Enhanced Ant Colony Optimization Research Articles

Improving Broadcast System of Integrated Satellite-Terrestrial Network-based on Enhanced Ant colony Optimization

With the use of seamless high-speed worldwide network connectivity in future, it is anticipated that the integrated satellite-terrestrial network (ISTN) will be a possible option. Due to the inadequacy of topological data and the close coupling of data and control planes, deploying optimized rules on routers is difficult. The important factors in ISTN networks are routing rules and policies, link failure, and high-bandwidth communication. Software-defined networking (SDN) is an open innovation approach that enables programmability from a central location. The controller handles the complexity of the network, whereas the infrastructure layer devices relay the packets. Thus, we investigated the broadcast by dynamically adjusting routes for fault tolerance and energy efficiency of the ISTN using distance between nodes. In addition, using dynamic source routing, this study examines the competence functions of all managing nodes in a network. The routing design is distributed among all nodes to create numerous collective paths. For LEO satellite networks, the ant colony optimization-based routing algorithm is an improved version that considers the probability of faults and low-energy consumption. The proposed simulation ensures a seamless transition in the event of failures and avoids the requirement for an additional coordination service.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Enhancing DoS Detection in WSNs Using Enhanced Ant Colony Optimization Algorithm

Enhancing DoS Detection in WSNs Using Enhanced Ant Colony Optimization Algorithm

Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering

Vehicular ad hoc networks (VANETs) are a rapidly evolving field at the intersection of intelligent transportation systems, emphasizing the need for a stable and scalable VANET topology to accommodate growing vehicular densities. The intricate challenge of route selection calls for advanced clustering protocols to bolster road safety and message routing. This research introduces a novel approach to intelligent clustering routing protocols, leveraging heuristic-based solutions built upon an enhanced ant colony optimizer (ACO) framework. The study unfolds in two stages: the creation of a dynamic search space model and the election of cluster heads (CHs). The innovative dynamic aware transmission range parallel Euclidean distance (DA-TRPED) technique establishes a dynamic search space using the parallel Euclidean distance (PED) concept. This approach evaluates vehicular nodes by estimating PED values, reducing the search process’s complexity. Subsequently, an intelligent cluster head is selected by enhancing the dynamic evaporation factor (DEF) within the ACO technique. The experimental validation of the DA-TRPED technique takes place in NS2 simulations, demonstrating superior performance compared to conventional ACO. This enhancement is evident in metrics such as packet delivery, packet drop, throughput, end-to-end delay, and the lifetime analysis of clustered nodes. The proposed approach holds promise for optimizing VANETs, enhancing their stability and scalability while promoting road safety and efficient message routing.

Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO-ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO-ResNet101 over current methodologies.

An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders.

Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.

An Enhanced Bio-Inspired Routing Algorithm for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes. In this context, this work presents an enhanced bio-inspired routing algorithm (EBIRA) to provide reliable communication. In EBIRA, enhanced ant colony optimization (EACO) finds the optimal long-life short-distance routes with the minimum hops based on distance, received signal strength metric, hop count, and evaporation rate. In EBIRA, the selected path has a short distance and a high level of connectivity at the link level with minimum hops. Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles. Simulation results show that the performance of EBIRA is better than reliable route discovery by using ant colony optimization (RDACO) and road-aware geographic routing protocol (RAGR) in terms of packet delivery ratio, throughput, and latency. Furthermore, variations of the received signal strength based on vehicle density and speed are evaluated, and the EBIRA route discovery success ratio is estimated and shown based on vehicle density at speed.
 HIGHLIGHTS
 
 Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes
 VANET routing faces various challenges due to its unique characteristics such as high mobility, dynamic topology, unlimited network size, no infrastructure, and wireless communication
 Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles
 The Less complexity, adaptability and self-organizing characteristics of ACO can cope with frequent topology changes, high mobility, absence of infrastructure, and wireless communication. The robustness feature of the ACO often helps to overcome network interruptions in the form of disconnections
 
 GRAPHICAL ABSTRACT

Enhanced Ant Colony Optimization for Routing in WSNs An Energy Aware Approach

Enhanced Ant Colony Optimization for Routing in WSNs An Energy Aware Approach

WITHDRAWN: Resilience of mobile ad hoc networks to security attacks and optimization of routing process

Abstract The need for efficient routing that can be used to avoid malfunctioning or malicious network known issues, While Mobile Ad-hoc Networks (MANETs) are becoming extremely prevalent and are exploited in various applications, including the application of the Vehicle Ad-hoc Network (VANET), wireless sensor network (WSN) environmental monitoring, and 5G device-to-device (D2D) connectivity. This research presents the Resilience of Mobile Ad hoc Networks to various security attacks and optimizes the routing Process. Moreover, its application to Ant Colony Optimization (ACO), a flippant is implemented, a logic-improved routing algorithm that addresses all such difficulties. To identify the linear nodes of complexity, the distributed fuzzy logic module eliminates them from the Safety Aware Fuzzy Enhanced Ant Colony Optimization (SAFEACO) routing process. The NS-3 simulator follows the SAFEACO routing protocol, and various tests in MANET and VANET scenarios have been carried out. SAFEACO's efficiency is correlated to other innovative and well-known solutions. SAFEACO performs superiorly for each metric of its application, such as the Packet Delivery Ratio (PDR) and the End-to-End delay. SAFEACO also offers greater strength against the black hole, Sybil and inundation attacks due to its ability to classify linear-complexity nodes.

Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm

Scheduling of a hot strip mill is an important decision problem in the steel manufacturing industry. Previous studies on the hot strip mill scheduling problem have mostly neglected the random factors in production. However, random variations in processing times are inevitable due to unpredictable delays and disturbances. In this article, we adopt a robust optimization approach to deal with the uncertainty in processing times. The advantage is that no assumption has to be made regarding the distribution of random data, and the obtained schedule will remain strictly feasible when the variations have not exceeded a predefined uncertainty set. First, a mixed-integer linear programming model is presented to formulate the robust scheduling problem. Then, a hybrid metaheuristic algorithm, which combines ant colony system (ACS) and enhanced local search, is proposed to provide an efficient solution to the problem. Finally, extensive computational experiments involving both randomly generated and real-world instances have been conducted to verify the effectiveness of the proposed algorithm. It is shown that the algorithm achieves optimality for small instances and outperforms two state-of-the-art metaheuristics when used to solve large instances.

EACO: An Enhanced Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing

EACO: An Enhanced Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing

Traffic Route Optimization for University Students with Enhanced Ant Colony Optimization Algorithm

Traffic Route Optimization for University Students with Enhanced Ant Colony Optimization Algorithm

The Optimal Energy Dispatch of Cogeneration Systems in a Liberty Market

This paper proposes a novel approach toward solving the optimal energy dispatch of cogeneration systems under a liberty market in consideration of power transfer, cost of exhausted carbon, and the operation condition restrictions required to attain maximal profit. This paper investigates the cogeneration systems of industrial users and collects fuel consumption data and data concerning the steam output of boilers. On the basis of the relation between the fuel enthalpy and steam output, the Least Squares Support Vector Machine (LSSVM) is used to derive boiler and turbine Input/Output (I/O) operation models to provide fuel cost functions. The CO2 emission of pollutants generated by various types of units is also calculated. The objective function is formulated as a maximal profit model that includes profit from steam sold, profit from electricity sold, fuel costs, costs of exhausting carbon, wheeling costs, and water costs. By considering Time-of-Use (TOU) and carbon trading prices, the profits of a cogeneration system in different scenarios are evaluated. By integrating the Ant Colony Optimization (ACO) and Genetic Algorithm (GA), an Enhanced ACO (EACO) is proposed to come up with the most efficient model. The EACO uses a crossover and mutation mechanism to alleviate the local optimal solution problem, and to seek a system that offers an overall global solution using competition and selection procedures. Results show that these mechanisms provide a good direction for the energy trading operations of a cogeneration system. This approach also provides a better guide for operation dispatch to use in determining the benefits accounting for both cost and the environment in a liberty market.

Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization

As we all know, there are a great number of optimization problems in the world. One of the relatively complicated and high-level problems is the vehicle routing problem (VRP). Dynamic vehicle routing problem (DVRP) is a major variant of VRP, and it is closer to real logistic scene. In DVRP, the customers’ demands appear with time, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. Owing to the complexity and significance of the problem, DVRP applications have grabbed the attention of researchers in the past two decades. In this paper, we have two main contributions to solving DVRP. Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO), which is the traditional Ant Colony Optimization (ACO) fusing improved K-means and crossover operation. K-means can divide the region with the most reasonable distance, while ACO using crossover is applied to extend search space and avoid falling into local optimum prematurely. Secondly, several new evaluation benchmarks are proposed, which can objectively and comprehensively estimate the proposed method. In the experiment, the results for different scale problems are compared to those of previously published papers. Experimental results show that the algorithm is feasible and efficient.

Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System.

This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.

Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering

One method of optimization that can be used for clustering is Ant Colony Optimization (ACO). This method is good in data clustering, but has disadvantage in terms of time and quality or solution convergence. In this study, ACO-based Pattern Reduction Enhanced Ant Colony Optimization (PREACO) method with a gaussian kernel function is proposed. First, it sets up initial solution. Second, the magnitude of pheromone is calculated to find the centroid randomly. With the initialized solution, the weight of the solution is calculated and the center of cluster is revised. The solution will be evaluated through a gaussian kernel functions. Function 'pattern enhanced reduction' is useful to ensure maximum value of pheromone update. Those steps will be conducted repeatedly until the best solution is chosen. Tests are performed on multiple datasets, with three test scenarios. The first test is carried out to get the right combination of parameters. Second, the error rate measurement and similarity data using Sum of Squared Errors is done. Third, level of accuracy of the methods ACO, ACO with the kernel, PREACO, and PREACO with the kernel is compared. The test results show that the proposed method has a higher accuracy rate of 99.8% for synthetic data, 93.8% for wine data than other methods. But it has a lower accuracy by 88.7% compared to the ACO.

Optimization of Enhanced Ant Colony Optimization Algorithm using Quad-Constrained FANTs and Multi-Criteria based BANTs in Mobile Adhoc Network

Portable specially appointed system comprise of hubs that are versatile. The hubs are interconnected with no brought together control. The correspondence between the hubs is completed by utilizing the versatile hubs which are going about as switches. Steering in a versatile specially appointed system is critical issues. The greatest test in systems is to discover the way between the correspondence end focuses. MANET hubs working on battery attempt to seek after the vitality productivity by lessening the vitality they devoured. In this paper just utilize the idea of Nature Inspired calculations (Swarm Intelligence Technique, for example, Ant Colony Optimization calculation. Fundamentally the convention is in view of swarm discernment. It is utilized to take care of complex issues by participation. The principle objective of this paper is to diminish overhead, upgrade the vitality, end to end delay. This paper just presents the utilize the idea of quad-constrained FANTs and multi-criteria based BANTs to course the information.

Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.

ANALYSIS AND SYNTHESIS OF ENHANCED ANT COLONY OPTIMIZATION WITH THE TRADITIONAL ANT COLONY OPTIMIZATION TO SOLVE TRAVELLING SALES PERSON PROBLEM

Ant Colony optimization is a heuristic technique which has been applied to a number of combinatorial optimization problem and is based on the foraging behavior of the ants. Travelling Salesperson problem is a combinatorial optimization problem which requires that each city should be visited once. In this research paper we use the K means clustering technique and Enhanced Ant Colony Optimization algorithm to solve the TSP problem. We show a comparison of the traditional approach with the proposed approach. The simulated results show that the proposed algorithm is better compared to the traditional approach.

An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem

In this paper, an enhanced ant colony optimization (EACO) is proposed for capacitated vehicle routing problem. The capacitated vehicle routing problem is to service customers with known demands by a homogeneous fleet of fixed capacity vehicles starting from a depot. It plays a major role in the field of logistics and belongs to NP-hard problems. Therefore, it is difficult to solve the capacitated vehicle routing problem directly when solutions increase exponentially with the number of serviced customers. The framework of this paper is to develop an enhanced ant colony optimization for the capacitated vehicle routing problem. It takes the advantages of simulated annealing and ant colony optimization for solving the capacitated vehicle routing problem. In the proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. Furthermore, an information gain based ant colony optimization is used to ameliorate the search performance. Computational results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on fourteen small-scale instances and twenty large-scale instances.