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Journal articles on the topic 'Ant colony framework'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Luo, Yueguo, Ping Guo, and Mingzhe Zhang. "A Framework of Ant Colony P System." IEEE Access 7 (2019): 157655–66. http://dx.doi.org/10.1109/access.2019.2949952.

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Mavrovouniotis, Michalis, Shengxiang Yang, Mien Van, Changhe Li, and Marios Polycarpou. "Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem." IEEE Computational Intelligence Magazine 15, no. 1 (2020): 52–63. https://doi.org/10.1109/MCI.2019.2954644.

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Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systema

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Blum, C., and M. Dorigo. "The Hyper-Cube Framework for Ant Colony Optimization." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 2 (2004): 1161–72. http://dx.doi.org/10.1109/tsmcb.2003.821450.

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Gavidia-Calderon, Carlos, and César Beltrán Castañon. "Isula: A java framework for ant colony algorithms." SoftwareX 11 (January 2020): 100400. http://dx.doi.org/10.1016/j.softx.2020.100400.

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González, Patricia, Roberto R. Osorio, Xoan C. Pardo, Julio R. Banga, and Ramón Doallo. "An efficient ant colony optimization framework for HPC environments." Applied Soft Computing 114 (January 2022): 108058. http://dx.doi.org/10.1016/j.asoc.2021.108058.

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Perumal, Boominathan, and Aramudhan Murugaiyan. "Virtual Machine Placement Using Hypercube Ant Colony Optimization Framework." International Journal of Intelligent Engineering and Systems 10, no. 5 (2017): 77–86. http://dx.doi.org/10.22266/ijies2017.1031.09.

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Rajesh, J., Kapil Gupta, Hari Shankar Kusumakar, V. K. Jayaraman, and B. D. Kulkarni. "Dynamic optimization of chemical processes using ant colony framework." Computers & Chemistry 25, no. 6 (2001): 583–95. http://dx.doi.org/10.1016/s0097-8485(01)00081-x.

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González, Patricia, Roberto R. Osorio, Xoán C. Pardo, Julio R. Banga, and Ramón Doallo. "An Efficient Ant Colony Optimization Framework for HPC Environments." Applied Soft Computing 114 (November 18, 2021): 108058. https://doi.org/10.5281/zenodo.11387453.

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Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by

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González, Patricia, Roberto Osorio, Xoan Carlos Pardo, Julio R. Banga, and Ramón Doallo. "An efficient ant colony optimization framework for HPC environments." Applied Soft Computing 114 (January 1, 2022): 14. https://doi.org/10.1016/j.asoc.2021.108058.

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Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by

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Wu, Min-Thai, Tzung-Pei Hong, and Chung-Nan Lee. "A continuous ant colony system framework for fuzzy data mining." Soft Computing 16, no. 12 (2012): 2071–82. http://dx.doi.org/10.1007/s00500-012-0878-5.

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Cheng, Chun Hung, Angappa Gunasekaran, and Kwan Ho Woo. "A bi-tour ant colony optimisation framework for vertical partitions." International Journal of Industrial and Systems Engineering 7, no. 3 (2011): 341. http://dx.doi.org/10.1504/ijise.2011.038983.

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Et. al., G. Joel Sunny Deol,. "Hadoop Job Scheduling Using Improvised Ant Colony Optimization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 3417–24. http://dx.doi.org/10.17762/turcomat.v12i2.2403.

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Hadoop Distributed File System is used for storage along with a programming framework MapReduce for processing large datasets allowing parallel processing. The process of handling such complex and vast data and maintaining the performance parameters up to certain level is a difficult task. Hence, an improvised mechanism is proposed here that will enhance the job scheduling capabilities of Hadoop and optimize allocation and utilization of resources. Significantly, an aggregator node is added to the default HDFS framework architecture to improve the performance of Hadoop Name node. In this paper

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Ahmid, Ali, Thien-My Dao, and Ngan Van Le. "Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems." Algorithms 14, no. 10 (2021): 286. http://dx.doi.org/10.3390/a14100286.

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Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the de

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Kim, Young-Min, Eun-Jung Lee, and Hong-Shik Park. "Ant Colony Optimization Based Self-Organizing QoS Framework in IP Networks." IEEE Communications Letters 14, no. 11 (2010): 1074–76. http://dx.doi.org/10.1109/lcomm.2010.091710.101382.

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Jayaraman, V. K., B. D. Kulkarni, Sachin Karale, and Prakash Shelokar. "Ant colony framework for optimal design and scheduling of batch plants." Computers & Chemical Engineering 24, no. 8 (2000): 1901–12. http://dx.doi.org/10.1016/s0098-1354(00)00592-5.

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Rateb, Roqia, Amal Shorman, Ahmad Sami Al-Shamayleh, Areej Alshorman, and Laith H. Baniata. "A Designing Framework for Ant Colony Algorithm for Managing Cognitive Insomnia." Journal of Computer Science 21, no. 7 (2025): 1554–64. https://doi.org/10.3844/jcssp.2025.1554.1564.

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Deng, Lijuan, Long Wan, and Jian Guo. "Research on Security Anomaly Detection for Big Data Platforms Based on Quantum Optimization Clustering." Mathematical Problems in Engineering 2022 (August 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/4805035.

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Due to the explosive growth of data in the Internet, more and more applications are being deployed on Big Data platforms. However, as the scale of data continues to increase, the probability of anomalies in the platform is also increasing. However, traditional anomaly detection techniques cannot effectively handle the massive amount of historical data and can hardly meet the security requirements of big data platforms. In order to solve the above problems, this paper proposes a security anomaly detection method for big data platforms based on quantum optimization clustering. Firstly, a framewo

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18

Eido, Warveen Merza, and Ibrahim Mahmood Ibrahim. "Ant Colony Optimization (ACO) for Traveling Salesman Problem: A Review." Asian Journal of Research in Computer Science 18, no. 2 (2025): 20–45. https://doi.org/10.9734/ajrcos/2025/v18i2559.

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The traveling salesman problem (TSP) is a fundamental combinatorial optimization problem with applications in resource management, logistics, and communications. In order to address TSP and its differences, this paper discusses developments in Ant Colony Optimization (ACO), a biologically inspired algorithm. Inspired by the foraging activity of ants, ACO's decentralized and recursive methodology has proven successful in solving difficult routing problems. ACO's scalability, convergence speed, and solution quality have been greatly enhanced over time through innovations including hybridization

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Tazeen, Nazia, and K. Sandhya Rani. "A Novel Ant Colony Based DBN Framework to Analyze the Drug Reviews." International Journal of Intelligent Systems and Applications 13, no. 6 (2021): 25–39. http://dx.doi.org/10.5815/ijisa.2021.06.03.

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Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks. Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Theref

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Oh, Eunseo, and Hyunsoo Lee. "Effective Routing Generation Framework using Multi-directional and Parallel Ant Colony Optimization." Journal of Korean Institute of Intelligent Systems 28, no. 5 (2018): 523–30. http://dx.doi.org/10.5391/jkiis.2018.28.5.523.

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21

Baioletti, Marco, Alfredo Milani, Valentina Poggioni, and Fabio Rossi. "Ant Search Strategies For Planning Optimization." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 334–37. http://dx.doi.org/10.1609/icaps.v19i1.13394.

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In this paper a planning framework based on Ant Colony Optimization techniques is presented. It is well known that finding optimal solutions to planning problems is a very hard computational problem. Stochastic methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. We propose several approaches based both on backward and forward search over the state space, using several heuristics and testing different pheromone models in order to solve sequential optimization planning problems.

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22

Li, Linna, Junyi Lu, Han Gao, and Dan Li. "The Sustainable Allocation of Earth-Rock via Division and Cooperation Ant Colony Optimization Combined with the Firefly Algorithm." Symmetry 17, no. 7 (2025): 1029. https://doi.org/10.3390/sym17071029.

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Optimized earth-rock allocation is key in the construction of large-scale navigation channel projects. This paper analyzes the characteristics of a large-scale navigation channel project and establishes an earth-rock allocation system in phases and categories without a transit field. Based on the physical characteristics of the earthwork and stonework used to design a differentiated transport strategy, a synergistic optimization model is built with economic and ecological benefits. As a solution, this paper proposes a sustainable earth-rock allocation optimization method that integrates the im

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Cavallaro, Claudia, Carolina Crespi, Vincenzo Cutello, Mario Pavone, and Francesco Zito. "Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem." Algorithms 17, no. 2 (2024): 63. http://dx.doi.org/10.3390/a17020063.

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This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of a colony of memory-enhanced ants (ME-ANTS) which, starting from a given position, must collaboratively discover the optimal path to the exit point within a grid network. The colony can be divided into groups of different sizes and its objectives are maximizing the number of ants that exit the grid

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Chai, Xiao Long. "A Planning Algorithm with Temporal Constraints in the Working Procedures." Advanced Materials Research 760-762 (September 2013): 1786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1786.

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Temporal planning is a broad research area in automated planning. In most real-world applications such as the problems of working procedures planning, many real planning problems often require the planning goals can be satisfied in shorter time and some temporal constraints should be satisfied in the planning answer. In this paper, the ant colony algorithm under the temporal constraints is presented which with the heuristic control rules and the evaluation tactics in the framework of the ant colony planning algorithm. The searching way of the algorithm has the character of global and parallel.

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25

Hu, Xiao-Min, and Jun Zhang. "Minimum Cost Multicast Routing Using Ant Colony Optimization Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/432686.

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Multicast routing (MR) is a technology for delivering network data from some source node(s) to a group of destination nodes. The objective of the minimum cost MR (MCMR) problem is to find an optimal multicast tree with the minimum cost for MR. This problem is NP complete. In order to tackle the problem, this paper proposes a novel algorithm termed the minimum cost multicast routing ant colony optimization (MCMRACO). Based on the ant colony optimization (ACO) framework, the artificial ants in the proposed algorithm use a probabilistic greedy realization of Prim’s algorithm to construct multicas

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Obeng, Asare Y., and Samuel K. Opoku. "Complementary Architecture of E-Learning Path Personalization and Optimization." European Journal of Electrical Engineering and Computer Science 7, no. 3 (2023): 41–48. http://dx.doi.org/10.24018/ejece.2023.7.3.533.

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Using computing algorithms to generate personalized learning resources to provide the needs and improve the capabilities, preferences, and academic performance of diverse learners is creating preferred learning environments. As more learning resources, strategies and techniques are frequently added to these e-learning systems, input data to personalize the learning path has been growing exponentially making swift responses to learner’s requests difficult. This study proposed a complementary learning path personalization architecture using ant colony (ACO) with nearest neighbour technique and g

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JIN, DI, DAYOU LIU, BO YANG, JIE LIU, and DONGXIAO HE. "ANT COLONY OPTIMIZATION WITH A NEW RANDOM WALK MODEL FOR COMMUNITY DETECTION IN COMPLEX NETWORKS." Advances in Complex Systems 14, no. 05 (2011): 795–815. http://dx.doi.org/10.1142/s0219525911003219.

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Detecting communities from complex networks has recently triggered great interest. Aiming at this problem, a new ant colony optimization strategy building on the Markov random walks theory, which is named as MACO, is proposed in this paper. The framework of ant colony optimization is taken as the basic framework in this algorithm. In each iteration, a Markov random walk model is employed as heuristic rule; all of the ants' local solutions are aggregated to a global one through an idea of clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progr

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Mohan, Anuraj, and Remya G. "A Parallel Implementation of Ant Colony Optimization for TSP based on MapReduce Framework." International Journal of Computer Applications 88, no. 8 (2014): 9–12. http://dx.doi.org/10.5120/15371-3900.

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Kang, Li, Wei-xin Xie, and Jing-xiong Huang. "Non-linear Multi-target Tracking Based on SIS Framework and Ant Colony Optimization." Journal of Electronics & Information Technology 30, no. 9 (2011): 2148–51. http://dx.doi.org/10.3724/sp.j.1146.2007.00688.

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30

Carpaneto, Enrico, and Gianfranco Chicco. "Distribution system minimum loss reconfiguration in the Hyper-Cube Ant Colony Optimization framework." Electric Power Systems Research 78, no. 12 (2008): 2037–45. http://dx.doi.org/10.1016/j.epsr.2008.06.009.

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Nguyen, Duc Cong Hiep, Holger R. Maier, Graeme C. Dandy, and James C. Ascough. "Framework for computationally efficient optimal crop and water allocation using ant colony optimization." Environmental Modelling & Software 76 (February 2016): 37–53. http://dx.doi.org/10.1016/j.envsoft.2015.11.003.

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Rao, A. Lakshmipathi. "DYNAMIC FIREWALL CONFIGURATION FOR VIRTUAL NETWORKS: A COMPREHENSIVE AUTOMATION FRAMEWORK." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35358.

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In recent years, segment routing has emerged as a revolutionary traffic engineering architecture. Segment routing, however, results in control overheads since more packet headers need to be included. When segment headers get excessively long, the overheads can significantly lower the forwarding efficiency for a big network. We suggest the intelligent routing scheme for traffic engineering (IRTE), which can provide load balancing with minimal control overheads, in order to meet the better of two goals. We first frame the problem as a mapping problem that maps various flows to important diversio

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Sun, Haodong, Yanyan Chen, Jianming Ma, Yang Wang, Xiaoming Liu, and Jiachen Wang. "Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm." Journal of Advanced Transportation 2022 (October 28, 2022): 1–14. http://dx.doi.org/10.1155/2022/6386119.

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With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists’ travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists’ travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from

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M. Rupasri. "A Cluster Based Energy efficient Q-Ant Colony Optimization(CEQACO) framework for cloud computing environment." Journal of Information Systems Engineering and Management 10, no. 41s (2025): 262–76. https://doi.org/10.52783/jisem.v10i41s.7817.

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In cloud computing, resource allocation and cloudlet scheduling are fundamental issues when dealing with a medium to large number of tasks. To meet consumer expectations and achieve optimal performance, multiple cloudlets need to be executed simultaneously using available resources, while minimizing makespan and effectively balancing the load. Despite ongoing developments in cloud computing, this technology faces numerous challenges, one of which is task scheduling. Task scheduling involves allocating users' tasks to virtual machines (VMs) to minimize turnaround time and improve resource utili

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Dahan, Fadl, Khalil El Hindi, and Ahmed Ghoneim. "An Adapted Ant-Inspired Algorithm for Enhancing Web Service Composition." International Journal on Semantic Web and Information Systems 13, no. 4 (2017): 181–97. http://dx.doi.org/10.4018/ijswis.2017100109.

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Web Service Composition (WSC) provides a flexible framework for integrating independent web services to satisfy complex user requirements. WSC aims to choose the best web service from a set of candidates. The candidates have the same functionality and different non-functional criteria such as Quality of Service (QoS). In this work, the authors propose an ant-inspired algorithm for such problem. They named it Flying Ant Colony Optimization (FACO). Flying ants inject pheromone not only on the nodes on their paths but also on neighboring nodes increasing their chances of being explored in future

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Alyouzbaki, Yasameen A. Ghani, and Muaayed F. Al-Rawi. "Novel load balancing approach based on ant colony optimization technique in cloud computing." Bulletin of Electrical Engineering and Informatics 10, no. 4 (2021): 2320–26. http://dx.doi.org/10.11591/eei.v10i4.2947.

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The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consum

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Yasameen, A. Ghani Alyouzbaki, and F. Al-Rawi Muaayed. "Novel load balancing approach based on ant colony optimization technique in cloud computing." Bulletin of Electrical Engineering and Informatics 10, no. 4 (2021): pp. 2320~2326. https://doi.org/10.11591/eei.v10i4.2947.

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The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consum

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Liu, Qiliang, Sancheng Zhu, Meihua Chen, and Wenkai Liu. "Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization." Applied Sciences 12, no. 15 (2022): 7608. http://dx.doi.org/10.3390/app12157608.

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Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically significant dynamic communities in vehicle movements is still challenging. To overcome this challenge, we developed an evolutionary ant colony optimization (EACO) method for detecting dynamic communities in vehicle movements. Firstly, a weighted, spatially embedded graph was constructed at each time snapshot. Then, an ant-colony-optimization-based spati

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Rajan, A. Albert, S. Thomas George, and R. Catherine Joy. "An evolutionary framework on ADHD diagnosis based on graph theory and ant colony optimisation." International Journal of Computer Aided Engineering and Technology 15, no. 2/3 (2021): 218. http://dx.doi.org/10.1504/ijcaet.2021.10039280.

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Joy, R. Catherine, S. Thomas George, and A. Albert Rajan. "An evolutionary framework on ADHD diagnosis based on graph theory and ant colony optimisation." International Journal of Computer Aided Engineering and Technology 15, no. 2/3 (2021): 218. http://dx.doi.org/10.1504/ijcaet.2021.117132.

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Chen, Chii-Jen, You-Wei Wang, Wei-Chih Shen, Chih-Yi Chen, and Wen-Pinn Fang. "The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images." Algorithms 7, no. 4 (2014): 635–49. http://dx.doi.org/10.3390/a7040635.

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Hsin, Hsien-Kai, En-Jui Chang, Kuan-Yu Su, and An-Yeu Wu. "Ant Colony Optimization-Based Adaptive Network-on-Chip Routing Framework Using Network Information Region." IEEE Transactions on Computers 64, no. 8 (2015): 2119–31. http://dx.doi.org/10.1109/tc.2014.2366768.

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Sayajirao Bahendwar, Yogesh, and Rajesh H. Talwekar. "A Hybrid Framework for Segmentation of MR Medical Images Using Adjusted Ant Colony Optimization." HELIX 9, no. 3 (2019): 4985–91. http://dx.doi.org/10.29042/2019-4985-4991.

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Palak, Palak, and Preeti Gulia. "Ant Colony Optimization Based Test Case Selection for Component Based Software." International Journal of Engineering & Technology 7, no. 4 (2018): 2743. http://dx.doi.org/10.14419/ijet.v7i4.17565.

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Reusability is one of the prime aspects of high quality software. Based on the concept of reusing the previous effort, Component based software engineering is a widely evolving software development paradigm that sets new challenges for testing team. The third party components need to be selected and assembled in development framework. Components interact with each other for various services and the interface between them can prove as the point of failure. As exhaustive testing of all interaction sequences is not possible, there is need for automated test case reduction and prioritization techn

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Khan, Dost Muhammad, Talal Aslam, Nadeem Akhtar, Salman Qadri, Imran Mujaddid Rabbani, and Dr Muhammad Aslam. "Black Hole Attack Prevention in Mobile Ad-hoc Network (MANET) using Ant Colony Optimization Technique." Information Technology And Control 49, no. 3 (2020): 308–19. http://dx.doi.org/10.5755/j01.itc.49.3.25265.

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The Mobile ad-hoc networks are auto configured systems where hubs can travel towards any direction. It doesn’t rely on any other frame to build the framework. Because of its versatility, the black hole is a real security issue to be settled. It occurs once a malignant hub referred to as black hole goes into the framework. Black hole center point exhibits its fake lead among the methodology after all disclosure. Currently, varied strategies are made arrangements for Mobile ad-hoc networks. Regardless, at interims seeing Black hole or fake hubs, the entire Mobile ad-hoc frameworks are exposed to

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Gao, Peng, Yue Jin Tan, Ju Fang Li, and Ren Jie He. "An Ant Colony Algorithm for Remote Satellite and Ground Integration Scheduling Problem in Parallel Environment." Advanced Materials Research 791-793 (September 2013): 1341–46. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1341.

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The algorithms for solving the remote satellite scheduling problem are less effective usually in single computing environment. This paper designed a framework of ant colony algorithm for remote satellite and ground integration scheduling problem in the parallel environment, and given the detail of key steps in the algorithm. Experiments are show at the end of this paper to prove effective and validation.

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Zhang, Zhenpeng, Pengyu Li, Shanglei Chai, Yukang Cui, and Yibin Tian. "DGA-ACO: Enhanced Dynamic Genetic Algorithm—Ant Colony Optimization Path Planning for Agribots." Agriculture 15, no. 12 (2025): 1321. https://doi.org/10.3390/agriculture15121321.

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Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise operational safety, (2) premature convergence to local optima, and (3) excessive energy consumption due to suboptimal trajectories. To overcome these challenges, this study proposes an enhanced Dynamic Genetic Algorithm—Ant Colony Optimization (DG

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Ben, Bella S. Tawfik, and M.A. Elgazza Mohamed. "Integer Linear Programming with Ant-Colony Optimized Algorithm to Extend MANET Lifetime." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 4 (2020): 277–81. https://doi.org/10.35940/ijrte.D4709.119420.

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Network which is randomly sorted out remotely and organization that conveys without framework and experiencing low force battery is called Mobile Ad-hoc Network (MANET). Oneof the main objectives isto get thebest route from source to destination to minimize the node energy consumption. Linear Programming (Integer Linear Programming) and nature inspiration technique (Ant Colony Optimization) are two models that enhance energy consumption.The proposed models in our work are considered extension of our previous works with modified version and different measures [13]. We introduced a modified vers

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Balraj, E., and T. Abirami. "Performance Improvement of Multibiometric Authentication System Using Score Level Fusion with Ant Colony Optimization." Wireless Communications and Mobile Computing 2022 (December 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/4145785.

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Abstract:

Biometric systems are widely used by many organisations to protect the data from anomaly users. Unimodal biometric systems have many problems like noisy data, nonversatility, and nonuniversality. To avoid these problems, multibiometric system is the most suitable approach where we can integrate more than two individual modalities. Our proposed framework is utilised to minimize the rate of error while working on the exhibition by utilising the methodology of Ant Colony Optimization in view of Score Level Fusion strategies. The proposed work will extract the highlights from two distinct modaliti

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Ali, Sajjad, Imran Khan, Sadaqat Jan, and Ghulam Hafeez. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid." Energies 14, no. 8 (2021): 2201. http://dx.doi.org/10.3390/en14082201.

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Abstract:

Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost

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