Modified Ant Colony Optimization Algorithm Research Articles

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

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

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.

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.

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

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.

Efficient Vehicle Routing Solutions: A Metaheuristic Approach

Efficient vehicle directing is fundamental for smoothing out strategies processes, cutting costs, and raising consumer satisfaction. The current study explores the usage of metaheuristic procedures to handle multifaceted vehicle directing issues that emerge in various fields, including public transportation frameworks, transportation logistics, and delivery services. The study is explicitly worried about the application and investigation of a modified version of Ant Colony Optimization (ACO), a metaheuristic algorithm that draws motivation from ant foraging behavior. To increment solution quality, robustness, and convergence speed, the modified ACO algorithm incorporates enhancements like multi-colony systems, local search heuristics, and dynamic pheromone update instruments. This examination shows the adequacy and adaptability of the recommended approach in delivering ideal or almost ideal routing arrangements through mathematical modeling, theoretical analysis, and empirical assessments utilizing real-world datasets. The outcomes add to the corpus of information on optimization algorithms and give valuable exhortation to working on the manageability and proficiency of vehicle routing operations.

Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.
  

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.

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach.

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.

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.

A fractional order PID controller using MACOA for indoor temperature in air-conditioning room

Aiming at controlling indoor temperature of each air-conditioning room equipped with fan coil unit (FCU), this paper proposes a hybrid control system to deal with such problem, in which a fractional order PID controller for indoor temperature (IT-FOPIDC) and a three-position controller for supply air volume (SAV-TPC) are used to maintain steady indoor temperature, respectively. For the key problem of tuning parameters of IT-FOPIDC, a modified ant colony optimization algorithm (MACOA) is reconstructed, and it is tested to be feasible by the simulation results. Thus, an algorithm using this MACOA is designed to adaptively solve the satisfied values of five parameters of IT-FOPIDC to further upgrade the control quality of indoor temperature from software. In the meanwhile, SAV-TPC manipulates the corresponding flow rate of supply air discharged from FCU to provide the dynamic variation of cooling or heating load to an air-conditioning room. So these two controllers work together for achieving the desired indoor temperature. The corresponding numerical simulation of this hybrid control system is carried out and the results indicate that this algorithm using MACOA is reliable to tackle the key problem of tuning parameters of IT-FOPIDC so that the control quality of indoor temperature is obviously improved. Moreover, for the same IT-FOPIDC, the control performance of the other intelligent optimization algorithms is compared to that of the proposed algorithm using MACOA under the same scenario, simulation results also show the latter is superior to the former in terms of analyzing the dynamic responses of indoor temperature, which may offer the corresponding reference guides for regulating the key performance parameters in HVAC& R engineering.

Cache-aware mobile data collection schedule for IoT enabled multi-rate data generator wireless sensor network

The energy hole problem is a major concern in the wireless sensor network (WSN) given the uneven energy depletion of the power-constrained sensor nodes. Recently, several studies have incorporated a mobile data collector (MDC) to collect data from sensor nodes where the MDC reaches a set of data collection points (DCPs) to collect data from nearby nodes. This strategy has resolved the underlying cause of unbalanced energy consumption by avoiding the long chain of multi-hop routing. However, in most of the existing protocols, sensor nodes information is relayed to the Base Station (BS) to nominate MDC sojourn points and travel sequence. Such a centralized approach results in a substantial exchange of messages among sensor nodes causing high energy consumption. Moreover, these approaches perceived a uniform data generation rate at sensor nodes while ignoring the limited buffer cache capacity during the MDC path design. In this context, this paper proposes a distributed MDC sojourn point nomination method that significantly improves the message complexity and energy consumption of the WSN. The selected DCPs information is relayed to the BS where a modified Ant Colony Optimization algorithm is applied to construct the MDC traversal path. The performance of the proposed protocol is extensively analysed with some related existing schemes in terms of network parameters like energy efficiency, network lifetime, packet delivery ratio, and end to end delay. Moreover, the scalability of the proposed scheme is evaluated by varying the number of MDC and sensor nodes in the network.

Modified ant colony optimization algorithm for solving the vehicle routing problem with a given load capacity

The article presents a mathematical model of the Vehicle routing problem with loading constraints in the form of a mathematical programming problem. A modification of the Ant colony optimization algorithm is presented to solve the problem. The main idea of the modification is to use the probabilistic rule of returning the agent (ant) to the depot before full unloading. The source of such behavior of the agent can be the concept of «fullness» of the ant. The computational experiment for comparing the classical and modified Ant colony optimization algorithms is presented. Testing was carried out according to two schemes: intensive testing and cross-testing. The conclusions about the efficiency of the proposed algorithm are presented.

Urban expansion simulation from the perspective of land acquisition-based on bargaining model and ant colony optimization

As a major method to serve the demand for land requirement in Chinese urban construction, land acquisition has been intensified since the speeding up of urbanization. However, clashes arise during the process of land acquisition, out of conflicts of interests, which to some extent have affected the development and direction of urban expansion, as well as social harmony and stability. Therefore, the urban expansion simulation should be based on smooth land acquisition technique. In this study, urban expansion was simulated from the perspective of land acquisition, based on coupled bargaining model and modified ant colony optimization (ACO) algorithm. First, the bargaining model with fairness preferences should be set up for the government and farmers, to search for the candidate areas for urban expansion, both sides of which could reach a consensus without conflicts of interest. Then, urban expansion simulation is applied to all the candidate areas, taking advantage of the modified ACO algorithm. The model considered the built-up area in Wuhan city as the demonstration area and simulated conditions of land use in 2016 and 2026. The result showed that the coupled model could simulate decision-making behavior of the government and farmers in land acquisition veritably, so as to protect farmers' economic interests, with an increase of over 50% on average, and ensure government's appropriate profit. Moreover, the simulation accuracies of the coupled model was found to be better than that of the traditional cellular automata model, and the Kappa coefficient was 0.65, which supports the effectiveness of the model in simulating urban expansion. Further, it was estimated that the urban land use of Wuhan will cover 516.22 km2 in 2026, and the southeastern part of the city will be the hot spot area of urban expansion.

Land use Land Cover Mapping using Modified Ant Colony Optimization Technique

Land use Land cover classification is an important aspect for managing natural resources and monitoring environmental changes. Urban expansion becomes one of the major challenges for the administrator. The LANDSAT 8 images are processed using the open source GRASS (Geographic Resource Analysis Support System). Unsupervised classification technique based on Ant Colony Optimization (ACO) algorithm has been modified and proposed as Modified Ant Colony Optimization (MACO) for LULC classification. In order to improve the classification accuracy of the proposed algorithm, we have combined spatial, spectral and texture features to extract more information of homogeneous land surface. The classification accuracy of the proposed algorithm has been compared with other unsupervised classification methods such as k-means, ISODATA and ACO algorithms. The overall classification accuracy of proposed unsupervised MACO algorithm has been increased by 11.24 %, 8.24% for open space and water bodies class, respectively as compared to ACO algorithm.

Optimized evolutionary algorithm and supervised ACO mechanism to mitigate attacks and improve performance of adhoc network

Optimization is the procedure of discovering optimal solution or results under particular circumstances. Typically, optimization is utilized for maximization or minimization of values of functions; it can be local or global optima. Discovering exact solutions to the issues is NP-hard. This type of complicated issue needs exponential quantities of computation power as well as item and quantity of decision parameters rise. For overcoming the issues, research scholars have suggested Evolutionary Algorithm (EA) protocols as a way for searching near-optimal solutions. In this work, Ant Colony Optimization (ACO) is used combined with the power of faith on effective factors in distance of trustworthy optimum routing in WSN. ACO has its basis in the foraging behavior of ants who look for the shortest route between then nest and the food source. The energy efficient node random trust based on routing network is designed in a safe and random routing mode that reveals all fingerprints and useful load distribution. The details are combined with confidence choices, using a modified ACO algorithm. The ACO application, makes this method a stronger and faster with a metaheuristic algorithm compared with the other routing methodologies. Modified ACO method, exercise as a parameter for calculating the energy levels of terminals used fitness route planning its organization. Proposed work shows improvement in the network performance under Dos and DDoS attack in terms of amount of data loss, data delay.

A Modified Ant Colony Optimization Algorithm for Power Scheduling

The purpose of this work is to solve the problems related to the power system in an efficient manner by assigning the optimal values. To meet the demand loads with good quality and quantity is a challenging problem in the field of the power system. Artificial Neural Network is realized in the field of energy management and load scheduling. The Backpropagation algorithm is used for the training purpose. It has the aspects of the quick meeting on the local bests but it gets stuck in local minima. To overcome this drawback an Ant Colony Optimization algorithm is presented to allocate optimal output values for the power system. This has the capacity for searching the global optimal solution. Present work modifies the ant colony optimization algorithm with backpropagation. This hybrid algorithm accelerates the network and improves its accuracy. The ant colony optimization algorithms provide an accurate optimal combination of weights and then use backpropagation technique to obtain the accurate optimal solution rapidly. The result shows that the present system is more efficient and effective. These algorithms significantly reduce the peak load and minimize the energy consumption cost.

Parameter Tuning in MACO for Actual Road Conditions

With the increase in traffic volume day by day, cities are facing the problem of extreme congestion in most of the developing countries. The congestion on the road leads to increase in travel time and travel cost as well as having a significant impact on the health of people. This paper aims to provide the parameter tuning in the existing modified ant colony optimization (MACO) algorithm using particle swarm optimization (PSO) algorithm to handle the congestion for actual road conditions. The performance of the MACO algorithm depends on the value of its parameters. Having a very large combination of these parameter values, a selection of the optimal values for these parameters is done using trial and error method. Therefore, we have proposed an algorithm based on PSO algorithm to find an optimal combination of the parameters of MACO under actual road conditions. MACO algorithm works under the assumption that all roads are in working condition, whereas the proposed work finds the best path even when the roads are temporarily blocked may be due to an accident or permanently blocked due to road construction. The experiments have been done for the network of North-West Delhi, India. It was found that the travel time is reduced significantly after using the optimal combination of values of parameters.

Modified Ant Colony Optimization Algorithm for Multiple-vehicle Traveling Salesman Problems

In this work, we extended the original Traveling Salesman Problem (TSP) to cover not only the case of multiple vehicles but also to constrain the minimum and maximum numbers of cities each vehicle can visit. Our algorithm is a modified Ant Colony Optimization (ACO) algorithm which has the ability to avoid local optima; our algorithm can be applied to transportation problem that covers either a single vehicle or multiple vehicles. To the original ACO, we added a new reproduction method, a new pheromone updating strategy, and four improved local search strategies. We tested our algorithm on several standard datasets in the TSP library. Its single-vehicle performance was compared to that of ant system (AS) and elitist ant system (EAS) algorithms. Its multiple-vehicle performance was evaluated against that of ant colony system variants reported in the literature. The experiments show that our proposed ACO’s single-vehicle performance was superior to that of AS and EAS on every tested dataset and its multiple-vehicle performance was excellent.

A modified Ant Colony Optimization algorithm to solve a dynamic traveling salesman problem: A case study with drones for wildlife surveillance

Abstract This study presents a novel Ant Colony Optimization (ACO) framework to solve a dynamic traveling salesman problem. To maintain diversity via transferring knowledge to the pheromone trails from previous environments, Adaptive Large Neighborhood Search (ALNS) based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes available in the literature. Numerical results indicate that the proposed immigrant schemes can handle dynamic environments efficiently compared to other immigrant-based ACOs. Finally, a real life case study for wildlife surveillance (specifically, deer) by drones has been developed and solved using the proposed algorithm. Results indicate that the drone service capabilities can be significantly impacted when the dynamicity of deer are taken into consideration. Highlights Proposed a novel ACO-ALNS based metaheuristic. Four variants of the proposed metaheuristic is developed to investigate the efficiency of each of them. A real life case study mirroring the behavior of DTSP is developed.