ACO Algorithm Research Articles

The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China.

The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ETO prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that Tmax, n and Ra can be used as the input combination of ETO model construction, and Tmax is the primary factor affecting ETO. ETO model constructed by BP algorithm has good goodness of fit with the ETO calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R2, MAE, NSE, GPI ranging 0.200-0.377, 0.932-0.984, 0.140-0.261, 0.920-0.984, 1.472-2.000, GPI ranking is 1-23. Therefore, the input combination (Tmax, n and Ra) and CSO-BP model are recommended as a simplified model for ETO prediction in Southwest China.

Research on Complex Multiconstraint Path Planning Based on ACA Hybrid Algorithm

Aiming at the multiconstraint complex problem of path planning, a hybrid algorithm with fast convergence and fast obstacle avoidance is proposed. According to the actual aircraft, a multiconstrained path cost model with seven components is constructed and defined the parameters and penalty factors of each part. To improve the sensitivity of each part of the trace planning cost function, according to the inconsistency of the cost index unit and measurement range, the expert scoring method is used to configure and normalize the index with weight. Make full advantage of the ACO algorithm and the artificial potential field algorithm to form the ACA hybrid algorithm, a pheromone heuristic function improvement method is proposed, and a control factor is introduced. The proposed model and planning algorithm are simulated based on the MATLAB platform environment, and it is shown by simulations that the hybrid algorithm has obvious advantages in terms of motion trajectory smoothness, number of iterations, and minimum path cost .It well solved the practical application problem of fast path planning convergence and fast avoiding obstacles in the complex environment.

A Recursive Ant Colony Optimization Algorithm for Energy Consumption in Cloud Computing

The Cloud Computing (CC) has vast amount of data centers that consists of many computing nodes and consumes a huge amount of electrical energy. Hence the researchers found that the high service-level agreements (SLAs) violations and Energy Consumption (EC) are the major challenging issues in CC. The various traditional approaches reduced the EC, but ignored the SLA violation during the selection of Virtual Machine (VM) from overloaded hosts. In order to effectively deal with these issues, this paper proposed the Recursive Ant Colony Optimization (RACO) in 2 different workloads. The main aim of the RACO is to minimize the high EC and SLA violations by the movement of best ant, which is random and identified the distance between their movements. The algorithm consists of 3 major steps includes tracking and updating the pheromone and finally city selection. The proposed method simulated on Cloud Sim to validate the efficiency and stability of the proposed RACO and their performance compared to that of other existing techniques. The results showed that RACO reduced the EC by 40 - 42 % (approx.) which is less than the traditional ACO algorithm in Planet Lab data.
 HIGHLIGHTS
 
 Reduced EC and SLA Violation of Data Center are done via RACO
 VM Allocations are done by IQR and utilises micro, small, medium and extra-large instances
 Pheromone Tracking, updating and city selection are the endeavours of RACO
 RACO reduced EC by 40 - 42 % and SLA Violation

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

With the development of the Internet, various forms of learning resources continue to flood the public, especially the promotion of video learning plat-forms. More and more people use their spare time to learn what they need. However, there is a general lack of intelligence in online learning platforms at present, which greatly reduced the utilization of online learning platforms and their educational advantages. The innovation of this paper is proposing and explaining that with the integration of big data, online education and ar-tificial intelligence, the contradiction of online education has turned into one between the lack of intelligence in online education and the demand of users. To solve this contradiction, this paper researches from the perspective of an algorithm. Course recommendation is the core algorithm of online ed-ucation. However, the current recommendation algorithm based on collabo-rative filtering has the disadvantages of cold start and useless recommenda-tion content. In this paper, Apriori and ACO algorithms in artificial intelli-gence are studied, and the proposal of an algorithmic framework named Posi-tion-Apriori-ACO brings forth new ideas in solving online education prob-lems. The Position-Apriori-ACO algorithm can effectively carry out course recommendation and learning path planning, and also provides a research di-rection for the intelligent development of online education.

Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method.

Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm.

Rescheduling of high-speed trains: a reinforcement learning approach

With the construction of high-speed railway networks in China, the traffic density is constantly increasing, and the complexity and difficulty of train rescheduling sharply increase when trains are delayed due to emergencies. How to dynamically adjust the train group operation to reduce the delay and improve the punctuality rate is the core of operation adjustments. In this paper, a model-free reinforcement learning method is proposed for the operation adjustment of high-speed trains in emergency situations. Firstly, the operation adjustment of multiple trains in multiple stations and blocks is modeled as a sequential multi-stage decision-making process of constrained resource occupation and allocation, and a dynamic spatio-temporal topological matrix is proposed to model the stations and blocks. In view of the strong spatio-temporal correlation of high-speed railway trains, a reinforcement learning state space, action space and reward function containing spatio-temporal distribution information such as vehicle positions and network resources are proposed for the first time, and an effective reward feedback mechanism is constructed. Then, aiming at the difficulty of huge search space in high-speed railway operation systems, this paper proposes an adaptive generation method of heuristic action subspace. This method uses some explicit static constraints to construct heuristic rules to reduce the search space, which can effectively reduce the trial-and-error times of model-free reinforcement learning, improve the efficiency of solution, and retain the advantages of model-free generality. Finally, the simulation analysis of the case based on the actual data of Beijing-Guangzhou high-speed railway shows that the proposed algorithm can converge well and significantly reduce the delay propagation within the train group under multiple delays caused by the high wind speed limit in different space and time ranges. Compared with MILP, ACO, and FCFS algorithms, the proposed method can reduce the average delay time of the train group by 2%–20%.

Cloud Computing Load Balancing Mechanism Taking into Account Load Balancing Ant Colony Optimization Algorithm.

The networking scale and traffic have exploded. At the same time, the rapid development of virtualization and cloud computing technologies not only poses a considerable challenge to the endurance of the network, but also causes more and more problems to the traditional network architecture with IP as the core. Cloud computing is a supercomputing model based on the Internet. With the rapid growth of network access and data traffic, the processing power and computing intensity will also increase, and a single server cannot afford the increase in business. In order to reduce network pressure and improve computing efficiency, load balancing for network computing is particularly important. This paper uses ant colony algorithm to design cloud computing load balance. The ant colony algorithm runs in the controller. According to the real-time network load situation provided by the controller, it calculates the link with the smallest load and provides a dynamic data stream forwarding strategy. The result of the experiments shows that the load-balanced ACO optimized technique can significantly provide an improved computational response. In the ACO algorithm, the average response time is about 30% lower than that in other algorithms. This shows that the use of the ant colony algorithm achieves a good optimization effect.

Multiparameter Adaptive Optimisation of MSE Osseous Expansion Position

The accuracy of the implantation position of the MSE osseous expansion anchorage implant is a key issue in the treatment of osseous expansion, which also suffers from the drawback of falling into prematureness too early when the standard QPSO algorithm is used for its multiobjective optimisation. In this study, an adaptive improved QPSO algorithm is proposed to address the above problems. Firstly, a partitioned retrieval strategy is used to divide the population into an auxiliary class group and a main iterative population, and the respective search iteration intervals of the populations are assigned, thus optimising the initialisation mechanism of the standard algorithm, and then, the pheromone mechanism in the ACO algorithm is introduced to make the particles in the QPSO algorithm carry pheromones, and the particles determine their direction of travel by sensing the pheromone concentration in each path, thus improving the search ability of the particles. Experimental simulation results show that the improved strategy proposed in this study effectively improves the population diversity of the standard QPSO algorithm, avoids the algorithm from entering local optimum, and has good application in MSE osseous expansion position optimisation.

Temperature Field of Artificially Frozen Gravel Formation and Optimization of Freezing Pipe Layout Parameters under Seepage Flow

An artificial ground freezing method is often applied to highly permeable gravel formations. Seepage flow increases energy consumption and engineering accidents under this condition. Based on the physical modeling tests, the numerical simulations were conducted. The physical modeling test examined the development of the temperature and frozen wall. Numerical modeling integrated with the ACO algorithm was established to optimize the layout parameters of the freezing pipes. The results indicate that the flowing water prolongs the closure time of the frozen wall. Meanwhile, the total thickness of the frozen wall is also reduced by the flowing water. There are significant differences in the development rates of the frozen wall in different zones. The thickness of the entire frozen wall is nonuniform owing to the seepage flow. Following optimization using the proposed algorithm, the closure time was shortened from 82.4 d to 56.9 d for the frozen wall. Moreover, the freezing efficiency increased by 30.95% after optimization, and consequently, the entire frozen wall was more uniform with a nonweak zone. A case study showed that this optimization system is an effective method for artificial ground freezing operations in geotechnical engineering.

Register-Pressure-Aware Instruction Scheduling Using Ant Colony Optimization

This paper describes a new approach to register-pressure-aware instruction scheduling, using Ant Colony Optimization (ACO) . ACO is a nature-inspired optimization technique that researchers have successfully applied to NP-hard sequencing problems like the Traveling Salesman Problem (TSP) and its derivatives. In this work, we describe an ACO algorithm for solving the long-standing compiler optimization problem of balancing Instruction-Level Parallelism (ILP) and Register Pressure (RP) in pre-allocation instruction scheduling. Three different cost functions are studied for estimating RP during instruction scheduling. The proposed ACO algorithm is implemented in the LLVM open-source compiler, and its performance is evaluated experimentally on three different machines with three different instruction-set architectures: Intel x86, ARM, and AMD GPU. The proposed ACO algorithm is compared to an exact Branch-and-Bound (B&B) algorithm proposed in previous work. On x86 and ARM, both algorithms are evaluated relative to LLVM's generic scheduler, while on the AMD GPU, the algorithms are evaluated relative to AMD's production scheduler. The experimental results show that using SPECrate 2017 Floating Point, the proposed algorithm gives geometric-mean improvements of 1.13% and 1.25% in execution speed on x86 and ARM, respectively, relative to the LLVM scheduler. Using PlaidML on an AMD GPU, it gives a geometric-mean improvement of 7.14% in execution speed relative to the AMD scheduler. The proposed ACO algorithm gives approximately the same execution-time results as the B&B algorithm, with each algorithm outperforming the other on a substantial number of hard scheduling regions. ACO gives better results than B&B on many large instances that B&B times out on. Both ACO and B&B outperform the LLVM algorithm on the CPU and the AMD algorithm on the GPU.

Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models

Predictive capability of response surface methodology (RSM) and ant colony optimization combined with support vector regression (ACO-SVR) models are applied for determining optimal parameters in the process of heterogeneous Fenton oxidation of melanoidin, a high molecular weight polymer widely produced during fermentation processes generating large quantities of wastewater with intense brown color and extremely high chemical oxygen demand (COD). Prediction of the performance of nano zero-valent iron supported on activated carbon cloth-chitosan (ACC–CH–nZVI) catalysts was carried out using Box-Behnken design (BBD) and analysis of variance to evaluate the interaction of independent variables involved in heterogeneous Fenton reaction. The optimized condition with minimal consumption of H2O2 (173 mM) resulted in 77.1% decolorization of melanoidin-contaminated water corresponding to 74.4% COD removal at pH 3 (600 mg/l Fe dosage) for 90 min reaction time. The corresponding weight ratio of H2O2 to COD was 0.98, much lower than the stoichiometric value 2.125, indicating the effectiveness of ACC–CH–nZVI as a heterogeneous Fenton-like catalyst. In comparison to previously published experimental results, ACO-SVR model shows higher coefficient of determination (R2; 0.9983) but lower root mean squared error (RMSE) and mean absolute error (MAE) than those of RSM model, indicating relative superiority in prediction capability. Besides, ACO algorithm appears to be a promising tool for improving forecasting accuracy of SVR model. This work demonstrates the applicability of ACO-SVR model in predicting the performance of wastewater treatment using Fenton process with limited number of experiment and exhibits satisfactory prediction results.

Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development

AbstractThe vertical alignment optimization is about developing a minimum cost curvilinear vertical profile of constrained grade sections and appropriate non‐overlapping vertical curves passing through fixed control points with elevation constraints. Variations in ground profile and discreteness in unit cutting and filling costs make it a non‐convex, noisy, constrained optimization problem with many local minima. Further, the gradient related constraints and vertical curvature are non‐linear. This paper presents an innovative exploring and exploiting ant colony optimization (E&E‐ACO) algorithm with an appropriate point sampling, vertical curve fitting strategies, and an intuitive feasible region identification approach for solving the vertical alignment optimization problem. The E&E‐ACO algorithm extensively explores the feasible search space to generate a set of potential solutions and effectively exploit the space around the potential solutions for developing the optimized vertical alignment. The efficacy of the proposed method is demonstrated using two case studies. In one case study, the optimized solution by the proposed method had a marginally better objective function value and about three times lesser computational time than the solution by the mesh adaptive direct search method. The optimized alignment satisfied the elevation constraints of fixed control points and imitated the manually designed real‐world vertical alignment. The linearly varying exploration and exploitation parameters had better convergence rate than the other tested variations. Further, the proposed method at the end of 1000 iterations yielded about six times better result than the traditional ACO algorithm.

Mobile sink-based data collection in event-driven wireless sensor networks using a modified ant colony optimization

The hotspot problem is one of the primary challenges in the wireless sensor networks (WSNs) because it isolates the sink node from the remaining part of the WSN. A mobile sink (MS)-based data acquisition strategy mitigates the hotspot problem, but the traditional MS-based data gathering approaches do not resolve the issue. However, the conventional techniques follow a fixed order of visits and static traversal of the MS. In this context, this paper uses a modified version of the ant colony optimization strategy for the data collected through a MS to mitigate the hotspot problem in the WSNs while improving the energy efficiency, network lifetime, throughput by reducing the packet loss and delay. In our work, we initially construct a forwarded load spanning tree to estimate the freight of each node in the WSN. Further, we choose RPs and their path simultaneously using the modified ACO algorithm by considering the forward loads, remaining energy, distance, etc. The proposed work also adopts the virtual RP selection strategy void unnecessary data exchanges between the nodes and RPs. Hence, it reduces the burden on relay nodes and optimize the energy usage among the nodes. We compare our approach with the recent ACO-based algorithms, and our approach outperforms them.

Sampling‐based modified ant colony optimization method for high‐speed rail alignment development

AbstractHigh‐speed railway (HSR) alignment development is a complex and tedious problem due to an infinite number of possible solutions, the existence of non‐linear costs and impacts, and complex location and geometric design constraints. In this study, a low‐discrepancy point sampling‐based modified ant colony optimization (SMACO) algorithm for obtaining horizontal alignments with optimized HSR‐specific cost and impact, including noise and vibration impacts, is proposed. The low‐discrepancy sampling approach is used to identify the potential, from which appropriate intermediate s are selected to develop feasible alignments using the SMACO algorithm. It effectively avoids restricted land parcels and satisfies HSR‐related geometric design requirements. A real‐world case study demonstrated that the HSR alignment obtained using the proposed method was marginally better than the path planner method‐based alignment and the constructed alignment. The sensitivity analysis highlighted the impact of two key parameters, that is, the right of way widths and noise and vibration screening distances on the HSR alignment development. This study advances the alignment development automation, particularly the HSR horizontal alignment for design speeds over 180 km/h. It facilitates extensive search space exploration independent of infeasible regions, identifies and selects without being constrained to prespecified locations and a user‐defined number, and proposes a suitably modified ACO algorithm for the HSR alignment development.

Adaptive channel allocation for spectrum sharing based on ACO algorithm

Adaptive channel allocation for spectrum sharing based on ACO algorithm

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.

An Optimized Fuzzy Based Ant Colony Algorithm for 5G-MANET

The 5G demonstrations in a business has a significant role in today's fast-moving technology. Manet in 5G, drives a wireless system intended at an enormously high data rate, lower energy, low latency, and cost. For this reason, routing protocols of MANET have the possibility of being fundamentally flexible, high performance, and energy-efficient. The 5G communication aims to afford higher data rates and significantly low Over-The-Air latency. Motivated through supplementary ACO routing processes, a security-aware, fuzzy improved ant colony routing optimization protocol is proposed in MANETs. The goal is to develop a MANET routing protocol that could provide a stable packet transmission ratio, less overhead connectivity, and low end-to-end latency in shared standard scenarios and attack states. MANET demonstrates effective results with hybrid architecture and proved to be effective than other state-of-the-art routing protocols of MANETs, like AODV, its routing organization implemented through Optimized Fuzzy based ACO Algorithm for 5G. Millimeter-wavelengths are required to perform a significant role in 5G. This research proposed to test the efficiency of MANET consisting of only mmWave User Equipment. MANET reduced packet transmission loss of UEs with mmWave, meaning well-transmitted SNR leads directly to a better packet delivery ratio. To verify results, simulation using the NS-3 simulator mmWave module is used.

Meta-Heuristic Algorithms for the Generalized Extensible Bin Packing Problem With Overload Cost

In this paper, we consider a generalized the extensible bin packing problem with overload cost, first proposed by Denton et al. [1] in 2010, in which the total size of items packed into a bin is allowed to exceed its capacity, and the cost incurred each bin is equal to the fixed cost plus the overload cost, the objective is to minimize the total cost of all bins. According to the characteristics of the problem, we first propose an improved ant colony optimization algorithm (IACO), which enhances the positive feedback effect of ACO by improving the update method of pheromone and the adaptive adjustment parameters. We also introduce a variable neighborhood search method in ACO to improve the convergence of the algorithm and get rid of the phenomenon of local extrema. Then, we present a discrete particle swarm optimization algorithm (DPSO) to solve the problem. In order to ensure the uniform distribution and high quality of the initial particle swarm, we use some heuristic methods in the initialization process of the swarm, so that the initial particle can cover the entire search space with a large probability, which effectively improves the performance of DPSO algorithm. Finally, we compare and analyze the performance of these proposed algorithms through two sets of computational experimental frameworks. Compared with some algorithms in the literature, computational results signify that the improved ACO algorithm and MDPSO algorithm are more competitive than some other metaheuristic algorithms..

Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are promising, emerging technologies for the applications in oceanic research. UWSN contains high number of sensor nodes and autonomous underwater vehicles that are deployed to perform the data transmission in the sea. In UWSN networks, the sensors are placed in the buoyant which are highly vulnerable to selfish behavioural attack. In this paper, the major challenges in finding secure and optimal route navigation in UWSN are identified and in order to address them, Entropy based ACO algorithm (E-ACO) is proposed for secure route selection. Moreover, the Selfish Node Recovery (SNR) using the Grasshopper Optimisation Algorithm (GOA) is used to minimize the packet loss in the UWSN. The performance of the proposed E-ACO method is compared with existing routing methods such as Secure Authentication with Protected Data Aggregation (SAPDA), Secure Energy Efficient and Cooperative Routing (SEECR), Fault Resilient Routing using Moth Flame Optimization (FRR-MFO) and Improved ACO (IACO) method. The packet delivery ratio of the proposed E-ACO with 500 nodes is 0.89 which is higher than other existing methods such as SAPDA, SEECR, FRR-MFO and IACO.

In-Vehicle Stereo Vision Systems with Improved Ant Colony Optimization Based Lane Detection: A Solution to Accidents Involving Large Goods Vehicles Due to Blind Spots

This paper presents an in-vehicle stereo vision system as a solution to accidents involving large good vehicle due to blind spots using Nigeria as a case study. In this paper, a stereo-vision system was attached to the front of Large Good Vehicles (LGVs) with a view to presenting live feeds of vehicles close to the LGV vehicles and their distance away. The captured road images using the stereo vision system were optimized for effectiveness and optimal vehicle maneuvering using a modified metaheuristics algorithm called the simulated annealing Ant Colony Optimization (saACO) algorithm. The concept of simulated annealing is strategies used to automatically select the control parameters of the ACO algorithm. This helps to stabilize the performance of the ACO algorithm irrespective of the quality of the lane images captured in the in-vehicle vision system. The system is capable of notifying drivers through lane detection techniques of blind spots. This technique enables the driver to be more aware of what surrounds the vehicle and make decisions early. In order to test the system, the stereo-vision device was mounted on a Large good vehicle, driven in Zaria (a city in Kaduna state in Nigeria), and data were in the record. Out of 180 events, 42.22% of potential accident events were caused by Passenger Cars, while 27.22%, 18.33% and 12.22% were caused by two-wheelers, Large Good Vehicles and road users, respectively. In the same vein, the in-vehicle lane detection system shows a good performance of the saACO-based lane detection system and gives a better performance in comparison with the standard ACO method.