique used to solve different combinatorial optimization problems. The search technique is inspired by the behaviour of ant colonies in nature. The efficiency of the proposed algorithm is demonstrated on IEEE 33-bus and IEEE 69-bus test distribution systems. Also, the results obtained by using ACO algorithm are compared to the results achievable by other heuristic and me

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Poudel, Yam, Jeewan Phuyal, and Rajiv Kumar. "Comprehensive Study of Population Based Algorithms." American Journal of Computer Science and Technology 7, no. 4 (2024): 195–217. https://doi.org/10.11648/j.ajcst.20240704.17.

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The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems

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Zhang, Y. Q., and H. Zhang. "Dynamic Scheduling of Blocking Flow-Shop Based on Multi-Population ACO Algorithm." International Journal of Simulation Modelling 19, no. 3 (2020): 529–39. http://dx.doi.org/10.2507/ijsimm19-3-co15.

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Thiruvady, Dhananjay, Su Nguyen, Fatemeh Shiri, Nayyar Zaidi, and Xiaodong Li. "Surrogate-assisted population based ACO for resource constrained job scheduling with uncertainty." Swarm and Evolutionary Computation 69 (March 2022): 101029. http://dx.doi.org/10.1016/j.swevo.2022.101029.

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GAO, YUELIN, and MIAOMIAO WANG. "HYBRID CODING PSO-ACO CO-EVOLUTIONARY ALGORITHM FOR SOLVING MIXED-INTEGER PROGRAMMING PROBLEMS." Asia-Pacific Journal of Operational Research 30, no. 03 (2013): 1340001. http://dx.doi.org/10.1142/s0217595913400010.

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A co-evolutionary algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) is given to solve the bound constrained mixed-integer programming problem (BCMIP). For the specificity of the problem, the hybrid coding includes the real coding and the integer coding. The real coding part is evolved by PSO while the integer coding part is evolved by ACO. The entire population is co-evolved by PSO and ACO. Numerical experiments show that the proposed algorithm is feasible and effective to solve BCMIP. We also obtain satisfactory result to solve MIP when the proposed algori

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Mohsen, Abdulqader M. "Annealing Ant Colony Optimization with Mutation Operator for Solving TSP." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8932896.

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Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve

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Opoku, P. "Establishing Accra Population-Based Cancer Registry." Journal of Global Oncology 4, Supplement 2 (2018): 66s. http://dx.doi.org/10.1200/jgo.18.64600.

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Background: The African Cancer Organization (ACO) seeks to establish Accra Population-Based Cancer Registry (ACR). The whole idea is to collect, store and analyze data on persons with cancer to provide complete, accurate and timely cancer report for interventional programs. Such information would guide us to monitor patient care, prioritize and allocate resources effectively, give understanding of the things we do not yet know, and also act as a driver for policy development for the urgent need of comprehensive cancer control in Ghana. Countries require cancer surveillance programs to collect

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Kang, Jian, Jinping Zheng, Baiqiang Cai, et al. "Current situation of asthma–COPD overlap in Chinese patients older than 40 years with airflow limitation: a multicenter, cross-sectional, non-interventional study." Therapeutic Advances in Respiratory Disease 14 (January 2020): 175346662096169. http://dx.doi.org/10.1177/1753466620961699.

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Background and aims: Asthma–chronic obstructive pulmonary disease (COPD) overlap (ACO) is poorly recognized in China. Our study determined the distribution of ACO and its clinical characteristics among patients (aged ⩾40 years) with airflow limitation at Chinese tertiary hospitals. Methods: This cross-sectional, non-interventional study (NCT02600221), conducted between December 2015 and October 2016 in 20 Tier-3 Chinese hospitals, included patients aged ⩾40 years with post-bronchodilator (BD) FEV1/FVC <0.7. The primary variable was distribution of ACO in adults with post-BD forced expirator

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Kar, Udit Narayan. "A RELATIVE INVESTIGATORY ANALYSIS ON ANT COLONY ALGORITHM AND GENETIC ALGORITHM FOR FEATURE SELECTION." EPH - International Journal of Science And Engineering 6, no. 3 (2020): 19–27. http://dx.doi.org/10.53555/eijse.v6i3.73.

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Feature subset selection is used as a common technique in data pre-processing for pattern recognition, machine learning and data mining has attracted much attention in recent years. A good feature selection method can reduce the cost of feature measurement and increase classifier efficiency and classification accuracy. One approach in the feature selection area is employing population based optimization algorithms such as Genetic Algorithm (GA)-based method and Ant Colony Optimization (ACO) based method. In this paper ant colony optimization algorithm (ACO) is compared to Genetic algorithm (GA

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Chandra, Agung, and Aulia Naro. "NATURE INSPIRED METAHEURISTICS COMPARATIVE STUDY TO SOLVE TRAVELING SALESMAN PROBLEM." Journal of Engineering and Management in Industrial System 9, no. 2 (2021): 1–10. http://dx.doi.org/10.21776/ub.jemis.2021.009.02.1.

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There are numerous optimization method to solve the traveling salesman problem, TSP. One of methods is metaheuristics which is the state of the art algorithm that can solve the large and complex problem. In this research, three of well-known nature inspired population based metaheuristics algorithm: Ant Colony Optimization – ACO, Artificial Bee Colony – ABC and Particle Swarm Optimization – PSO are compared to solve the 29 destinations by using Matlab program. The ACO produces the shortest distance, 94 kilometers and is more efficient than ABC and PSO methods.

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Salhany, Richard J., Daniel Genovese-Scullin, Jasmin A. Eversley-Danso, Humroy Mendez, Vladimir Rubinshteyn, and Nisha Lakhi. "Evaluating Inpatient Hospital Charges Associated With Trauma Service Patients Participating in an Accountable Care Organization." Health Services Insights 16 (January 2023): 117863292311663. http://dx.doi.org/10.1177/11786329231166367.

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Background: The Medicare Accountable Care Organization (ACO) Program has created a vehicle for providers who practice cost containment and exceed quality for the Medicare population. The success of ACO’s nationwide have been well documented. However, there is little research evaluating if there is a cost saving benefit in trauma care with respect to participating in an ACO. Thus, the primary objective of this study was to evaluate inpatient hospital charges associated with trauma service utilization of patients participating in the ACO compared to non-ACO patients. Methods: This case-control r

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Rahman, Muhammad Affiq Abd, Bazilah Ismail, Kanendra Naidu, and Mohd Khairil Rahmat. "Review on population-based metaheuristic search techniques for optimal power flow." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (2019): 373. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp373-381.

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<span>Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. <br /> The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm

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Roeva, Olympia, Dafina Zoteva, and Velislava Lyubenova. "Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm." Processes 9, no. 8 (2021): 1418. http://dx.doi.org/10.3390/pr9081418.

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In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behav

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Nelson-Brantley, Heather, Edward F. Ellerbeck, Stacy McCrea-Robertson, et al. "Implementation of cancer screening in rural primary care practices after joining an accountable care organisation: a multiple case study." Family Medicine and Community Health 9, no. 4 (2021): e001326. http://dx.doi.org/10.1136/fmch-2021-001326.

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ObjectiveTo describe common strategies and practice-specific barriers, adaptations and determinants of cancer screening implementation in eight rural primary care practices in the Midwestern United States after joining an accountable care organisation (ACO).DesignThis study used a multiple case study design. Purposive sampling was used to identify a diverse group of practices within the ACO. Data were collected from focus group interviews and workflow mapping. The Consolidated Framework for Implementation Research (CFIR) was used to guide data collection and analysis. Data were cross-analysed

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Qawqzeh, Yousef, Mafawez T. Alharbi, Ayman Jaradat, and Khalid Nazim Abdul Sattar. "A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments." PeerJ Computer Science 7 (August 25, 2021): e696. http://dx.doi.org/10.7717/peerj-cs.696.

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Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to cert

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Wang, Yingzhi, Tailin Han, Xu Jiang, Yuhan Yan, and Hong Liu. "Path Planning of Pattern Transfer Based on Dual-Operator and a Dual-Population Ant Colony Algorithm for Digital Mask Projection Lithography." Entropy 22, no. 3 (2020): 295. http://dx.doi.org/10.3390/e22030295.

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In the process of digital micromirror device (DMD) digital mask projection lithography, the lithography efficiency will be enhanced greatly by path planning of pattern transfer. This paper proposes a new dual operator and dual population ant colony (DODPACO) algorithm. Firstly, load operators and feedback operators are used to update the local and global pheromones in the white ant colony, and the feedback operator is used in the yellow ant colony. The concept of information entropy is used to regulate the number of yellow and white ant colonies adaptively. Secondly, take eight groups of large

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Niu, Qinyu, Yao Fu, and Xinwei Dong. "Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm." World Electric Vehicle Journal 15, no. 4 (2024): 166. http://dx.doi.org/10.3390/wevj15040166.

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To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoot

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Oleiwi, Bashra Kadhim, Hubert Roth, and Bahaa I. Kazem. "A Hybrid Approach Based on ACO and Ga for Multi Objective Mobile Robot Path Planning." Applied Mechanics and Materials 527 (February 2014): 203–12. http://dx.doi.org/10.4028/www.scientific.net/amm.527.203.

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In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for ef

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Guo, Dan, Ke Huang, Xiaolong Guan, et al. "Association between chronic respiratory diseases and frailty in Chinese elderly: a population-based longitudinal study." BMJ Open Respiratory Research 12, no. 1 (2025): e002171. https://doi.org/10.1136/bmjresp-2023-002171.

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BackgroundChronic respiratory diseases (CRDs) have been shown to be associated with frailty, but these findings have not yet reached a consensus. The aim of this study was to investigate the association between CRDs and frailty in the elderly using a nationally representative data from China.MethodsData from the China Health and Retirement Longitudinal Study (CHARLS) were analysed, including 3309 frailty-free participants followed for three waves from 2011. Frailty was assessed using the physical frailty phenotype scale, and CRDs were conformed by self-reported physician diagnoses. Cox proport

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Chen, Chi-Chung, Li Ping Shen, Chien-Feng Huang, and Bao-Rong Chang. "Assimilation-accommodation mixed continuous ant colony optimization for fuzzy system design." Engineering Computations 33, no. 7 (2016): 1882–98. http://dx.doi.org/10.1108/ec-08-2015-0248.

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Purpose The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design. Design/methodology/approach The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to app

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Nazri, S. S. Z., M. S. Hadi, H. M. Yatim, M. H. Ab Talib, and I. Z. M. Darus. "Modelling of Flexible Manipulator System via Ant Colony Optimization for Endpoint Acceleration." Journal of Physics: Conference Series 2129, no. 1 (2021): 012016. http://dx.doi.org/10.1088/1742-6596/2129/1/012016.

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Abstract The application of flexible manipulators has increased in recent years especially in the fourth industrial revolution. It plays a significant role in a diverse range of fields, such as construction automation, environmental applications, space engineering and many more. Due to the lightweight, lower inertia and high flexibility of flexible manipulators, undesired vibration may occur and affect the precision of operation. Therefore, development of an accurate model of the flexible manipulator was presented prior to establishing active vibration control to suppress the vibration and inc

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Chu, Ha Thi, Thuy Chau Nguyen, Isabelle Godin, and Olivier Michel. "A Proposal to Differentiate ACO, Asthma and COPD in Vietnam." Journal of Personalized Medicine 13, no. 1 (2022): 78. http://dx.doi.org/10.3390/jpm13010078.

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Background: In low- and middle-income countries, such as Vietnam, the population is exposed to multiple risks, leading to frequent allergic asthma, COPD and their overlap (ACO). We aimed to differentiate asthma and COPD, so that recommended treatments can be applied. Methods: We hypothesized that during life, the cumulative exposure to noxious particles increases the relative prevalence of COPD, while due to immuno-senescence, the prevalence of allergic asthma decreases with age. Among 568 patients with chronic respiratory symptoms, five phenotypes were defined, based on responsiveness to a br

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Chen, Xi, Yukuan Dong, Xiaoshi Wang, and Qiushi Wang. "Optimization of an Urban Microgreen Space Distribution Based on the PS-ACO Algorithm: A Case Study of Shenyang, China." Land 13, no. 10 (2024): 1599. http://dx.doi.org/10.3390/land13101599.

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In this study, optimization of the microgreen space distribution through multistage regulation is investigated, with the goal of alleviating the imbalance between the supply and demand of green resources in the central urban area of Shenyang. An optimized evaluation model of green space supply and demand is employed to calculate the green space accessibility index at a 100-meter grid scale and identify different levels of green space resource supply and demand. Priority is given to supplementing resources for the elderly population by balancing the green space supply in vulnerable areas. Parti

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Koleade, Adetola, Jamie Farrell, Gerald Mugford, and Zhiwei Gao. "Prevalence and Risk Factors of ACO (Asthma-COPD Overlap) in Aboriginal People." Journal of Environmental and Public Health 2018 (November 21, 2018): 1–9. http://dx.doi.org/10.1155/2018/4657420.

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Background and Objective. Aboriginal peoples are at a higher risk of many chronic respiratory diseases compared to the general Canadian population. Patients with asthma-COPD overlap (ACO), a disease newly described in 2015, are associated with frequent exacerbations, rapid decline in lung function, poor quality of life, high mortality, and disproportionate utilization of health-care resources than patients with asthma and COPD alone. The objective was to investigate the prevalence and risk factors of ACO in Aboriginal peoples.Methods. Data from the 2012 Aboriginal Peoples Survey (APS) were use

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Fındık, Şaban, and Salih Ermiş. "Forecasting the Power Output of Photovoltaic Systems Using Ant Colony Optimization and Determining the Optimal Time Interval for Agricultural Use." Türk Tarım ve Doğa Bilimleri Dergisi 12, no. 3 (2025): 653–65. https://doi.org/10.30910/turkjans.1683035.

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The rapid increase in global energy demand, driven by industrialization, population growth, and technological advances, has emphasized the importance of the transition to renewable energy sources. In addition, renewable energy, especially photovoltaic (PV) systems, has become a widespread energy source in agricultural applications to meet the energy needs in agricultural irrigation. However, the efficiency of PV systems varies depending on meteorological and environmental factors such as solar radiation and temperature. Therefore, predictability of energy production is a critical requirement f

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Liew, Ching Kho, Shareduwan Mohd Kasihmuddin Mohd, Asyraf Mansor Mohd., and Sathasivam Saratha. "Logic mining in football matches." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 2 (2020): 1074–83. https://doi.org/10.11591/ijeecs.v17.i2.pp1074-1083.

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Sports results forecast has became increasingly popular among the fans nowadays. It made predicting the outcome of a sport’s match, a new and interesting challenge. This paper presented a logic mining technique to model the results (Win Draw / Lose) of the football matches played in English Premier League, Spanish La Liga and France Ligue 1. In this research, a method namely k satisfiability based reverse analysis method (kSATRA) hybridized with Ant Colony Optimization (ACO) was brought forward to obtain the logical relationship among the clubs in these leagues. The logical rule obtained

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Annu, Raj, and poriye Monika. "Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2475–81. https://doi.org/10.35940/ijeat.C5683.029320.

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In the advent of the cyber world, all know that cyber security is randomly used research area for researchers to secure host, network, and data because of increasingly complex attacks. In the advent of anomaly-based intrusion detection system, various techniques are applied to detect intrusion on system or network. This approach attains an extreme detection rate and accuracy but there may be overhead acquired to build and training them. The objective of this paper is to detect the intrusion of a system by proposing a Data mining technique which is based on supervised learning algorithm for tra

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Wang, Peng. "Optimization Algorithms of PERT Network Diagram in Software Project Management System." Applied Mechanics and Materials 321-324 (June 2013): 2056–59. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2056.

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The PERT network diagram optimization and the node layout optimization were studied in order to solve the difficulty of using manual methods to generate the PERT network chart in large-scale software project management. The new methods of optimization the node of PERT was proposed based on ant colony optimization algorithm (ACO) and genetic algorithms (GAs). The high adaptability and population optimization technology of GAs are discussed and also the ACO is introduced to realize the nodes layout global adjustment. In the end, calculation and simulation results show that the methods proposed c

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Chen, Peng, and Tian Tian. "Matching Community Sports Facilities With Ant Colony Algorithm in National Fitness." International Journal of Distributed Systems and Technologies 16, no. 1 (2025): 1–20. https://doi.org/10.4018/ijdst.369653.

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This study addresses the challenge of selecting optimal locations for urban sports facilities, leveraging the strengths of the ant colony optimization (ACO) algorithm. An enhanced ACO model is proposed, incorporating population density and distance to sports facilities as critical factors in the objective function. The model employs a unique pheromone updating strategy that reduces search time and improves solution quality. Two updates to the pheromone levels are performed, and the initial pheromone distribution is reset based on path distances. The effectiveness of the model is demonstrated t

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Wu, Frances M., Thomas G. Rundall, Stephen M. Shortell, and Joan R. Bloom. "Using health information technology to manage a patient population in accountable care organizations." Journal of Health Organization and Management 30, no. 4 (2016): 581–96. http://dx.doi.org/10.1108/jhom-01-2015-0003.

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Purpose – The purpose of this paper is to describe the current landscape of health information technology (HIT) in early accountable care organizations (ACOs), the different strategies ACOs are using to develop HIT-based capabilities, and how ACOs are using these capabilities within their care management processes to advance health outcomes for their patient population. Design/methodology/approach – Mixed methods study pairing data from a cross-sectional National Survey of ACOs with in-depth, semi-structured interviews with leaders from 11 ACOs (both completed in 2013). Findings – Early ACOs v

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Kho, Liew Ching, Mohd Shareduwan Mohd Kasihmuddin, Mohd Asyraf Mansor, and Saratha Sathasivam. "Logic mining in football matches." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (2020): 1074. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp1074-1083.

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Sports results forecast has became increasingly popular among the fans nowadays. It made predicting the outcome of a sport’s match, a new and interesting challenge. This paper presented a logic mining technique to model the results (Win Draw / Lose) of the football matches played in English Premier League, Spanish La Liga and France Ligue 1. In this research, a method namely <em>k</em> satisfiability based reverse analysis method (<em>k</em>SATRA) hybridized with Ant Colony Optimization (ACO) was brought forward to obtain the logical relationship among the clubs in thes

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Riabko, A. V., O. V. Zaika, R. P. Kukharchuk, T. A. Vakaliuk, and I. V. Hordiienko. "Algorithm of ant colony optimization (ACO) for 3D variation traveling salesman problem." Journal of Physics: Conference Series 2288, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2288/1/012001.

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Abstract The article discusses the solution of the spatial traveling salesman problem (TSP 3D variation) using Ant Colony Optimization (ACO). The traveling salesman problem considers n bridges and a matrix of pairwise distances between them. It is necessary to find such an order of visiting cities so that the total distance traveled was minimal, each city was visited exactly once and the salesman returned to the city from which he began his route. In the TSP 3D variation problem, each “city” has 3 coordinates x, y, z. The analysis of the main methods of solving, in particular, the metaheuristi

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Zhang, Jingrui, Zhenhong Xu, Houde Liu, Xiaojun Zhu, and Bin Lan. "An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario." Machines 13, no. 6 (2025): 474. https://doi.org/10.3390/machines13060474.

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This paper proposes a multi-map path planning approach for rescue robots to address the challenges posed by complex obstacle information, high uncertainty, and the difficulties in mine disaster scenarios. Based on multiple possible environmental maps, each with associated subjective probabilities derived from prior knowledge and expert estimations, a mathematical model for multi-map path planning in mine disaster rescue scenarios is developed. An improved hybrid algorithm combining ant colony optimization (ACO) and genetic algorithm (GA) is then proposed to solve the established model. In the

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Jabbar, Ayad ‎. Mohammed. "Controlling the Balance of Exploration and Exploitation in ACO Algorithm." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 26, no. 4 (2018): 1–9. http://dx.doi.org/10.29196/jub.v26i4.678.

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Ant colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant colony. The algorithm is a population-based solution employed in different optimization problems such as classification, image processing, clustering, and so on. This paper sheds the light on the side of improving the results of traveling salesman problem produced by the algorithm. The key success that produces the valuable results is due to the two important components of exploration and exploitation. Balancing both components is the foundation of controlling search within the ACO. This paper p

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Aliyu, Muhammad, Murali M, Abdulsalam Y. Gital, and Souley Boukari. "Efficient Metaheuristic Population-Based and Deterministic Algorithm for Resource Provisioning Using Ant Colony Optimization and Spanning Tree." International Journal of Cloud Applications and Computing 10, no. 2 (2020): 1–21. http://dx.doi.org/10.4018/ijcac.2020040101.

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Resource provisioning is the core function of cloud computing which is faced with serious challenges as demand grows. Several strategies of cloud computing resources optimization were considered by many researchers. Optimization algorithms used are still under reckoning and modification so as to enhance their potentials. As such, a dynamic scheme that can combine several algorithms' characteristics is required. Quite a number of optimization techniques have been reassessed based on metaheuristics and deterministic to map out with the challenges of resource provisioning in the Cloud. This resea

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Jafar, Abbas, and Myungho Lee. "Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques." Applied Sciences 15, no. 6 (2025): 2960. https://doi.org/10.3390/app15062960.

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Kidney disease is a global health concern, impacting a substantial part of the overall population and contributing to high morbidity and mortality rates. The initially diagnosed phases of kidney disease are often present without noticeable indications, leading to delayed diagnosis and treatment. Therefore, early detection is crucial to reducing complications and improving the lives of those impacted. However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms’ “black-box” nature, which adversely affect their interpretability

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Acherjee, Bappa, Debanjan Maity, and Arunanshu S. Kuar. "Ultrasonic Machining Process Optimization by Cuckoo Search and Chicken Swarm Optimization Algorithms." International Journal of Applied Metaheuristic Computing 11, no. 2 (2020): 1–26. http://dx.doi.org/10.4018/ijamc.2020040101.

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The ultrasonic machining (USM) process has been analyzed in the present study to obtain the desired process responses by optimizing machining parameters using cuckoo search (CS) and chicken swarm optimization (CSO), two powerful nature-inspired, population and swarm-intelligence-based metaheuristic algorithms. The CS and CSO algorithms have been compared with other non-conventional optimization techniques in terms of optimal results, convergence, accuracy, and computational time. It is found that CS and CSO algorithms predict superior single and multi-objective optimization results than gravit

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Xiao, Jinzhuang, Xuele Yu, Keke Sun, Zhen Zhou, and Gang Zhou. "Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA." Mathematical Biosciences and Engineering 19, no. 12 (2022): 12532–57. http://dx.doi.org/10.3934/mbe.2022585.

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<abstract> <p>With their intelligence, flexibility, and other characteristics, automated guided vehicles (AGVs) have been popularized and promoted in traditional industrial markets and service industry markets. Compared with traditional transportation methods, AGVs can effectively reduce costs and improve the efficiency of problem solving in various application developments, but they also lead to serious path-planning problems. Especially in large-scale and complex map environments, it is difficult for a single algorithm to plan high-quality moving paths for AGVs, and the algorithm

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Kumar, Prasanna, Mervin Herbert, and Srikanth Rao. "Population Based Metaheuristic Algorithm Approach for Analysis of Multi-Item Multi-Period Procurement Lot Sizing Problem." Advances in Operations Research 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/3601217.

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This research study focuses on the optimization of multi-item multi-period procurement lot sizing problem for inventory management. Mathematical model is developed which considers different practical constraints like storage space and budget. The aim is to find optimum order quantities of the product so that total cost of inventory is minimized. The NP-hard mathematical model is solved by adopting a novel ant colony optimization approach. Due to lack of benchmark method specified in the literature to assess the performance of the above approach, another metaheuristic based program of genetic a

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