Implementation Of Ant Colony Optimization Research Articles

Implementation of Ant Colony Optimization and Principal Component Analysis for Early Prediction of Tomato Leaf Diseases: A Feature Reduction Approach

This paper details the implementation of a sophisticated algorithmic framework designed for the early prediction of various tomato leaf diseases, crucial for enhancing crop management and yield. The research deploys image processing techniques to extract pivotal features such as Red Mean, Green Mean, Blue Mean, Height, Width, and Defect Color channels from high-resolution images of tomato leaves. These features serve as indicators for diseases including Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, and infestations by Two-spotted Spider Mites. The core of the implementation lies in the integration of Ant Colony Optimization (ACO) with Principal Component Analysis (PCA) for feature reduction, which streamlines the dataset while retaining critical information. This combination not only reduces computational load but also improves the accuracy of the early prediction model. The paper demonstrates the application of this hybrid approach and compares its performance with existing models, emphasizing its efficiency and accuracy in early-stage disease prediction. The findings indicate that the proposed method outperforms traditional techniques, offering a reliable and scalable solution for agricultural disease management.

Improved cooperative Ant Colony Optimization for the solution of binary combinatorial optimization applications

AbstractBinary combinatorial optimization plays a crucial role in various scientific and engineering fields. While deterministic approaches have traditionally been used to solve these problems, stochastic methods, particularly metaheuristics, have gained popularity in recent years for efficiently handling large problem instances. Ant Colony Optimization (ACO) is among the most successful metaheuristics and is frequently employed in non‐binary combinatorial problems due to its adaptability. Although for binary combinatorial problems ACO can suffer from issues such as rapid convergence to local minima, its eminently parallel structure means that it can be exploited to solve large and complex problems also in this field. In order to provide a versatile ACO implementation that achieves competitive results across a wide range of binary combinatorial optimization problems, we introduce a parallel multicolony strategy with an improved cooperation scheme to maintain search diversity. We evaluate our proposal (Binary Parallel Cooperative ACO, BiPCACO) using a comprehensive benchmark framework, showcasing its performance and, most importantly, its flexibility as a successful all‐purpose solver for binary combinatorial problems.

Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent

Cancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.

Study on OBE Teaching Concept in the Context of Deep Learning for the Construction of University Mathematics Microcourses

Outcome-Based Education (OBE) is a goal-based educational system in which each part of education is around outcomes. By the end of the course, every student should have achieved the goal. Outcome-Based Education (OBE) involves various teaching methods and is not restricted to any specified way of teaching. Based on the targeted results, the teacher will mentor the students by acting as an instructor, trainer, and facilitator. The Deep Learning Technology of Artificial Intelligence is applied in various applications to carry out automation and physical tasks without human intervention along with data transfer through wireless networking. In this research, an Apriori Algorithm supports the identification of a suitable method for the teaching process (the OBE Teaching concept) through the outcome of the learning process. This optimization of identification of suitable method is performed with the implementation of Ant Colony Optimization (ACO) in the construction of the University Mathematics Course. The study results proved that the proposed algorithm provides an accuracy of 98.87%. The proposed algorithm can be trained further based on different rules to attain some increased performance of the methodology.

Implementasi Ant Colony Optimization (ACO) pada Sistem Irigasi Lahan Tadah Hujan

The purpose of this research is to produce an optimal water distribution system for irrigation of rainfed land. The problem with conventional irrigation systems is that the water distribution process cannot be controlled and monitored automatically and in real time. The impact on water distribution becomes ineffective. The implementation of Ant Colony Optimization (ACO) is used in research as a method to determine the location or node based on the pheromone pattern of the soil dryness level at the sprinkler nodes to be distributed by the water flow, taking into account the criteria level on the soil as a trend of probability values ??and determining the nodes according to the needs in the flow water. The results obtained from this study indicate that the data displayed is the level of dryness of each node, the volume of water in the reservoir, and the flow of water flowing. The ACO test shows the sequence of nodes that will be passed after the optimization process of water distribution in a rainfed irrigation system using the ACO method gets an error value calculated by the MAPE method of 43% so that it gets an accuracy value of 57%.

A Composite Metric Routing Approach for Energy-Efficient Shortest Path Planning on Natural Terrains

This paper explores the problem of energy-efficient shortest path planning on off-road, natural, real-life terrain for unmanned ground vehicles (UGVs). We present a greedy path planning algorithm based on a composite metric routing approach that combines the energy consumption and distance of the path. In our work, we consider the Terramechanics between the UGV and the terrain soil to account for the wheel sinkage effect, in addition to the terrain slope and soil deformation limitations in the development of the path planning algorithm. As benchmarks for comparison, we use a recent energy-cost minimization approach, in addition to an ant colony optimization (ACO) implementation. Our results indicate that the proposed composite metric routing approach outperforms the state-of-the-art energy-cost minimization method in terms of the resulting path distance, with a negligible increase in energy consumption. Moreover, our results indicate also that the proposed greedy algorithm strongly outperforms the ACO implementation in terms of the quality of the paths obtained and the algorithm running time. In fact, the running time of our proposed algorithm indicates its suitability for large natural terrain graphs with thousands of nodes and tens of thousands of links.

Implementation of ACO in Vanet with Detection of Faulty Node

Objective: To propose an Ant Colony Optimization (ACO) based routing approach for estimating the reliability parameter of backward and forward ants. Methods: The ACO-based proposed methodology is used to predict path or route discovery in VANET. This study has discovered the best optimal path for data transmission. The forward ant movement has a minimum hop count of four for vehicle number 6. The vehicle 6 is a source vehicle, and it starts data transmission. The pheromone concentration of the entire vehicle defers and, after updating, the performance was analyzed. The best path is selected based on hop count, then a path with maximum pheromone concentration is selected. The pheromone evaporation factor is updated after a certain time interval. Findings: We have checked the reliability of the path, considering pheromone evaporation as a factor, the pheromone table is updated at a regular time interval, and the reliability parameter is also proposed for performing a task without failure in an efficient manner. We have arranged a routing table in descending order of hop count and then select the best of three paths with a minimum hop count. We search for hop count less than or equal to 5 and total pheromone greater than or equal to 17.55. We find that Ant 3 having a hop count of 5 and total pheromone concentration of 19.37. In this work, we have taken the evaporation factor as 0.5. If the transmission of a message is successful, then the reliability assigned to the edge will be 100, and for unsuccessful transmission, reliability is taken down by 20. If the reliability of the path will become 40, then the vehicle has to search for a new path. In this way, we eliminate malicious nodes for efficient transmission of message. Novelty: The performance of VANET is improved using ACO based proposed routing approach. The optimal path is selected with a minimum hop count and reliability parameter to identify the successful and unsuccessful path. Keywords: Network security; VANET; Sybil; ACO; Pheromone Evaporation

High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

On Randomness and Structure in Euclidean TSP Instances: A Study With Heuristic Methods

Prediction of the quality of the result provided by a specific solving method is an important factor when choosing how to solve a given problem. The more accurate the prediction, the more appropriate the decision on what to choose when several solving applications are available. In this article, we study the impact of the structure of a Traveling Salesman Problem instance on the quality of the solution when using two representative heuristics: the population-based Ant Colony Optimization (ACO) and the local search Lin-Kernighan (LK) algorithm. The quality of the result for a solving method is measured by the computation accuracy, which is expressed using the percent error between its solution and the optimum one. We classify the instances in structured, semi-structured, and unstructured and perform a between classes and inside-classes analysis. All the structured instances were solved to optimality by the ACO implementation, which was not the case for the LK application. On small random instances, the ACO implementation used in this paper also optimally found the solutions. We show that the quality of the results on semi-structured and unstructured instances can be predicted using some instance parameters when using the ACO implementation. Using the same parameters, the accuracy of the solutions provided by the Lin-Kernighan application cannot be predicted. We also propose several new structured, clustered, and unstructured 2D Euclidean Traveling Salesman Problem instances that can be used by the research community for further investigations.

Ant Colony Optimization Implementation on Traveling Salesman Problem to Achieve the Shortest Logistic Route

Transportation refers to the movement of everything from raw materials to finished goods between various facilities in the supply chain. In transportation, the exchange between responsiveness and efficiency is manifested in the choice of transportation modes. Because transportation costs can be as much as one third of the supply chain operating costs, the decision made here is very important. Traveling Salesman Problem is one of the best-known NP-hard problems where there is no precise algorithm to solve it in polynomial time. The ACO algorithm has good potential for problem solving and recent research that has attracted a lot of attention, in particular is the case of solving NP-Hard problems. One of the earliest best works is completing TSP using ACS (Ant Colony System). Ant System is the first ACO algorithm with its main characteristics being that at each iteration, the pheromone values are updated by all (m) ants who have built a solution in the iteration itself. From the arrangement of the shipping routes that have been implemented by the Ant Colony Optimization on the Traveling Salesman Problem, the amount of savings in the transportation mode of trucks to mileage is 37%.

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping.

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Implementing a GPU-based parallel MAX–MIN Ant System

The MAX–MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore’s law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another key-component – the tabu list structure – which is used in the ACO’s solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

SummaryThe Internet of Things (IoT)–enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart‐patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First‐Come‐First‐Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT‐enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios.

Software Test Suite Minimization using Ant Colony Optimization

In the fast pacing technological era, the key to a successful software industry is quick delivery of high quality software to the clients. This high quality is achieved by performing software testing on the product. The high quality product ensures stakeholder’s satisfaction which in turn spreads good word about the software industry making it a success. In this paper, we will focus on the problems faced during regression testing and how the same can be handled. Regression testing is a critical activity done during the software maintenance phase of the software development cycle. However, it has countless underlying issues like effective test case generation and prioritization, etc which need to be dealt with. These issues demand effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of Ant Colony Optimization (ACO) for test suite minimization has been an area of interest for many researchers. This paper presents an implementation of ACO for test suite minimization, showcasing how arbitrary nature of ACO helps choose an optimal solution to the problem.

Penjadwalan Perkuliahan dengan Metode Metaheuristic Ant Colony Optimization Studi Kasus Politeknik Negeri Bali

Lecture scheduling problems are a very complex problem, where the core of the scheduling is how to schedule several components consisting of students, lecturers, space, time, and subject matter with due regard to certain constraints and constraints. The goal to be achieved is to apply the Ant Colony Optimization (ACO) Metaheuristic method to optimize the lecture scheduling system. Every step taken by ACO uses an algorithm adapted from ant behavior to solve combinatorial problems. This system is created using Microsoft Visual Basic programming by entering several parameters, namely Lecturer, Course, Room, and several variables from each parameter which are then processed to produce lecture scheduling. The results obtained from the ACO implementation to solve college scheduling problems are seen from the number of constrained constraints and the length of time required from each iteration in each method to get a class schedule so that they get a schedule with a 82 percent truth level which is a comparison of constraint violations to the total number of timeslot used for scheduling.

Optimal Location of Phasor Measurement Unit Using Ant Colony Optimization Under Contingency

Accurate and fast monitoring is achieved by using the real time synchronized phasor data obtained from Phasor Measurement Unit (PMU). As the cost of PMU is more expensive, it is not economical to install PMU at all the nodes of a power system. Therefore, it is very much essential to implement an optimal method for PMU location. In this research work, implementation of Ant Colony Optimization (ACO) for optimal PMU placement under normal and single PMU loss conditions is discussed. The PMUs are placed in such a manner that all voltage phasors and current phasors are measurable with a minimum number of PMUs. The proposed ACO is able to locate the PMUs optimally enabling the complete measurement of all voltage and current phasors with a minimum number of PMUs. The ACO is an efficient optimization algorithm to optimize the placement of the PMU for measuring the voltage and current phasors with minimum no of PMU. The proposed method is demonstrated in IEEE 14 bus, IEEE 30 bus, and IEEE 57 bus to find the optimal location of PMU under normal and for single PMU loss conditions.

Assessment of Ant Colony using Component based Software Engineering Metrics

Objectives: Conventionally, to solve a problem it is necessary to design a new system every time and that it is difficult to reuse existing system for the designing of new system. This paper estimates usability of reusable components and interaction between components for integration of system. Methods: To handle this kind of problem Component Based System (CBS) has been considered. The reusability concept is the main factor of Component Based Software Engineering (CBSE) for components. More usage of reusable components provides better results in terms of estimation of reliability and efficiency of Component Based System (CBS). Ant Colony Optimization (ACO) is one among soft computing technique which may be used to estimate the path followed by components and interaction of components. In this paper ACO is used as methodology to find out more reusable components to increase the reliability or efficiency of the system. MATLAB is used for implementation of ACO. Performance metrics like Component efficiency, component density and component dependency are used for the analysis of proposed work. Findings: The results shows that in proposed mechanism the component efficiency is high, component density is also high as compare to existing CBS. Applications: This paper may helpful for the analysis of soft computing techniques by the use of CBSE performance metrics.

A Software Framework for Rapid Application-Specific Hybrid Photonic Network-on-Chip Synthesis

Network on Chip (NoC) architectures have emerged in recent years as scalable communication fabrics to enable high bandwidth data transfers in chip multiprocessors (CMPs). These interconnection architectures still need to conquer many challenges, e.g., significant power consumption and high data transfer latencies. Hybrid electro-photonic NoCs have been recently proposed as a solution to mitigate some of these challenges. However, with increasing application complexity, hardware dependencies, and performance variability, optimization of hybrid photonic NoCs requires traversing a massive design space. To date, prior work on software tools for rapid automated NoC synthesis have mainly focused on electrical NoCs. In this article, we propose a novel suite of software tools for effectively synthesizing hybrid photonic NoCs. We formulate and solve the synthesis problem using four search-based optimization heuristics: (1) Ant Colony Optimization (ACO); (2) Particle Swarm Optimization (PSO); (3) Genetic Algorithm (GA); and (4) Simulated Annealing (SA). Our experimental results show significant promise for the ACO and PSO based heuristics. Our novel implementation of PSO achieves an average of 64% energy-delay product improvements over GA and 53% improvement over SA; while our novel ACO implementation achieves 107% energy-delay product improvements over GA and 62% improvement over SA.

Memcomputing Implementation of Ant Colony Optimization

We report on similarities between memcomputing with memristive networks and ant colony optimization. In particular, we show that one can design memristive networks to solve short-path optimization ...