Modified Ant Colony Optimization Research Articles

Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm

To improve the workshop production efficiency of textile enterprises and balance the total operating time of all machines in each operation, this paper proposes a modified algorithm based on the combination of the ant colony optimization (ACO) algorithm and production products, which we call the product ant colony optimization (PACO) algorithm. The local pheromone update rule in the ACO algorithm is modified through the close relationship between textile machinery and production products in the textile workshop; the pheromone is then introduced into production products based on the constraints of the textile machine. A heuristic function is designed to improve the utilization rate of textile machines to increase the heuristic value of machines that are less frequently used in the algorithm iteration process. In addition, this paper combines the convergence speed and the global search ability of the algorithm with a designed variable pheromone evaporate parameter. The comparison among the initially designed PACO algorithm, the separately modified PACO algorithm, and the integratively modified PACO algorithm demonstrates that the proposed enhancement effectively addresses scheduling issues in textile flexible workshops and various workshops with similar constraint conditions.

Enhancing the Lifetime of WSN Using a Modified Ant Colony Optimization Algorithm

Wireless sensor networks (WSNs) have been extensively used in various fields, such as health, defense, education, and industrial applications, to collect and transmit environmental data to the base station. However, energy efficiency is a significant challenge in WSNs, as data transmission is typically limited to a single route, leading to excessive energy consumption by the nodes along that route. This can lead to a decrease in the network's overall efficiency and effectiveness. To address this issue, this study aims to extend the lifespan of WSNs by optimizing route selection based on three variables: residual node energy, distance to the base station, and number of shared neighbors. In this paper, the authors propose three systematic approaches, namely Energy-Aware ACO Routing (EACO), Cost-Effective ACO Routing (CEACO), and Cost-Efficient Node Replacement Strategies ACO (CERACO), to enhance the lifetime of WSNs. These approaches consider various factors such as cost, energy consumption, replacement, and reliability. The paper provides a practical guide for researchers and practitioners to overcome the challenges related to energy efficiency and cost-effectiveness in WSNs. Experimental results demonstrate that the first dead node occurs later with the proposed methods than with the traditional Ant Colony Optimization (ACO) algorithm.

Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section

The railway is one of the most energy-efficient modes of transport, helping to enhance the environment and public health in cities and agglomerations. In this paper, the authors raise the issue of the construction of an underground railway route in Wrocław (Poland) to allow the organization of the suburban rail system in the agglomeration. There are many concepts for the construction of this route, but so far none has been realized. Therefore, it is important to design the route properly. Here, five options for this tunnel are considered and evaluated. To make such an evaluation, the authors construct a modified ant colony optimization algorithm (ACO). The "classic" algorithm considers the determination of the shortest route. The modification of the algorithm will allow a more accurate analysis of the issue, taking into account more parameters than just the length of the route. These are the location of traffic generators in the city center, the number of inhabitants neighboring the stations, and the number of tram or bus lines integrated with the railway. The presented method and exemplary case study should allow for the evaluation, introduction, or development of the city railway.

A Modified Ant Colony Optimization Algorithm with Pheromone Mutations for Dynamic Travelling Salesman Problem

Ant Colony Optimization (ACO) is an algorithm based on Meta-heuristics and swarm intelligence, taking inspiration from the exploring behavior of social insects like ants. Some very challenging optimization problems, like the vehicle routing problem, have been solved by Ant colony optimization. But, the Standard ACO algorithm has problems like trapping in local optimum. This paper discusses an improved version of the ACO algorithm, known as Modified ACO (M-ACO). The main motivation of the proposed algorithm is to use pheromone mutation techniques, such as swap and random, in dynamic environments, to overcome the problems of standard ACO and solve the Travelling Salesman Problem, which is a well-known NP-hard problem. Mutation allows better searching and exploring in other areas, for better results. Also, the exploitation is increased that uses local information to search local regions more intensively for finding minimum distance and increasing the rate of convergence. Evaporation-based Ant colony optimization (E-ACO) framework and population-based ant colony optimization (P-ACO) framework are the two groups into which the algorithms under study are separated. Both frameworks are evaluated on dynamic environments produced using TSPLIB base instances and updated for pheromone values using mutation technique. Experiments and Statistical tests were conducted on the TSPLIB datasets such as pr439 and lin318, in dynamic environments and on comparing ACO with M-ACO and P-ACO, better results were obtained for M-ACO and P-ACO. Metrics like offline have been considered to measure algorithm performance and behavior.

改进蚁群优化算法的最大似然DOA估计方法

In order to alleviate the huge amount of calculation when using the continuous domain ant colony optimization (ACO) algorithm to handle the maximum likelihood (ML) estimation problem, we propose a maximum likelihood direction of arrival (ML-DOA) estimation method based on modified ant colony optimization (MACO) algorithm. Firstly, MACO algorithm adopts the elite reverse learning strategy to obtain a better initial solution group. Secondly, the optimization method of ant colony is conducted by combining global cross-neighborhood search and local search of Gaussian kernel function to expand the algorithm search space and accelerate the convergence speed. Finally, the nonlinear global optimal solution of ML estimation method is obtained. Simulation results show that, compared with ML estimation methods based on particle swarm optimization (PSO) algorithm and ACO algorithm, the convergence speed of the ML-MACO algorithm is 4 times faster than that of the ML-ACO algorithm, the computational load is 1/3 that of the ML-ACO algorithm, the resolution success rate is higher than the ML-PSO algorithm and the ML-ACO algorithm, and the estimation error is less than the ML-PSO algorithm and the ML-ACO algorithm. The ML-MACO algorithm maintains the excellent estimation performance of the ML algorithm with lower computational effort, better convergence performance, and higher estimation accuracy.

Enhancing Network Lifetime of WSNs through the Implementation of a Modified Ant Colony Optimization Algorithm

Energy efficient routing has been the mainstay of research in wireless sensor networks. With rising utility of WSNs in a broad range of applications, a significant volume of research contributions are targeted towards obtaining an energy efficient routing with a good quality of service (QoS) metric. In recent times, the utility of evolutionary algorithm for such multi-objective problems has been on the rising trend as evolutionary models closely mimic the real time scenario of any problem optimization. In this research work, a modified ant colony optimization (M-ACO) has been proposed and implemented for optimization in terms of energy conservation and thereby to extend the network lifetime of the network

Modified Ant Colony Optimization for Human Recognition in Videos of Low Resolution

Privacy protections for people filmed in public settings is a prerequisite to widespread camera use. For this reason, low-resolution videos are used from which specific people can be reliably obscured. Since the human region in low-resolution videos comprises of so few pixels and so little information, human detection is more challenging there than it is in high-resolution videos. With the current state of affairs, one of the most important challenges is tracking a target from lower resolution movies. Identification or monitoring of persons in low-resolution movies has become a common issue in many domains due to a lack of appropriate data. This study presents a novel people-detection algorithm that makes use of low-resolution film to overcome the aforementioned problem. In the first stage, a three-step procedure is executed, the video data gathered from low-resolution videos from various form of data is considered. The captured video is separated into frames and transformed from RGB to gray-scale. Local Binary Pattern (LBP) method is used in the second phase to accomplish background subtraction. Thirdly the feature extraction is performed in which histogram of optical flow (HOF)and some of the features are extracted in the form of eigen values. Finally these features are optimized using Modified Ant Colony Optimization (MACO) model to remove the unwanted features and select global features. Finally, classification operation is performed using Support Vector Machine (SVM) classifier to recognize the person from lower resolution videos. The results obtained using the implemented MACO-SVM obtains good results when compared with existing techniques with rate of accuracy 91.46% for soccer dataset, 90.8% for KTH dataset and 89.75% accuracy using VIRAT dataset.

Modified Ant Colony Optimization with Route Elimination and Pheromone Reset for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window

The vehicle routing problem (VRP) has many applications in goods distribution and goods transportation. Today, many companies have requirements for VRP with multiple pickup and multiple delivery within due time. This problem is called multiple pickup and multiple delivery vehicle routing problem with time window (PDPTW). PDPTW has many constraints and ant colony optimization (ACO) has been used to solve it although ACO creates too many infeasible routes. Moreover, it often gets trapped in local optimum. To solve these problems, this paper proposed an improved ACO by using the route elimination technique and the pheromone reset technique. The ACO with route elimination technique, it has proven to solve the PDPTW problem with increased performance. The proposed technique was tested on datasets from the Li & Lim’s PDPTW benchmark problems and provided more satisfactory results compared to other ACO techniques.

Dispersed-ply design and optimization to improve the brittle flexural behaviour of composite laminates

This work aims to improve the flexural behaviour of unidirectional fibre-reinforced laminates by means of coupling an optimization procedure for quasi-isotropic configurations with the design space opened by dispersed-ply orientations. The design approach consists of finding suitable alternatives to traditional laminates (with fibre orientations limited to 0°, ±45∘, and 90°), while maintaining their stiffness characteristics. This strategy isolates the interlaminar response as the objective function that is optimized to improve their flexural behaviour. To this end, a modified Ant Colony Optimization was implemented and geared towards optimizing the interlaminar stress profile, allowing plies at every possible 5° orientation, with the ultimate goal of delaying delamination. To validate the approach, a traditional reference laminate and derived fully dispersed designs were experimentally tested. The correlated responses show that it was not possible to improve flexural resistance. However, the typical flexural brittleness of laminates can be modified into a pseudo-ductile behaviour.

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes' optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets.

Dynamic Economic Dispatch with Valve-Point Effect Using Crow Search Algorithm

This paper presents a method based on meta-heuristic to solve Dynamic Economic Dispatch (DED) problem in a power system. In this paper, Crow Search Algorithm (CSA), which is one of the heuristic methods is proposed to solve the DED problem in a power system. In this study, line losses, generation limit values of generators, generation-consumption balance, valve-point effect and ramp rate limits of generator are included as constraints. The proposed algorithm was implemented on two different test cases. Finally, the CSA results were compared with the results of well-known heuristics in the literature such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Symbiotic Organism Search (SOS) algorithm, Artificial Bee Colony (ABC) algorithm, Simulated Annealing (SA), Imperial Competitive Algorithm (ICA), Modified Ant Colony Optimization (MACO) algorithm. The results show that the proposed algorithm has a better operating cost. With the results of the algorithm proposed in the test system 1, a profit of $2,056,5931 per day and $751,751,4815 per year is obtained. It is seen that with the results of the algorithm proposed in the test system 2, a daily profit of $12,279,7328 and a yearly profit of $4,482,102,472 are obtained. Test systems are operated by using less fuel with the results of the proposed algorithm and thus the harmful gas emissions released by thermal production units to the environment are also reduced.

Modifikasi Algoritma Ant Colony Optimization Dalam Menentukan Rute Pengisian Mesin ATM

The TSP problem is known as a non-deterministic polynomial-hard (NP-Hard) problem. In its solution, TSP can be solved using swarm intelligence algorithms such as Artificial Bee Colony (ABC), Partical Swarm Optimization (PSO), dan Ant Colony Optimization (ACO). In this study, TSP settlement was carried out using the ACO algorithm because the amount of data was less than 80 data. In addition, modifications were made to the ACO algorithm with the aim of optimizing the probability in node selection by put in Fuzzy C-Means algorithm into the ACO algorithm. 
 Based on the result application of the Modified Ant Colony Optimization algorithm, the distance covered is 101.712 Km when the parameter optimization has been carried out, with parameter values alpha = 5, beta = 0, rho = 0.3, number of ants = 31, dan maximum iteration = 100. Where each parameter has its own role, such as the Intensity Controlling Constant (alpha) which makes the ants only care about the pheromone intensity value without caring about the distance value between points so that the diversity of the paths found gets smaller when the value alpha gets bigger, Visibility Controlling Constant (beta) which affects the diversity of routes produced by each ant where when beta = 0 then the route chosen by each ant varies and when beta > 0 has the possibility for the route that has been selected to be re-elected by other ants so that the diversity of routes found getting smaller, while for the Ant Track Control Constant (rho) it has an influence in determining the next destination point when the value of gets bigger. In addition, the Modified Ant Colony Optimization algorithm has the advantage of accelerating convergence in finding the shortest route.

Thresholding Segmentation of Skin Lesions with Modified Ant Colony Optimization Algorithm

Image segmentation is the process of breaking up a digital image into many segments, each containing pixels with similar properties. Generally speaking, image segmentation aims to provide an easier-to-analyse and more understandable representation of a picture. Numerous techniques have been presented since this specific topic of picture segmentation was first introduced as a classical issue. The primary content of an image is broken down during the image segmentation process in order to make its representation simpler. Segmenting images involves the separation of pixels into different classes to enable analysis of objects within the image. Multithresholding is a commonly used method for segmentation, but the challenge lies in finding the optimal threshold that accurately segments each image. Metaheuristic methods are high-level procedures that aim to solve optimization problems by searching for acceptable solutions. Recently, researchers have become increasingly interested in using metaheuristics to address image segmentation as an optimization problem. By combining traditional approaches to image segmentation with metaheuristics, researchers have been able to enhance accuracy in various applications.

Number and Operation Time Minimization for Multi-UAV-Enabled Data Collection System With Time Windows

In this article, we investigate multiple unmanned aerial vehicles (UAVs)-enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time-constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated, considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer nonconvex problem, we decompose the problem into two subproblems: 1) UAV trajectory and 2) hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed the MACO-based algorithm, is given by leveraging the MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm.

Improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

Diabetic Retinopathy is a retinal eye disease that affects people with Diabetes Mellitus. DM is metabolic disorder and it is caused by the high glucose level in blood. This leads to eye deficiency called Diabetic Retinopathy (DR). In this paper, we proposed iGWO algorithm for the detection and diagnosis of DR stages and Region growing technique is used for segmentation of the images. Various preprocessing techniques are followed for the better enhancement of the images. An improved Grey Wolf Optimization (iGWO) algorithm are proposed to obtain the global optimum. Convnets are utilized for DR categorization. The iGWO-FFOCNN model is compared with existing technologies like SVM-GSO (Support Vector Machines – Glowworm Swam Optimization), PSO-CNN (Particle Swarm Optimization-Convolutional Neural Networks), CNN (Convolutional Neural Networks), DCNN-EMFO (Deep Convolutional Neural Networks- Enhanced Moth Flame Optimization) and MACO-CNN (Modified Ant Colony Optimization–CNN). The APTOS DR Dataset utilized for the proposed metodology. The effort is made for identifying the DR stages by using iGWO. Finally, the results confirm that iGWO-FFOCNN technique yields good performance than compared to the existing techniques in relation to F-measure, precision, recall and also in DSC (Dice Similarity Coefficient), JSC (Jaccard Similarity Coefficient) and time period.

Persistent Monitoring for Indoor Farming using Static and Mobile Sensors

This paper considers the use of static and mobile sensors (quadrotors) for persistent monitoring in an indoor farming scenario. The focus is on autonomous navigation of the quadrotors to monitor the coverage holes generated by a number of broken static sensors. To that end, a three-layer strategy is proposed by (i) dividing the coverage holes into several region-of-interests (ROIs); (ii) selecting the quadrotor(s) responsible for monitoring each ROI using a novel Modified Ant Colony Optimization; (iii) designing coverage control with time-varying density function for ensuring persistent monitoring. Simulation results demonstrate the efficacy of the proposed strategy in persistently minimizing the coverage holes over time.

A Framework for Optimizing Software Regression Test Case based on Modified-Ant Colony Optimization (M-ACO)

Software Regression Testing (SRT) helps with the modified code to ensure that no other new defects have been trained and significantly impact the existing code. SRT is a time and cost taking process. Test Case Prioritization (TCP) helps to reschedule test cases (TC’S) and picks according to the prioritization, which allows the software testers to maximize the number of faults detected within a given time frame. Ant Colony Optimization (ACO) is an optimization algorithm motivated by the searching behavior of insects. This paper proposes a simple framework for the novel Modified-ACO (m-ACO) to prioritize the TC’s. m-ACO is planned as the adjusted type of the ACO method, which reschedules the execution sequence of TC’S by changing the wonder of characteristic insects for choosing their food source. The proposed Framework will help the software testers save their time to execute SRT TC’s. It has been tested for Zasta Billing Web Application and compared the time taken to complete the SRT manually and using the proposed Framework.The proposed Framework helps to reduce the time taken from 90 days to 45 days to achieve the SRT.

Construction of Efficient Wireless Sensor Networks for Energy Minimization Using a Modified ACO Algorithm

In this paper, we have proposed different deployment strategies and have applied area-wise clustering along with modified Ant Colony Optimization to minimize energy consumption. Background: Previously, some deployment strategies were used to enhance the lifetime of WSN. In our research, we have applied some novel deployment strategies like random, spiral, and S-pattern along with a novel area-wise clustering process to get better results than the existing literature, as shown in Table 4. Objective: The main objective of the research article is to enhance the lifetime of Wireless Sensor Network with the help of different deployment strategies like random, spiral, and S-pattern). A novel clustering process (i.e., area-wise clustering), and a Meta-heuristic algorithm (modified ACO) are applied. Methods: We have applied different methods for deployment strategies (random, spiral, and S-pattern). A novel clustering process (i.e., area-wise clustering), and a Meta-heuristic algorithm (modified ACO) are applied to get the desired results. Results: Random Deployment: 11.15 days to 15.09 days. Spiral Deployment: 11.25 days to 15.23 days. S-Pattern Deployment: 11.33 days to 15.33 days. Conclusion: In this paper, efficient Wireless Sensor Networks have been configured considering energy minimization as the prime concern. To minimize the energy consumption, a modified ACO algorithm has been proposed. In our work, the minimization of energy consumption leads to an increment of the lifetime of WSN to a significant margin theoretically. The obtained result has been compared with the existing literature and it has been found that the proposed algorithm produced a better result than the existing literature.

A Novel Hybrid Algorithm of Max-Min Ant System with Quadratic Programming to Solve the Unit Commitment Problem

In this paper, we propose a novel hybrid algorithm based on MAX-MIN Ant System version of ant colony optimization coupled with quadratic programming (MMAS-QP). Quadratic programming is used to optimize the Economic Dispatching process and MMAS for planning the switching schedule of a set of production units. The algorithm is implemented in MATLAB software environment for two systems, one is 4 generating units running for 8 hours, and the other is 10 generating units running for 24 hours. The impact of heuristic parameters on the behavior of the algorithm is highlighted through the parameters setting. Results obtained shows improved solution compared to several methods such as Modified Ant Colony Optimization (MACO), particle Swarm Optimization combined with Lagrange Relaxation (PSO-LR), Swarm and Evolutionary Computation (SEC), Particle Swarm Optimization combined with Genetic Algorithm (PSO-GA). The proposed method improves sufficiently the quality of the solution as well as the execution time.

Modified ant colony optimization based PID controller design for coupled tank system

Most of the optimization algorithm suffers from slow convergence and poor solution accuracy due to nonlinearity characteristics of the problem. This paper proposes a meta-heuristic method, namely the modified Ant Colony Optimization (m-ACO) technique to address these issues. The conventional ant colony optimization algorithm is improved by using multiple random initializations to increase the probability of getting a better start population and hence increase the possibility to achieve near-global optimum. The efficacy of the proposed algorithm is tested on classical benchmark functions and on the PID controllers. The performance of controllers depends on the selection of tuning parameters that are optimized to obtain an optimum solution. The m-ACO technique is used to optimize parameters of PID controller for Coupled Tank System. It is one of the benchmark control problems due to its nonlinear characteristics. The obtained results of the proposed algorithm are compared with the Zeigler Nichols tuned PID controller, and PSO tuned PID controller in terms of transient performances including rise-time, peak overshoot and performance parameters such as Integral Square Error (ISE) and Integral Absolute Error (IAE). The proposed method is also tested for showing effectiveness in set-point tracking and disturbance rejection.