The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified ...The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.展开更多
Ants rank among the most ecologically dominant and evolutionarily remarkable insects on the planet,capturing the imagination of both curious children and thoughtful scholars alike.Aristotle,impressed by their division...Ants rank among the most ecologically dominant and evolutionarily remarkable insects on the planet,capturing the imagination of both curious children and thoughtful scholars alike.Aristotle,impressed by their division of labor and cooperative behavior,described them as“political animals”.In Aesop’s Fables,they are celebrated for their foresight and diligence in preparing for hardship.Traditional Chinese narratives similarly portray ants as modest creatures that,through collective effort,achieve extraordinary power and influence.展开更多
The ability of queens and males of most ant species to disperse by flight has fundamentally contributed to the group’s evolutionary and ecological success and is a determining factor to take into account for biogeogr...The ability of queens and males of most ant species to disperse by flight has fundamentally contributed to the group’s evolutionary and ecological success and is a determining factor to take into account for biogeographic studies(Wagner and Liebherr 1992;Peeters and Ito 2001;Helms 2018).展开更多
Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN ...Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN planning remains a challenge,especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently.This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization(ACO)algorithm into the refinement process.The Ant System algorithm,inspired by the foraging behavior of ants,is well-suited for addressing optimization problems by efficiently exploring solution spaces.By incorporating ACO into the refinement phase of HTN planning,the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation.This paper involves the development of a hybrid strategy called ACO-HTN,which combines HTN planning with ACO-based plan selection.This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions.To evaluate the effectiveness of the proposed technique,this paper conducts empirical experiments on various domains and benchmark datasets.Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning,outperforming traditional methods in terms of solution quality and computational performance.展开更多
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
This paper reviews the historical application,primary species,and efficacy of predatory ants in pest management,and systematically elucidates their core mechanisms,including direct predation,non-lethal deterrence,and ...This paper reviews the historical application,primary species,and efficacy of predatory ants in pest management,and systematically elucidates their core mechanisms,including direct predation,non-lethal deterrence,and ecological regulation,as well as two application models:forest land introduction and farmland conservation.Meanwhile,the negative impacts on other arthropods,population diffusion,and management challenges in current applications are analyzed.Future research directions,such as precise assessment and risk control,integrated pest management(IPM)strategies integrating multiple technologies,and applications of molecular ecology,are clarified.The aim is to provide a reference for the in-depth research,promotion,and application of this type of natural enemy insect in sustainable IPM of agriculture and forestry.展开更多
Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human p...Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.展开更多
文摘The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.
基金supported by the National Natural Science Foundation of China(32388102 to G.Z.,32370668 to W.L.)Yunnan Provincial Science and Technology Department,Yunnan Fundamental Research Projects(202201AT070129 and 202401BC070017 to W.L.)。
文摘Ants rank among the most ecologically dominant and evolutionarily remarkable insects on the planet,capturing the imagination of both curious children and thoughtful scholars alike.Aristotle,impressed by their division of labor and cooperative behavior,described them as“political animals”.In Aesop’s Fables,they are celebrated for their foresight and diligence in preparing for hardship.Traditional Chinese narratives similarly portray ants as modest creatures that,through collective effort,achieve extraordinary power and influence.
基金funded by the“Departments of Excellence”program of the Italian Ministry for University and Research(MIUR,2018-2022 and MUR,2023-2027).
文摘The ability of queens and males of most ant species to disperse by flight has fundamentally contributed to the group’s evolutionary and ecological success and is a determining factor to take into account for biogeographic studies(Wagner and Liebherr 1992;Peeters and Ito 2001;Helms 2018).
基金supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-15-2022-1137supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R323),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN planning remains a challenge,especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently.This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization(ACO)algorithm into the refinement process.The Ant System algorithm,inspired by the foraging behavior of ants,is well-suited for addressing optimization problems by efficiently exploring solution spaces.By incorporating ACO into the refinement phase of HTN planning,the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation.This paper involves the development of a hybrid strategy called ACO-HTN,which combines HTN planning with ACO-based plan selection.This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions.To evaluate the effectiveness of the proposed technique,this paper conducts empirical experiments on various domains and benchmark datasets.Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning,outperforming traditional methods in terms of solution quality and computational performance.
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
基金Supported by National Innovation and Entrepreneurship Training Program for College Students(202410580010)Key Projects of the Second Round Project of High-quality Development in Hundred Counties,Thousands Towns and Ten Thousand Villages for Rural Science and Technology Special Commissioners Dispatched by the Guangdong Provincial Department of Science and Technology(KTP20240684)+1 种基金the 2025 Science and Technology Innovation Guidance Project of Zhaoqing(241223100090425)Doctoral Scientific Research Initiation Fund Project of Zhaoqing University(611/230009).
文摘This paper reviews the historical application,primary species,and efficacy of predatory ants in pest management,and systematically elucidates their core mechanisms,including direct predation,non-lethal deterrence,and ecological regulation,as well as two application models:forest land introduction and farmland conservation.Meanwhile,the negative impacts on other arthropods,population diffusion,and management challenges in current applications are analyzed.Future research directions,such as precise assessment and risk control,integrated pest management(IPM)strategies integrating multiple technologies,and applications of molecular ecology,are clarified.The aim is to provide a reference for the in-depth research,promotion,and application of this type of natural enemy insect in sustainable IPM of agriculture and forestry.
基金supported in part by the National Natural Science Foundation of China(Grant No.62273107,61702127,62272113)Science and Technology Program of Guangzhou(Grant No.201804010461).
文摘Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.
