Ant Colony Optimization Technique Research Articles

Application of an Improved Ant Colony Optimization Algorithm of Hybrid Strategies Using Scheduling for Patient Management in Hospitals

To balance the convergence speed and solution diversity and enhance optimization performance when addressing large-scale optimization problems, this research study presents an improved ant colony optimization (ICMPACO) technique. Its foundations include the co-evolution mechanism, the multi-population strategy, the pheromone diffusion mechanism, and the pheromone updating method. The suggested ICMPACO approach separates the ant population into elite and common categories and breaks the optimization problem into several sub-problems to boost the convergence rate and prevent slipping into the local optimum value. To increase optimization capacity, the pheromone update approach is applied. Ants emit pheromone at a certain spot, and that pheromone progressively spreads to a variety of nearby regions thanks to the pheromone diffusion process. Here, the real gate assignment issue and the travelling salesman problem (TSP) are chosen for the validation of the performance for the optimization of the ICMPACO algorithm. The experiment's findings demonstrate that the suggested ICMPACO method can successfully solve the gate assignment issue, find the optimal optimization value in resolving TSP, provide a better assignment outcome, and exhibit improved optimization ability and stability. The assigned efficiency is comparatively higher than earlier ones. With an assigned efficiency of 83.5%, it can swiftly arrive at the ideal gate assignment outcome by assigning 132 patients to 20 gates of hospital testing rooms. To minimize the patient's overall hospital processing time, this algorithm was specifically employed with a better level of efficiency to create appropriate scheduling in the hospital.

Improving Software Defect Prediction With a Combination of Feature Selection Based On Ant Colony Optimization and Ensemble Technique

Software defect prediction plays a vital role in enhancing software quality and minimizing maintenance costs. This study aims to improve software defect prediction by employing a combination of Ant Colony Optimization (ACO) for feature selection and ensemble techniques, particularly Gradient Boosting. The research utilizes three NASA MDP datasets: MC1, KC1, and PC2, to evaluate the performance of four machine learning algorithms: Random Forest, Support Vector Machine (SVM), Decision Tree, and Naïve Bayes. Data preprocessing involved handling class imbalances using the SMOTE technique and transforming categorical data into numerical representations. The results indicate that the integration of ACO and Gradient Boosting significantly enhances the accuracy of all four algorithms. Notably, the Random Forest algorithm achieved the highest accuracy of 99% on the MC1 dataset. The findings suggest that combining ACO-based feature selection with ensemble techniques can effectively boost the performance of software defect prediction models, offering a robust approach for early detection of potential softwaredefects and contributing to improved software reliability and efficiency.

An Innovative Approach for Mission Sharing and Route Planning of Swarm Unmanned Aerial Vehicles in Disaster Management

Fast and effective response in disaster situations is critical for the success of rescue operations. In this context, swarm Unmanned Aerial Vehicles (UAVs) play an important role in disaster response by rapidly scanning large areas and performing situation assessments. In this paper, we propose an innovative method for task allocation and route planning for swarm UAVs. By combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO) techniques, this method aims to ensure the most efficient movement of UAVs. First, clusters are created using GA to determine the regions of the disaster area that need to be scanned. At this stage, factors such as the capacities of the UAVs, their flight times, and the breadth of their mission areas are taken into account. Each UAV is optimized to scan a specific area assigned to it. Once the clusters are formed, the routes of the UAVs within each cluster are determined by the Ant Colony Algorithm (ACA). The route planning is tested both on Google Maps and in a simulation environment. Google Maps is used to evaluate the accuracy and feasibility of route planning based on real-world conditions, while the simulation environment provides the opportunity to test the behavior of the UAVs and the effectiveness of the routes in a virtual setting. With real-time data integration, the UAVs' route planning can be updated instantly and quickly adapted to emergency situations.

Design of a precise ensemble expert system for crop yield prediction using machine learning analytics

AbstractAgriculture is facing significant challenges in the development of crop yield forecasts, which are important aspects of decision‐making at the international, regional, and local levels. The area of agriculture is attracting growing attention because of increasing the demand for food supplies. To ensure future food supplies, crop yield prediction (CYP) provides the best decision‐making to assist farmers in agricultural yield forecasting efficiently. Nevertheless, CYP is a difficult endeavor because of the intricacy of the underlying mechanisms and the effect of numerous factors, including weather patterns, soil characteristics, and crop management techniques. In today's era, ensemble learning (EL) approaches have recently demonstrated significant promise for enhancing the reliability and accuracy of CYP. The success of the EL techniques depends on several facts, including how the base learner models are trained and how these are combined. This study provides important insights into the EL techniques for CYP. This paper proposes an expert system model named precise ensemble expert system for crop yield prediction (PEESCYP) to predict the best crop for agricultural land. The proposed PEESCYP model employs multiple imputation by chained equation (MICE) data imputation technique to treat the missing values of the collected dataset, the isolation forest (IF) technique for outlier detection, the ant colony optimization (ACO) technique to perform feature selection, robust scaling (RS) technique to perform data normalization, and the extra tree (ET) is used for classification to overcome the variance and overfitting problem of the single classifiers. The measurements of the proposed PEESCYP model have been collected by means of accuracy, precision, recall, and F‐1 score using a prepared dataset, which is collected from International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT), and the proposed model is compared with different single‐classifier based ML models, EL models, and various existing models available in the literature. The results of this experiment underline that the proposed PEESCYP model outperforms the others.

Intelligent Routing Approaches Based on Ant Colony Optimization for Dynamic Internet of Things Network

Introduction: This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path. Method: The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACODijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB. Results: At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED. Conclusion: In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).

Enhancing Resource Utilization and Load Distribution with ACO and Reinforcement Learning in Dynamic Computing Infrastructures

In the rapidly evolving landscape of dynamic computing infrastructures, efficient resource utilization and adaptive load balancing are critical for maintaining system performance and sustainability. This research introduces a novel framework that integrates Ant Colony Optimization (ACO) and Reinforcement Learning (RL) to enhance resource utilization and load distribution in such environments. In this research, we present a comprehensive framework for enhancing resource utilization and load distribution in dynamic computing infrastructures using a hybrid approach that integrates Ant Colony Optimization (ACO) and Reinforcement Learning (RL) algorithms. The proposed framework aims to address the challenges of adaptive load balancing and efficient resource management in highly variable and resource-intensive computing environments. Our approach leverages the strengths of ACO in discovering optimal paths and RL in learning from the environment to make informed decisions. We evaluate the performance of our framework using key parameters: Total Energy Consumption (kWh), Average Energy Consumption per Node (kWh), Peak Energy Consumption (kW), Energy Efficiency (Tasks/kWh), and Dynamic Energy Consumption (kWh/hour). The evaluation compares three methods: Least Load Balancing (LLB), ACO, and RL, with RL demonstrating the best results. Experimental results indicate that the RL-based approach significantly reduces Total Energy Consumption and Average Energy Consumption per Node while maintaining a lower Peak Energy Consumption. Furthermore, the RL method shows improved Energy Efficiency and optimal Dynamic Energy Consumption, highlighting its potential for sustainable and efficient resource management in dynamic computing infrastructures. This study underscores the importance of intelligent load balancing and resource optimization strategies in modern computing environments and demonstrates the effectiveness of combining ACO and RL techniques to achieve these goals. Our findings provide valuable insights for future research and development of advanced load balancing frameworks that can adapt to the ever-evolving demands of dynamic computing systems.

Instance reduction algorithm based on elitist min-max ant colony optimization technique

Although the advancement of technology has made data processing quite easier, it is still an enormous task, especially if the volume is large and can affect the learning capability of the classifier. Instance Reduction techniques can be employed on data to attain best results while making a substantial difference in terms of data storage needs, and processing speed. The proposed novel algorithm – Elitist Min-Max Ant Colony based Instance Reduction (ÆIR), can be attributed to the hybridization of bio-inspired optimization algorithms and Machine Learning algorithms for instance reduction. The primary features of ÆIR are the introduction of new hyperparameters such as the elitist ants, the min-max bounding of pheromones, and the incorporation of the length of the reduced set, all which aid in electing better instances for the reduced set. To evaluate the performance of the proposed ÆIR algorithm, 18 benchmark datasets of various dimensions were used alongside 4 different classifiers: Random Forest, Decision Tree, K-Nearest Neighbor and Naïve Bayes. The experiments demonstrated the effectiveness of ÆIR in reducing the size of the instances. On an average, approximately 50 % reduction in the number instances was observed. This can result in an equivalent decline in terms of the storage requirements, while contemporaneously improving the prediction accuracy.

Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique

A wireless sensor network (WSN) is a wireless network composed of sensor nodes, widely used in military, industrial, and agricultural fields. In these practical application scenarios, due to the limited routing search ability of wireless sensor nodes and the small coverage range of a WSN, it faces the problem of a relatively short network lifecycle. Therefore, increasing a WSN’s coverage range and reducing node energy consumption are of high value. This article proposes a method of using the ant colony algorithm (ACOD) to calculate the optimal routing of a WSN, the K-means algorithm for clustering, and the whale algorithm (WOA) and a backpropagation (BP) neural network to increase the coverage range of a WSN. After multiple experiments, the total energy consumption of WSN nodes has been effectively reduced, the coverage range of the WSN has been increased, and the lifecycle of the WSN has been extended.

DYNAMIC FIREWALL CONFIGURATION FOR VIRTUAL NETWORKS: A COMPREHENSIVE AUTOMATION FRAMEWORK

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 diversion sites in order to obtain optimal performance. Second, by reducing the problem to a k-dense subgraph problem, we demonstrate that it is nondeterministic polynomial (NP)-hard. We create improved ant colony optimisation (IACO), a popular ant colony optimisation technique for network optimisation problems, to address this issue. We also develop and examine the theoretical performance of the load balancing algorithm with diversion routing (LBA-DR). Ultimately, we test the IRTE in various real-world topologies, and the findings demonstrate that the IRTE works better than previous algorithms. For example, while testing on Bell Canada topology, the maximum bandwidth is 24.6% less than that of traditional algorithms. Keywords: Segment Routing, Traffic Engineering, Control Overheads, Packet Headers, Forwarding Efficiency, Intelligent Routing Scheme.

Using Improved Hybrid Grey Wolf Algorithm Based on Artificial Bee Colony Algorithm Onlooker and Scout Bee Operators for Solving Optimization Problems

Grey Wolf optimization (GWO) is a newly developed stochastic meta-heuristic technique motivated by nature. It shows potential in diverse optimization challenges. It replicates grey wolf hunting behaviour and social hierarchy, exploring the solution space similar to their natural process. The algorithm efficiently explores and converges to the optimal solution. However, a drawback of the standard GWO is its limited exploitation capability due to its exploration-focused iterations. This may hinder finding the optimal solution nearby, leading to lower local convergence rates and degraded solution quality. To address this, the GWO-Employed-Onlooker model suggests incorporating the onlooker and scout bee operators from the artificial bee colony algorithm (ABC) during the position-changing stage of the grey wolves. This enhances exploitation capability, resulting in improved local convergence rates and better solution quality. The proposed method’s performance is evaluated on various optimization functions and compared their convergence rate to standard GWO, Genetic Algorithm (GA), Firefly Algorithm (FA), ABC, and Ant Colony Optimization (ACO) techniques. The results demonstrate that the proposed strategy GWO-Employed-Onlooker is better, indicating that it is valuable in solving optimization problems.

A hybrid framework for routing and channel assignment in WMN’s

Wireless mesh network (WMNs) can replace the physical existence of wired network; nevertheless, to solve the problem of traffic and congestion, they must carefully arrange radio resource assignment. In this paper, we focus on the major issues while transferring the data over the network and optimization of resources. WMN mainly suffers from congestion problem when numbers of channel transfer data at a same time which indirectly leads to overlapping channels and sometime data cannot be mitigated to destination. To overcome this problem and provide optimal solution we proposed hybrid channel assignment technique which offers four phase solution technique, in this technique firstly by mitigating the traffic than assigning the optimal path based on greedy and BFS. The purpose of this congestion control hybrid ant based greedy-BFS-load balancing (CCHAGBL) technique is to diminish the number of used slots, which is directly related to the overall resource assignment. We also provide optimal solution using Ant colony optimization technique with hybrid algorithm to achieve the network efficiency and resource utilization with randomly generated traffic. With the help of hybrid ant based greedy-BFS-load balancing (GBL) channel assignment algorithm throughput and packet delivery ratio is upgraded and delay is reduced to minimum.

Presenting a meta-heuristic solution for optimal resource allocation in fog computing

Given that cloud computing is a relatively new field of study, there is an urgent need for comprehensive approaches to resource provisioning and the allocation of Internet of Things (IoT) services across cloud infrastructure. Other challenging aspects of cloud computing include IoT resource virtualization and disseminating IoT services among available cloud resources. To meet deadlines, optimize application execution times, efficiently use cloud resources, and identify the optimal service location, service placement plays a crucial role in installing services on existing virtual resources within a cloud-based environment. To achieve load balance in the fog computing infrastructure and ensure optimal resource allocation, this work proposes a meta-heuristic approach based on the cat swarm optimization method. For more clarity in the difference between the work presented in this research and other similar works, we named the proposed technique MH-CSO. The algorithm incorporates a resource check parameter to determine the accessibility and suitability of resources in different situations. This conclusion was drawn after evaluating the proposed solution in the ifogsim environment and comparing it with particle swarm and ant colony optimization techniques. The findings demonstrate that the proposed solution successfully optimizes key parameters, including runtime and energy usage.

Large scale penetration impact of PMS-WTGS on voltage profile and power loss for 5-bus, 330kV system of the Nigerian grid

In recent times, adverse environmental changes and the gradual reduction in fossil fuel deposits is making integration of renewable energy secure global attention. This paper therefore investigates the effect of high penetration of wind power on power loss and voltage profile of 52-bus, 330kV Nigerian grid. Modeling and simulation of the study were successfully done using the Continuation Power Flow method and Power System Analysis Toolbox in the Matrix Laboratory environment. The Ant Colony Optimization (ACO) technique codes were used to determine the best sites and sizes of Permanent Magnet Synchronous-based Wind Turbine Generators (PMS-WTGs) on the grid as nonoptimal placement and size of PMS-WTGs will result into active power losses and poor voltage profile. The ACO and power flow analysis indicated two load buses, Aladja bus and Yola bus as best sites for PMS-WTGs with optimal sizes of 100MW and 197MW respectively. The result shows a decrease in the system's active (42.1%) and reactive (43.9%) power losses while heightening voltage magnitudes of critical buses to statutory values and enhancing the grid power quality. The overall results indicate that integrating 297MW PMS-WTGs improves voltage stability and power loss reduction by maximizing system loadability and reducing line losses.

Research on Integrating Blockchain and Machine Learning LPP Algorithm in Online Education Platform under COVID-19 Environment

Learners confront the issue of navigating an enormous quantity of resources in the developing field of online education, which has been exacerbated by the COVID-19 pandemic. To solve this, our research proposes a novel Learner Path Planning (LPP) model that integrates blockchain and machine learning technologies to maximize the online learning experience. This model employs the ant colony optimization technique, which has been upgraded with blockchain for enhanced security and machine learning for intelligent path planning, to provide a more personalized and efficient learning experience. Our approach determines the extent of concept realization and interaction by examining the interaction degrees of knowledge points, establishing heuristic information and initial pheromone levels for the optimization process. This technique not only optimizes teaching duration based on instructional efficacy, but it also adapts dynamically to individual learner needs. Our empirical data reveal that goal success rates improve significantly across all learner levels. For example, elementary students in 2021 had the highest goal achievement rate of 0.5896. In 2019, intermediate and advanced learners attained rates of 0.7726 and 0.9058, with a significant association between course similarity and target achievement. Blockchain integration ensures secure and transparent processing of educational data, while machine learning algorithms successfully personalize learning routes to meet the various demands of learners. This study not only assists learners in effectively identifying suitable resources, but it also provides useful insights for instructors in improving online teaching approaches. The model's adaptability and scalability make it particularly applicable in the context of the COVID-19 pandemic's rapid developments and problems in the education sector.

A region-wise indoor localization system based on unsupervised learning and ant colony optimization technique

WiFi-based indoor localization has gained widespread attention in the recent past with the ubiquitous deployment of WLAN. However, for sustainable performance, it is crucial to identify and maintain the important WiFi Access Points (APs). Interestingly, the localization capabilities of APs differ even within their area of coverage depending on the indoor ambience and building properties. Thus, the significance of an AP to the localization performance can be better assessed if the entire region is divided into optimal number of clusters/subregions. This type of problem is hardly investigated in the literature. Consequently, in this paper, the aforementioned challenges are addressed from the perspective of expert systems through applying machine learning and meta-heuristic techniques. Accordingly, our contribution is three-fold. First, we have designed a Region-wise Indoor Localization System (RwILS) in which a sub-regional division algorithm is proposed using DBSCAN approach. Second, a sub-region-wise important AP selection algorithm is designed based on Ant Colony Optimization technique. Third, a location estimation approach is proposed that first identifies the sub-region and then predicts the location point in that sub-region using supervised learning classifiers. A publicly available dataset, JUIndoorLoc is used for experimental evaluation. The localization accuracy of RwILS is improved to 95.68% while the state-of-the-art classifiers give 71% to 83% accuracy. RwILS also outperforms some recent works which further validates its effectiveness. Thus, this proposed technique can lead to an effective expert system in the domain of indoor localization.

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes.

Analyze the Impact of Mobility and Pause Time on ACO based Manet Routing Protocols - Review

Abstract: Recently, many researchers have studied its performance within Mobile Ad-hoc Networks (MANETs). Every ad hoc network protocol focuses on a different set of measurements and traits. The majority of the currently utilized mobility models are employed to provide realistic movement patterns for MANET scenarios. A crucial element in the creation of MANET protocols is simulation. The future topology of a network can be predicted, which enables Quality of service- aware routing to choose a dependable link for data transmission. Ant algorithms, also known as swarm intelligence, are a wider field of study that deals with algorithmic approaches that are motivated by the behaviour of ant colonies and other insects. Ant Colony Optimization (ACO) is a subset of this larger topic. Based on the pheromone value deposited over the link, we identified two categories for the link quality in the proposed approach. We have demonstrated how the network's performance is enhanced by the suggested technique. The nodes are moving in the specified terrain dimensions through the Enhanced Manhattan Mobility Model (EMMM). The five QoS performance metrics are analyzed over three MANET routing protocols. The comparison is made over two important factors-mobility and pause time with the proposed ACO technique of reliable links. In the first experiment of different speeds, DSR is 25.5% better than AODV and 5.3% better than DSDVin metric packet dropping, 1.15% better than AODV and 1.01% better than DSDV in Packet Delivery Ratio, 1.5% better than AODV and 1.04% better than DSDV in packet overhead, and 62.7% better than AODV and 73.1% better than DSDV in average end-to-end delay. In the second experiment of pause times, DSR is 25.05% better than AODV and 4.5% better than DSDV in packet dropping, 1.18% better than AODV and 1.01% better than DSDV in packet delivery ratio, 1.01% better than DSDV in throughput, 0 to 0.002% in packet overhead, and 0.02% to 0.005% shorter delay.

Optimizing workload distribution in Fog-Cloud ecosystem: A JAYA based meta-heuristic for energy-efficient applications

Fog-integrated Cloud has emerged as a novel computing paradigm that brings Cloud computing services to the network's edge in real-time, though with limited capabilities. Despite its advantages, there are several challenges including workload distribution, energy consumption, computational time, and network latency, that require attention. The workload of IoT applications can be distributed over the Fog or Cloud devices based on their priority, deadline, and latency restrictions. In this work, we introduce a novel population-based metaheuristic called MAYA, a modified variant of the JAYA algorithm, to address the Energy-Efficient Workload Distribution of Sensors (EEWDS) in the Fog-Cloud ecosystem. The workload distribution of IoT applications depends on several factors such as request deadlines, the energy consumed during transmission, and needed computation. The performance of the proposed model for the energy consumption, computation time, CO2 emission, fairness index, and the convergence rate, is evaluated through simulation experiments. The results are compared in two scenarios: one concerning to methodology, where the performance is compared with JAYA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) techniques. The other scenario is based on the environment, where we examine Cloud-only, Fog-only, and Fog-Cloud integrated environments. Compared to JAYA, GA, PSO and ACO, the proposed MAYA technique demonstrates significant improvements, including reduction in energy consumption by 34.76%, 88.92%, 85.36% and 93.84%; decrease in computation time by 37.64%, 85.07%, 87.22%, and 91.08%; decrease in CO2 emissions by 23.46%, 76.24%, 97.17%, and 99.02%; and increase in fairness index by 9.62%, 3.72%, 16.90%, and 15.26% respectively.

Finding the Link Between Iranian EFL Teacher Motivation and Engagement via Ant Colony Optimization Algorithm and Fuzzy Decision Mode.

Teacher motivation is considred as one of the most decisive factorts infulencing teacher functioing as well as students' achievement. Many variable can develop teacher motoivation. In this study, it is presumed that teacher engagement, comprising three facets of emotional, behavioral, and cognitive influence teacher motivation. To examine this hypothesis, this study takes the initiative to utiliuze an innovative artificial intelliengce (AI)-inspired approach called Ant Colony Optimization (ACO) technique. ACO is an artificial intelligence (AI) algorithm originating from natural phenomena. The concept originates from biology and physics and specifically from ants' movements. ACO has the ability to find the connections between inputs and outputs, and it can find the most influencing inputs. Motivation was the output of the study, and the inputs were three different engagement factors. Based on the results, ACO reached a high R-value meaning that it could predict the output with a high accuracy. The findings of this study substantiate the wide-ranging and multifacsted potentials of AI, in particular ACO, in studying and predicting human functioning in academic settings.

Machine Learning Algorithms in Cloud Manufacturing - A Review

Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment.