Ant Colony Optimization Algorithm Algorithm Research Articles

Optimal Allocation of Water Resources Using Agro-Economic Development and Colony Optimization Algorithm

Water is an irreplaceable commodity with a high economic value. Today, water scarcity is the biggest challenge in the world, and the crises arising from lack of freshwater resources are serious threats to sustainable environmental development and human health and welfare. As the problems grow in complexity and dimensions, it becomes less possible to solve them with conventional optimization methods or explicit computational methods in a proper amount of time and with the currently limited computation memory, making it very difficult to achieve an optimal absolute solution. In this regard, metaheuristic algorithms that are generally inspired by nature are used in complex optimization problems. The Pishin Dam is an important dam in the eastern basin of Iran in the south of Sistan and Baluchestan province. This region faces severe water stress due to very low precipitation and very high evaporation on the one hand and the growing increase in urban, agricultural, and industrial demand on the other hand. The water development plans executed by the Ministry of Energy in the studied region influence water supply and demand profoundly. This research investigated the optimal allocation of water resources of this dam under management scenarios using the metaheuristic technique of the ant colony optimization algorithm (ACO). The results showed that the best value of the objective function was 82.3658 million m3. When applying the scenario of developing the cultivation area, the best value was obtained at 67.1258, which was significantly different from the base state. The results show that the ACO algorithm is suitable for the water resources of the Pishin Dam and can be used in planning and policymaking.

Dynamic planning method of evacuation route in dam-break flood scenario based on the ACO-GA hybrid algorithm

As the major water storage infrastructure, the failure of a reservoir dam will cause significant losses to people's lives and property. A scientific and reasonable evacuation route is one of the essential measures to reduce casualties. Thus, it is of great significance to realize the dynamic planning of evacuation routes in dam-break flood scenarios to reduce the risk of dam failure. This study employed a physically based mathematical model (Breach model) and a two-dimensional hydrodynamic model to simulate dam-break flood routing. Further, a road network construction method was proposed based on the graph theory and flood routing information. The ant colony optimization algorithm (ACO) was improved by the backtracking, the improved heuristic function, and the elite ants, and the genetic algorithm (GA) was optimized according to the characteristics of flood avoidance route planning. Accordingly, an improved ant colony-genetic optimization hybrid algorithm (ACO-GA) was proposed. Compared with the basic ACO algorithm, the correct rate of simulation results of the ACO-GA hybrid algorithm was improved by 34 %, the average number of iterations was reduced by 18.3 times, and the optimization ability was markedly enhanced. Based on the constructed dam-break flood scenario and the ACO-GA hybrid algorithm, the novel dynamic planning method for evacuation routes in the dam-break flood scenario was proposed. Moreover, this method was applied to a typical water conservancy engineering project, and the planned optimal evacuation routes accurately avoided the flood influence, and the calculation results were reasonable and accurate. The real-time dynamic planning of flood avoidance routes based on flood routing information was realized. This study provides effective guarantee for the emergency transfer of downstream people, which has valuable implications for the theoretical research and engineering practice of reservoir dam safety and management.

USING FEATURE SELECTION AND ACO ALGORITHM FOR OPTIMIZING SMART CLASSROOM

The smart education had a huge impact on learning and teaching, so it must be effective and highly efficient. An efficient smart campus or smart classroom will make the learning more and more easily, the students could learn and give the best activities. In addition, the teachers will be able to make right decisions. To achieve this goal, the smart classroom's conditions must be ideal. Since ACO (ant colony optimization algorithm) is a meta heuristic algorithm, in this paper, it is found that ACO, in conjunction with a machine learning classifier, was an effective method used in feature selection for selecting best features from an intelligent campus data set to create an environment that is conducive to academic success and student learning, such as (humidity and temperature), lighting and sound pressure levels, wind direction, and raw rainfall amounts (among other variables). In this contribution to get the most accurate results, the ACO algorithm was combined with a logistic regression classifier that was used to select the best features. The accuracy of the proposed model was 0.927438624 and 0.898268071 for two sets of data back to the School of Design and Environment 4, building located at the National University of Singapore

Path Planning in the Case of Swarm Unmanned Surface Vehicles for Visiting Multiple Targets

In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case of swarm Unmanned Surface Vehicles (USVs). This study aims to further explore the performance of the ACO algorithm by integrating fuzzy logic in order to cope with the multiple contradicting objectives and generate quality solutions by in-parallel identifying the mission areas of each USV to reach the desired targets. The design of the operational areas for each USV in the swarm is performed by a comparative evaluation of three popular clustering algorithms: Mini Batch K-Means, Ward Clustering and Birch. Following the identification of the operational areas, the design of each USV path to perform the operation is performed based on the minimization of traveled distance and energy consumption, as well as the maximization of path smoothness. To solve this multiobjective path planning problem, a comparative evaluation is conducted among ACO and fuzzy inference systems, Mamdani (ACO-Mamdani) and Takagi–Sugeno–Kang (ACO-TSK). The results show that depending on the needs of the application, each methodology can contribute, respectively. ACO-Mamdani generates better paths, but ACO-TSK presents higher computation efficiency.

Geometric Mean Method Combined With Ant Colony Optimization Algorithm to Solve Multi-Objective Transportation Problems in Fuzzy Environments

The transportation problem (TP) is a well-known subject in the field of optimization and a very prevalent challenge for businesspeople. The goal is to reduce the total transportation cost, of delivering resources from sources to destinations. The literature demonstrates that various approaches have been designed with a single goal in mind, although TPs are not always developed with a bi-goal in mind. Solving transportation difficulties with several objectives is a common task. In this study, a new method for addressing multi-criteria TP using geometric means, along with a novel approach of the Ant Colony Optimization algorithm (ACO) for solving multi-objective TP in a fuzzy environment. Fuzzy numbers have been used to solve real-world problems in various domains, including operations research and optimization. The ACO Algorithm has long been recognized as a viable alternative strategy for solving optimization problems. The purpose of this study is to provide a unique approach for organizing fuzzy numbers as well as enhancements to the ACO algorithm for solving the Multi-Objective TP model. Our method, such as Geometric Mean Ant Colony Optimization Algorithm (GMACOA), outperforms other methods in terms of objective values. Numerical examples are provided to demonstrate the method in comparison to various current methods.

Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control

<abstract> <p>This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools.</p> </abstract>

An Improved Ant Colony Optimization Algorithm for Scheduling in Cloud Computing Environment

Cloud computing is a model for delivering, hosting and accessing shared pool of resources and services over the internet in on-demand, self-service, dynamically scalable and metered manner. Scheduling access to cloud resources is a topic of interest to both researchers and IT community. Several approaches have been proposed from the traditional methods to those that are exhaustive in nature. However, Cloud task scheduling is an NP-hard optimization problem, and can break down deterministic or exhaustive approaches with the increase in the number of variables to be optimized. Recently there is has been attempt to use meta-heuristic algorithm for scheduling in cloud computing. These include Genetic Algorithms (GA), Particle Swamp Optimization (PSO), Ant Colony Optimization Algorithm (ACO) and other nature inspired algorithms. The algorithms offer NP-hard problems global solutions acceptable in time frame proportional to the number of variables to be optimized. We use ACO algorithm for scheduling in cloud computing environment. Load balancing is added into the algorithm to prevent the algorithm from falling into local minima. A comparison with (43) shows that the proposed algorithm achieved 51.95% in makespan. However, the compared work is better than this work in the average running time by 86.50%.

Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

A method combining ELM and PLSR (ELM-P) for estimating chlorophyll content in rice with feature bands extracted by an improved ant colony optimization algorithm

Using spectral information to detect chlorophyll content in rice canopy leaves quickly, non-destructively and accurately has a great practical significance for rice growth evaluation, precise fertilization and scientific management. In this paper, Japonica rice in northeast China is taken as the research object, and rice canopy hyperspectral data of key growth stages are obtained through plot experiments. Firstly, the standard normal variate (SNV) and SG smoothing method is used to preprocess the hyperspectral data, and based on the processed spectral data and ant colony optimization algorithm (ACO), an improved adaptive updating ant colony optimization algorithm (AU-ACO) is proposed to select feature bands of chlorophyll content by introducing an adaptive adjustment strategy of volatilization coefficient and optimal pheromone updating strategy in different stages, and compared with the standard ACO algorithm and full-band modeling method. Then, taking the extracted feature band and full-band as input, considering the advantages of linear model and nonlinear model, a hybrid prediction model (ELM-P) combining extreme learning machine (ELM) with partial least squares regression (PLSR) is proposed. In this model, PLSR is used to obtain the preliminary prediction of the chlorophyll content in rice, and the linear trend is obtained, and then the ELM with strong nonlinear approximation ability is used to predict the deviation of PLSR model, the final prediction value is obtained by superposition of the two outputs. In order to verify the superiority of the proposed model, PLSR and ELM prediction models are also established by taking the full-band and the feature bands by different extraction methods as input. The simulation experiment results show that under the same prediction model conditions, the feature bands extracted by proposed AU-ACO algorithm as input can reduce the complexity of the model and improve the prediction performance of the model. The determination coefficient (R2-P) of testing set and the determination coefficient (R2-C) of training set for each model are greater than 0.6785, among them, the ELM-P model with the feature band extracted by AU-ACO algorithm as input has the highest prediction accuracy, R2-C and R2-P are 0.7969 and 0.7918 respectively, RMSE-C and RMSE-P are 1.2969 and 1.1293 mg/L respectively, which can provide valuable reference for detecting and evaluating chlorophyll content in Japonica rice.

Image Edge Detection with Fuzzy Ant Colony Optimization Algorithm

Searching and optimizing by using collective intelligence are known as highly efficient methods that can be used to solve complex engineering problems. Ant colony optimization algorithm (ACO) is based on collective intelligence inspired by ants' behavior in finding the best path in search of food. In this paper, the ACO algorithm is used for image edge detection. A fuzzy-based system is proposed to increase the dynamics and speed of the proposed method. This system controls the amount of pheromone and distance. Thus, instead of considering constant values for the parameters of the algorithm, variable values are used to make the search space more accurate and reasonable. The fuzzy ant colony optimization algorithm is applied on several images to illustrate the performance of the proposed algorithm. The obtained results show better quality in extracting edge pixels by the proposed method compared to several image edge detection methods. The improvement of the proposed method is shown quantitatively by the investigation of the time and entropy of conventional methods and previous works. Also, the robustness of the proposed method is demonstrated against additive noise.

ACO powered by Local Searches Algorithms for the Solution of TSP Problems


 Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

Dynamic integrated process planning, scheduling and due-date assignment using ant colony optimization

This paper presents two well-known meta-heuristics which are Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) to solve the dynamic integrated process planning, scheduling and due date assignment problem (DIPPSDDA) in which jobs arrive to the shop floor randomly. In this study, it is aimed to find the best combination of dispatching rule, due date assignment rule and route of all job with the objective of minimizing earliness, tardiness and due-dates of each jobs. 8 different size shop floors for the comparison of the GA and ACO algorithms performances have been developed. As a result of the experimental study, it was concluded that ACO algorithm outperformed GA algorithm. In addition, it has been suggested that integrated approaches can provide more global manufacturing efficiency than individual approaches.

Using Local Searches Algorithms with Ant Colony Optimization for the Solution of TSP Problems

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

Parallel custom instruction identification for extensible processors

With the ability of customization for an application domain, extensible processors have been used more and more in embedded systems in recent years. Extensible processors customize an application domain by executing parts of application code in hardware instead of software. Determining parts of application code as custom instruction generally requires subgraph enumeration and subgraph selection. Both subgraph enumeration problem and subgraph selection problem are computationally difficult problems. Most of previous works focus on sequential algorithms for these two problems. In this paper, we present a parallel implementation of a latest subgraph enumeration algorithm based on a computer cluster. A standard ant colony optimization algorithm (ACO), a modified version of ACO with local optimum search and a parallel ACO algorithm are also proposed to solve the subgraph selection problem in this work. Experimental results show that the parallel algorithms outperform the sequential algorithms in terms of runtime or (and) quality of results. In addition, we have formally proved the upper bound on the number of feasible solutions in subgraph selection problem with or without the overlapping constraint.

Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.

Two-Level Optimization Method of Transmission Tower Structure Based on Ant Colony Algorithm

This paper highlights a two-level optimization process combined with ant colony optimization algorithm (ACO)，which is applied to design the shape of the transmission tower structure, and compiles optimization design procedure for the solution of the structure shape with the MATLAB language. By comparing the different methods of the transmission tower structure optimization design, the ACO algorithm is demonstrated to be more accuracy and practical in dealing the complex optimizations problem, i.e. the combination of continuous variables and discrete variables. These calculation examples illustrate that the two-level optimization method is reliable and versatile.