ant colony algorithm

Rotor imbalance in helicopters can lead to a flight quality degradation and a significant reduction in the lifespan of affected components. Considering the fundamental importance of safety and helicopter control being dependent on rotors, the propagation of this issue can result in undesirable events and catastrophic failures. Therefore, early detection of mass imbalance and misalignment plays a crucial role in reducing events and costs, and researchers in this field have recognized its significance. This paper focuses on experimental studies conducted on a coaxial rotor model helicopter to detect the presence of mass imbalance, misalignment, and a combination of them. A total of 320 tests were performed for eight conditions, including healthy, misalignment in the blades, and mass imbalances at various points on the blade's tip and center, within a rotational speed range of 880 to 1050 rotations per minute. Based on the time and frequency response of the system, signal features were extracted in the form of time-domain, frequency-domain, and time-frequency (using wavelet transform) graphs. In the next step, using Principal Component Analysis (PCA) in conjunction with the Ant Colony Optimization algorithm, 15 features containing more information were selected and inputted for classification into a neural network. According to the empirical evaluation, the proposed algorithm classifies and identifies the faults with a high-efficiency ratio. Thus, it can be utilized in condition-based maintenance of the rotorcrafts.

The shortage and restriction of drinking water and even water consumption in many water-scarce countries of the globe, including Iran, has become an essential and vital issue. This is one of the key reasons that people and communities maximize the development of crops. The best management of crop production, taking into account the requirement for water in the agricultural sector, is one of the acceptable options in nations like Iran, which is also active in the field of agriculture. On the basis of this, it is vital to look at various analytical, planning, and decision-making techniques. Additionally, taking into account and calculating virtual water can significantly improve the cultivation pattern and decrease the amount of water currently consumed; as a result, optimizing the cultivation pattern using virtual water calculations can be highly beneficial. Coherent management and accurate decision-making in a variety of areas will result from the usage and implementation of spatial information systems, and taking into account virtual water will enhance navigation patterns and decrease water consumption. In order to achieve the best allocation of the cultivation pattern and as a result, the correct use of water in the agricultural lands of Ben-Rood district in the operations of Varzaneh city, located in the southeast of Isfahan province, was achieved in the current research by using the decision-making system that was created with the capabilities of GIS and artificial intelligence, along with the virtual water calculations of some agricultural plants. Using the collective intelligence algorithm of the ant community (ACO) in conjunction with the spatial information system, it was possible to achieve this goal after examining the optimization methods in decision-making using environmental parameters. This was done by taking into account the virtual water of cultivation and the growth of plants in the irrigation networks of the agricultural lands of the mentioned sector. Finally, the findings of this study demonstrate that the amount of water consumed may be decreased to 37% of the initial amount after optimizing the allocation of land for the cultivation of specific crops in that area, based on the virtual water of the crops.

Wireless mesh networks are appropriate and cost-effective infrastructure for Internet but due to the limited scalability and capacity, a lot of research has been doing on new ways to improve these limitations such as optimization of scheduling, routing, etc. In this paper focusing on time division multiple access (TDMA) method, a new algorithm called a</strong>nt colony for l</strong>ink s</strong>cheduling in m</strong>esh networks (ALSM) based on Ant Colony algorithm is proposed which reduces the possibility of collision to zero by scheduling links. In this algorithm, we will try to size super frames and assign each link to a time slot in such a way that limitations are satisfied and finally, the end-to-end latency is minimized. In multi-input multi-output (MIMO) networks, we face two types of interference (weak and strong). In ALSM, the ant colony has been modified in such a way that the optimal timing of the links can be obtained by considering these two types of interferences. Our results show that ALSM algorithm can schedule links with shorter super frames compared to other recent algorithms.

An important issue in dynamic computer networks such as Internet networks, where the cost of connections varies continuously, is to create a traffic load balancing and increase the transmission speed of packets in the network, so that data packets are using paths with minimal congestion, as a result, one of the main approaches to solve routing problems and load balancing algorithms is based on ant - based algorithms using a novel approach based on optimization of multiple ant colony optimization, the purpose of this research is to present an appropriate routing algorithm in order to shorten and improve the path due to end - to - end delay parameters, packet loss rate, bandwidth and energy consumption rate, to reach a sense of data on the Internet systems. this method has been implemented in MATLAB software and shows the results of the improvement experiments in the mentioned parameters.

The Multiple Traveling Salesmen Problem (MTSP) is a generalized Traveling Salesmen Problem (TSP). The difference with the traveling salesmen problem is that all cities are visited by multiple salesmen, and each salesman from the city that initiated the move must go back to the same city, which is, in fact, suitable for modeling practical problems in real life than TSP. To solve MTSP with a few starting points, you need the minimum and maximum number of cities each salesman should visit. The total number of cities that salesmen go through should be equal to all cities. In this article, The hybrid Algorithm (IAC-PGA), which combines Parteno Genetic Algorithms (PGA) and Ant Colony (ACO) and uses the 2-opt local search method to improve the algorithm. This method provides full double displacement to improve the response. The main idea in this article is to use the PGA algorithm to search for the best number of cities visited as well as to obtain the starting point of each salesman using the genetic algorithm, and then to use the ACO algorithm to accurately determine the cities visited and the best tour for each salesman. The objective function for this problem is to minimize the distance traveled by all salesmen. For the purpose of analysis, the parameters of each algorithm are selected according to the number of experimental samples in the most appropriate case, and then the results of the algorithm are compared with other algorithms including PGA, Improved PGA (IPGA), Two-part Wolf Pack Search (TWPS), Artificial Bee Colony (ABC), and Invasive Weed Optimization (IWO). Statistics show the algorithm improvement for problem solving. The results of comparative experiments show that the proposed IAC-PGA algorithm is sufficiently effective in solving large-scale MTSP and is not worse than other algorithms on a small scale and performs better than the existing algorithms.

Intrusion detection systems for security in computer networks have been proposed to be crossed if the attacker from other security equipment, able to detect it and prevent it from advancing. One of the challenges of these systems, it is high dimensional data. In this study was to reduce the dimensions of a simple genetic algorithm with the length of the string variable we use. Then, according to selected characteristics, a meta-heuristic model for data classification, using ant colony algorithm offer. Classification model proposed by trying to divide the data into two samples is Hnjydh and Nahnjydh. The proposed method for evaluating the performance of database intrusion detection NSL-KDD than other data from the records of more realistic approach is used. The results of the experiments, the proposed method has better performance compared with other existing methods show.

Construction is one of the most important issues in a society economically, socially, culturally and so on. But the issue of quality in the construction projects requires discussion takes more scientific. The objective of this study is to investigate the optimization of the quality of construction projects and construction costs based on system reliability theory. Also, a real construction project is presented to evaluate the efficiency of the proposed model. In order to achieve this goal, a four-step algorithm is defined and proposed model using two methods M3AS and classic ant colony algorithm is solved. The results of both methods are compared. Parzin five-story residential building located on Zaferanieh Street is studied. The results indicated that M3AS innovative approach through local search is more capable than the classical ant colony algorithm for optimization. Most previous studies have not considered the importance of the quality of project. In this study, quality in the form of reliability at the time of completion of the work is quantified. The proposed model has been so overcome lack of expression and effectively using reliability theory. This model can automatically produce reliability and optimal cost of system with regard to structure function of reliability and non-linear function of cost-reliability. In this study, multi-objective optimization model based on reliability theory extends to decision-makers to determine and choose between cost and quality for construction projects. Since a major part of the annual budget allocated to construction projects, these projects are the most important component of the country's development that must be managed properly in terms of cost and quality and be completed according to the schedule. iT is clear that the status of the construction industry in terms of quality and cost should be much improved.

A theorical method is presented to analyse a way to increase generated power and energy efficiency in wind turbine by optimizing the Cut-in, Rated, Furling speeds by the ant colony algorithm. In this paper, we first discuss the wind turbine math modeling, which ultimately achieves the objective function. Then, by collecting the turbine statistical data, the computerized code of the neural network, wind turbine is modeled in MATLAB software and its results are presented as tables and diagrams. By examining the results of optimization by ant colony algorithm and comparing the data with the results of the research in the article on exergy analysis of wind turbines and Also the paper that describes the exergy analysis and optimization of wind turbines by genetic algorithm and searching algorithm optimizing the turbine, Finally, 65.63% increase in output power and 61.54% increase in energy efficiency was observed in the ant algorithm.

Vehicle routing problem has an important place in applied research. Finding the right path to solve Vehicles Routing Problem is one of the most significant measures taken in recent decades. In this routing method, each delivery must be made within time windows, and not to mention the fact that each customer must be visited only once. At the present paper, Ant Colony Algorithm Optimization for Vehicle Routing Problem with Time Windows and Capacity Constraint (ACOVRPTWCC) has been utilized. First, Ant Algorithm for Vehicle routing Problem with Time Windows and vehicle Capacity Constraint classic has been implemented. In this way, of 9 problem implemented through 100 customers in 6 problems, solutions are optimized. Then, algorithm is carried out on a new type of vehicle routing problem with time window. The most decrease of cost related to R206 problem by 32.72 percent minimization of cost. For any problem, on the average, 20 second time is needed.

Since precise assignment of vehicles to the routs in supply chain network has a great eect on the important results such as cost reduction, on time delivery of the products or services, and customer satisfaction indexes (CSI), the Vehicle Routing Problem (VRP) is one of the most important problems in the supply chain management. For the complexity of this problem in real world, researchers usually ignore some real conditions and restriction in modelling and solving this problem. In this paper, the Vehicle Routing Problem (VRP) considering real conditions, such as vehicle capacity and restriction for vehicles with their movement in some routes, is studied. At rst, the literature review and past studies are presented. Then, the considered problem will be illustrated completely and its parameters and variables will be dened. After that, the mathematical model of the problem is presented considering restriction for movement of the vehicles. This mathematical model will be codedinGAMSsoftware; becausethisproblemisknown as an NP-Hard problem, the mathematical model of the problem is solved just for the small-sized problems. A new model is also presented for the medium- and largesized problems based on ant colony (AC). The parameters of the ant colony are adjusted well in order to increase the eciency of the algorithm. Finally, some diversity test problems are designed considering the important and eective parameters. These test problems are solved in order to evaluate the eciency of the proposed algorithm, and the result shows that the proposed algorithm has a good performance in solving the test problems. Performanceoftheproposedalgorithmisalso compared with two power algorithms tabou search (TA) and genetic algorithm (GA) in solving the test problems. Results show that the proposed algorithm has better performance both in optimality and running time comparison of two other algorithms.

The optimization of the utility consumption (steam and cooling water) is an important part for the optimization of process operating cost. To this end, the heat exchanger networks (HEN) are investigated in a process manner. Different approaches are presented for the synthesis of HEN where stochastic algorithms have received more attention recently. In this paper, a combination of Ant Colony Optimization (ACO)-- as a stochastic algorithm-- and Nelder-Mead simplex algorithm-- as a deterministic algorithm-- are used for the optimization of heat exchanger networks. ACO was presented originally for problems with discrete variables, and it is used here in the same way. To prove the effectiveness of the presented method, two case studies are undertaken, and their results are compared with that of other methods.

Promptness of relief groups and especially, of inter- cities ambulances has a vital role in their performance during unpredicted disasters. In this regard, optimal routing of these groups seems necessary in order to cover maximum population centers. For this purpose, the use of artificial intelligence and the so-called “new routing algorithms,” and its localization among inter/ intra- cities sections, based on their extent and spread, can be an efficient way for efficient urban management and relief organization. Therefore, the aim of this study was to show the practical application of ant colony algorithm for optimizing routing and minimizing the travelled distance. Ultimately, to demonstrate the capabilities of this algorithm, it was compared with the genetic algorithm. In this research, the case study was performed on over 29 urban and rural points, originated in Parsabad city, in MATLAB and shown in the GIS environment. The proposed model in this paper can not only be used to analyze the issue, but it also can be used to optimize the routing of distribution of basic goods in cases of natural and human disasters, traffic problem, and so on. Need to note that in the proposed method, the Rolette wheel Selection method is used for random selection of the neighborhoods.The results showed that due to the limited area of the case study, time and quality of achieving to optimal route in ant colony algorithm were calculated 0.19 ms faster than the genetic theory, whereas, given the movement of 30 ants, the time required to arrive to the scene by the ambulances for ant colony algorithm and the genetic algorithm was calculated 19' 45'' and 24' 15'', respectively.