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Abstract One of the main constituents of any reservoir characterization is an accurate forecast of water saturation, which is highly dependent upon the cementation exponent. Even though there have been a lot of studies, the most common correlations depend on total porosity. This means that they do not work as well in heterogeneous carbonate reservoirs, especially tight formations with total porosities less than 10%. This study aims to develop accurate and universal models for estimating the cementation exponent in deep and tight carbonate pore systems located in West Asia. Two heuristic algorithms, including the radial basis function neural network optimized by ant colony optimization (RBFNN-ACO) and gene expression programming (GEP), were employed to calculate the cementation exponent. To do this, we prepared a databank incorporating cementation exponents, total porosity, and various pore types. Then, the databank is classified into the test subset (for model prediction checking) and the train subset (for model construction). The reliability of the new recommended models is inspected by applying several statistical quality measures associated with graphical analyses. So, the consequences of the modeling disclose the large precision of the above-mentioned RBFNN-ACO, GEP Model-I, and GEP Model-II by average absolute percentage relative deviations (AAPRD%) of 6.28%, 6.39%, and 7.45%, respectively. Based on the outliers analysis, nearly 95% of the databank and model estimations are, respectively, valid and reliable. Additionally, the three input variables, including moldic porosity (with a + 70% impact value), non-fabric-selective dissolution (connected) porosity (with a -30% impact value), and interparticle porosity (with a -23% impact value), exhibit the main affecting parameters on the cementation exponent. Comparing current results with traditional literature correlations demonstrates the supremacy of the RBFNN-ACO model (AAPRD = 6.28% and root mean squared error (RMSE) = 0.17) over the examined literature correlations such as Borai’s equation (AAPRD = 12.30% and RMSE = 0.41). In addition, RBFNN-ACO can give better results than Borai’s Eqn. for tight (porosity less than 10%) and deep carbonate samples.

Analyzed three swarm intelligence algorithms, namely Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), Particle Swarm Optimization (PSO) and the adaptability of these algorithms to a dynamic environment. Firstly, the ACO algorithm was analyzed, the behavior of ants in nature, the purpose of the algorithm, and its shortcomings in a dynamic environment. Then the existing modifications of this algorithm to changing environments were investigated, namely AСO with dynamic pheromone updating (AACO), ACO with adaptive memory (ACO-AP), ACO with multi-agent system (MAS-ACO), ACO with machine learning algorithms (MLACO). The advantages and disadvantages of these modifications are also discussed in detail. The software tools that implement the functionality of this algorithm, such as AntTweakBar, AntOpt, EasyAnt have been mentioned. These software tools provide an opportunity to develop new modifications of the ACO algorithms and to study existing ones. Furthermore, the capabilities of the BCO algorithm were clarified and the behavior and parameters of this algorithm were described, its pros and cons in a dynamic environment were investigated. The following BCO modifications were considered: Group Bee Algorithm (GBA), Artificial Bee Colony (ABC), and open source software: PySwarms, PyABC. The third part of the article investigates the work of the PSO algorithm, its advantages and disadvantages of adaptation to dynamic environments. Dynamic Particle Swarm Optimization with Permutation (DPSO-P), Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning (DMS-P50-EL) are considered as modifications of PSO to adapt to dynamic environments. The libraries for work such as SciPy, DEAP, PyGAD, Particleswarm, JSwarm (has a wide API and well-written documentation), Dlib have been mentioned. Finally, a comparative table with the most important properties (resistance to environmental changes, complexity of implementation, the possibility of using for a UAV swarm, etc.) for all three algorithms was created, a brief description of similar articles comparing algorithms of swarm intelligence was also made, and the conclusions of the study were drawn.

Conventional communications systems centered on data prioritize maximizing network throughput using Shannon’s theory, which is primarily concerned with securely transmitting the data despite limited radio resources. However, in the realm of edge learning, these methods frequently fall short because they depend on traditional source coding and channel coding principles, ultimately failing to improve learning performance. Consequently, it is crucial to transition from a data-centric viewpoint to a task-oriented communications approach in wireless system design. Therefore, in this paper, we propose efficient communications under a task-oriented principle by optimizing power allocation and edge learning-error prediction in an edge-aided non-orthogonal multiple access (NOMA) network. Furthermore, we propose a novel approach based on the ant colony optimization (ACO) algorithm to jointly minimize the learning error and optimize the power allocation variables. Moreover, we investigate four additional benchmark schemes (particle swarm optimization, quantum particle swarm optimization, cuckoo search, and butterfly optimization algorithms). Satisfactorily, simulation results validate the superiority of the ACO algorithm over the baseline schemes, achieving the best performance with less computation time. In addition, the integration of NOMA in the proposed task-oriented edge learning system obtains higher sum rate values than those achieved by conventional schemes.

Under conditions of partial shadowing, traditional Maximum Power Point Tracking (MPPT) algorithms face difficulties in precisely locating the maximum power point (MPP) of the system. To address this problem, this paper proposes an optimization algorithm, Ant-Fuzzy Optimization (AFO) algorithm. AFO utilizes the global search capability of the ant colony optimization (ACO) algorithm and the high precision performance of the fuzzy logic (FL) algorithm, mitigating the tendency of the fuzzy algorithm to fall into local optima in shadow conditions. Internally, the AFO algorithm comprises two parallel logics, selecting different strategies for tracking based on varying environmental states, achieving a balance between tracking accuracy and computational efficiency. This intelligent logic selection mechanism allows the algorithm to flexibly adapt to diverse working environments of photovoltaic (PV) arrays, enhancing the robustness and adaptability of the system. The paper establishes corresponding simulation models in MATLAB/SIMULINK and validates AFO through hardware experiments on the dSPACE real-time simulation system. The results demonstrate the feasibility and effectiveness of AFO in practical environments. Both simulation and experimental prototypes indicate that AFO can rapidly and accurately extract the maximum power point with an accuracy of 98.7%. Furthermore, AFO exhibits rapid dynamic response characteristics, reaching steady state within 0.9 seconds, providing a reliable solution for optimizing the output power of photovoltaic arrays.

The increasing use of Wireless Sensor Networks (WSNs) is leading to network traffic growth as data exchange among WSN nodes increases. Protecting WSNs from Denial of Service (DoS) attacks is essential for enhancing data security and avoiding interruptions that can harm productivity and reputation. Detecting a DoS attack quickly is crucial to minimize its impact on the targeted system or network. To meet this requirement, it is critical to have an effective DoS attack detection mechanism that ensures system or network availability and safeguards data and resources. The suggested approach focuses on enhancing DoS attack detection, reducing anomalies, and offering an efficient way to protect WSNs from DoS attacks. A new framework has been proposed to improve DoS attack detection by using optimization techniques and Machine Learning (ML) algorithms to detect and manage DoS attacks effectively. This system integrates Ant Colony Optimization (ACO) with ML algorithms to propose the Enhanced Ant Colony Optimization (EACO) technique. The proposed system has been compared to ACO through the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms to assess their performance in identifying DoS attacks. The results from the assessment show that when the EACO algorithm is paired with ML algorithms, it can achieve accuracy, sensitivity, specificity, and F1 scores between 87.6% and 99.8%. Furthermore, the EACO surpasses ACO in terms of accuracy, sensitivity, specificity, F1 score, precision, and Negative Predictive Value (NPV) by about 3.64%, 38.6%, 1.11%, 27.53%, 16.35%, and 2.78%, respectively.

Determining the bearing capacity of a strip footing under inclined loading is crucial in designing foundations. Due to the complex correlations, the subject area remains predominantly unexplored, or it has been simulated using only limited datasets. This paper presents the development of a prediction model based on machine learning (ML), leveraging advanced hybrid artificial neural network (ANN) models for estimating the bearing capacity of strip footings under inclined loading. The ANN models are hybridized with four different optimization algorithms, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE), which enhance the accuracy and efficiency of the predictive capabilities of ANN. The models are trained on a dataset of 920 records, and their performance is evaluated using a range of significant performance metrics. The ANN-ICA model achieved the highest rank in the score analysis (R2 =0.912, RMSE=0.165 in testing), followed by ANN-ALO and ANN-ACO. To reinforce the trustworthiness of the predictions, external validation is employed, and visual analysis is conducted using the Taylor diagram. The findings suggest that the proposed models are robust, and the incorporation of optimization techniques has improved the performance of traditional ANNs. The research findings have significant implications for the field of geotechnical engineering, providing engineers and researchers with valuable insights into the applicability of hybrid artificial neural network (ANN) models and alternative machine learning (ML)-based prediction models in assessing the bearing capacity of strip footings.

In this comprehensive paper the method of detecting various diseases within short period of time using Audio Reality/ Virtual Reality (AR/VR) techniques is proposed. For proper functioning of AR/VR models in medical applications three distinct algorithms such Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) is integrated where the entire operation is performed with respect to search space. Moreover, the detection process in AR/VR models depends on several factors where minimum error functions must be ensured. Hence in the integrated technique both Absolute Errors (AE) and Time Errors (TE) are measured and compared with existing methods. As the performance of detection is greatly improved with search space the fitness function of each algorithm is observed and it is considered as maximization objective in the proposed method. Furthermore, the complexity of AR/VR models in real time detection process is detected and it is realistic that high complex detections are converted to simple detections. In the comparative analysis of three algorithms ACO proves to be much better as errors are minimized with maximization of fitness function.

This paper proposes a flexible travel recommender model (FTRM) that emphasizes the symmetry between user preferences and travel constraints, addressing key challenges in the field such as the integration of diverse constraint types and the customization of travel itineraries. The key contribution of the proposed model lies in its integration with the item constraints data model (ICDM), which effectively manages a plethora of constraint types. Additionally, this study develops a novel algorithm inspired by ant colony optimization (ACO) principles, demonstrating performance metrics that are comparable to state-of-the-art algorithms in this field. A comprehensive set of systematic experimental analyses is conducted, employing various models across diverse situational contexts, with the primary goal of illustrating the capabilities of the proposed symmetrical FTRM using real-world data from the Durham dataset. The obtained results highlight the model’s ability to accommodate diverse constraint types, facilitating the customization of travel itineraries to suit individual user preferences and achieve a balanced and symmetrical travel experience. Specifically, our model outperforms existing models in terms of flexibility and customization, showing significant improvements in user satisfaction and itinerary efficiency.

The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

The submarine is an underwater ship that can perform a variety of combat missions in a complex marine environment. Path planning of submarines has always been the focus of military marine engineering research. In most practical applications, there are numerous marine physical phenomena in the marine environment, such as pycnocline, density fronts, mesoscale eddy, etc., which have an important impact on the navigation of submarines. First, the artificial potential field heuristic factor is introduced into the ant colony algorithm to improve its convergence speed, and the artificial potential field ant colony optimization (APF-ACO) is obtained. In addition, this article uses the unit-body to reflect the regional physical elements and quantifies the physical marine phenomenon in the form of the cost function, which is used to solve the problem of submarine path planning in the complex marine environment. In this article, the algorithm is tested in a real marine data environment. The experimental results show that the algorithm can realize the utilization of the ocean sound speed environment, ocean density environment and ocean current environment, and obtain a path more suitable for submarine underwater navigation.

Disassembly Planning (DP) refers to an optimization method to find the most cost-effective disassembly sequence for products based on the disassembly properties of parts. In traditional sequential disassembly planning, only a single part or component is removed. To effectively improve the product disassembly efficiency, this study explores the problem of Asynchronous Parallel Disassembly Planning (aPDP) with multiple manipulators. In the aPDP situation where multiple manipulators are used, the optimization method of arranging the manipulators needs to be considered in addition to the limitation of the priority order of parts. This study proposes an improved particle swarm optimization to discuss the aPDP combination optimization problem. The minimum Make Span is the objective, and the solution status and convergence speed are compared with the results of other particle swarm optimization methods, a Genetic Algorithm and Ant Colony Optimization. The results show that the proposed improved version of the particle swarm optimization algorithm has better solution quality and execution time.

Distribution is one of the essential activities in business because it determines the price of products or goods in the market. So, choosing the shortest path is considered one of the most important things in business, especially distributors. PT Everbright, one of the business actors engaged in the manufacture of batteries and as the main distributor, hopes that the distribution of goods to have the shortest route so that costs can be minimized. The aim of the research was to determine the shortest path in distributing goods in the Medan area to the location of consumers. The research used Ant Colony Optimization (ACO). Determination of the shortest path is based on distance calculations in kilometers, protocol roads, gangs, normal road conditions, and differences between small and major roads. Based on the research results, it is found that the J1 line has the greatest value. So, the path chosen by the ants to pass is J1 which is 11 km away from the departure location (PT Everbright) to the destination (Pasar Glugur Kota). It passes through PT Everbright --> Jln. Gatot Subroto --> Jln. Kapt. Muslim --> Jln. Tgk. Amir Hamzah --> Jln. KH. Syeikh Abdul Wahab Rokan --> Pasar Glugur Kota.

There are many potential sources of data uncertainty, such as imperfect measurement or sampling, intrusive environmental monitoring, unreliable sensor networks, and inaccurate medical diagnoses. To avoid unintended results, data mining from new applications like sensors and location-based services needs to be done with care. When attempting to classify data with a high degree of uncertainty, many researchers have turned to heuristic approaches and machine learning (ML) methods. We propose an entirely new ML method in this paper by fusing the Radial Basis Function (RBF) network based on ant colony optimization (ACO). After introducing a large amount of uncertainty into a dataset, we normalize the data and finish training on clean data. The ant colony optimization algorithm is then used to train a recurrent neural network. Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). Error metrics show that our model significantly outperforms the gold standard and other popular ML methods. Using industry-standard performance metrics, the results of our experiments show that our proposed method does a better job of classifying uncertain data than other methods

In order to solve the problems of deadlock, slow convergence, easy to get into local optimum and uneven path when ant colony algorithm is applied to the path planning of mobile robots, a smooth path planning method combining improved A* ant colony algorithm and the rolling window method is proposed. Firstly, the improved A* algorithm is used to initialize the ant colony pheromone to solve the problem of low ant colony efficiency. Then, the state transition probability function is improved to consider the feasible path “activity” and the end position in the function to avoid deadlock phenomenon. At the same time, based on the mechanism of inequality principle, the pheromone of ant colony is updated to avoid falling into the local optimal path and accelerate the convergence speed of the algorithm. Secondly, based on the global path planning, local real-time path planning is carried out by integ-rating the rolling window method and the dynamic obstacle avoidance strategy. Finally, Bessel curve is used to process the smoothness of the planned path, so that the smoothed path is closer to the actual motion path. In order to ensure the best performance of the algorithm, genetic algorithm with elite strategy is used to optimize the parameters of the algorithm. Three sets of experimental results show that the proposed algorithm is effective in the presence of simple or complex, static or dynamic obstacles.

In order to solve the problems of low precision and premature convergence of traditional ant colony system in solving traveling salesman problem (TSP), this paper proposes a dynamic hierarchical ant colony system which integrates reward and punishment learning strategy (DHL-ACS). Firstly, the ant colony is dynamically divided into empire ants, colony ants and king ant, in which empire ants and colony ants perform local pheromone update, and king ant performs global pheromone update. In local pheromone update, the empire ants perform larger weight coefficient, which is responsible for the development of better solution to enhance the guidance of algorithm, and the colony ants perform small weight coefficient to explore the suboptimal solution and to ensure the diversity of algorithm. In addition, the method of exchanging high-quality solutions between empire ants and colony ants is used to improve the accuracy of the solutions. Secondly, an improved learning strategy is proposed, which realizes the assimilation of the better solution by rewarding the common path of empire ants and colony ants, and improves the convergence speed of the algorithm. Furthermore, when the algorithm stops, the feedback operator is introduced to reduce the pheromone concentration on the path of king ant, which can punish the path of higher pheromone, so as to improve the diversity of the population and enhance the ability of the algorithm to jump out of the local optimum. Through the experiments of several sets of TSP instances, it is shown that the improved algorithm balances the relationship between convergence rate and diversity, especially for large-scale TSP problems, which can effectively improve the accuracy of solutions.

Aiming at the problems of traditional ant colony algorithm in solving traveling salesman problem (TSP), such as slow convergence speed and easy to fall into local optimum, an ant colony optimization algorithm (RCACO) based on dynamic recombination and cooperative communication strategy is proposed. Firstly, the ant colony is divided into greedy ant colony and exploratory ant colony. The two ant colonies implement different path construction rules and pheromone update strategies to balance the convergence speed and diversity of the algorithm. Secondly, a new dynamic recombination operator based on the clew binary tree is adopted, and the solution set is dynamically recombined according to different strategies to improve the diversity of the algorithm. Furthermore, a collaborative communication strategy based on similarity and potential value is proposed. From a global perspective, the path with the most potential to become the optimal solution is found, and pheromone rewards are given to these paths to improve the convergence speed of the algorithm. Finally, the algorithm also adds a stagnation avoidance strategy to help the ant colony jump out of the local optimum and improve the accuracy of the algorithm. Through the Matlab simulation experiment of multiple groups of cases in TSPLIB, compared with the traditional ant colony algorithm and other optimization algorithms, the simulation results show that the improved ant colony algorithm significantly improves the convergence speed and solution accuracy.

Intelligent techniques to solve the problem of decision-making in project management, apart from the methods of operations research, the choice was made on one of the algorithms of crowd intelligence represented by the Ant Colony Optimization algorithm (ACO)to solve the matter of finding the optimal critical path for the enterprise business network because the business network is more Networks tradition the behavior of the ant colony system to find the optimal critical path for the Critical Path Network(CPN) as. You own a project beginning contract (the first event) equivalent to an ant hill.The project end contract (the last event) is equivalent to the food site.The matter of finding the optimal critical path for the project is equivalent to the search process to find an optimal (the shortest) path between the nest and the food site. The program ANTOCPN, written in Matlab language on a virtual business network. The program is featuring by its efficiency, accuracy of results, and the possibility of applying it to any business network, regard of the degree of complexity in terms of the number of paths (activities), whether real or imaginary, smoothly and easily. Also, the results of the ANTOCPN algorithm program were compared with the results of the genetic algorithm program for the same question GAOCPN for previous research, and the ant algorithm proved its worth in terms of speed in obtaining the optimal solution.

The unrelated parallel machine scheduling (PMS) problem is essential for the manufacturing industry. Scheduling will save company resources, especially time management. By solving scheduling problems quickly and precisely, the company can get more profit. On that note, this paper focused on unrelated PMS problems, which did not consider the inherent uncertainty in processing time and set up time by minimizing the makespan and tardiness. This paper aimed to minimize the makespan and tardiness using timing considerations. This paper described how to schedule unrelated parallel machines using the Ant Colony Optimization (ACO) Algorithm approach. The ACO is beneficial for inherent parallelism problems and can provide fast and reasonable solutions. This study revealed that the results of ACO Algorithm scheduling were obtained under a steady condition in iteration 30467. This condition can be interpreted that the makespan and tardiness value is close to 2.75%. By minimizing the makespan and tardiness, the delay of product delivery to consumers can be anticipated. Moreover, a company can maintain customer satisfaction and increase its profit.

Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.

Cloud computing has a great ability to store and manage remote access to services in a term of software as a service (SaaS). Recently, many organizations have moved to use outsourcing over the cloud to reduce the local resource burden. The stored services over the cloud are too scalable and complex, so an optimization method is more desirable to select appropriate services that satisfy the clients’ request. To do so, the quality of service (QoS) parameters that associated with each service are the best resources for choosing and optimizing the appropriate services over the cloud. Therefore, the cloud service composition aims to select and integrate services over the cloud to satisfy the clients’ request. In this work, a hybrid algorithm is introduced, which combines ant colony optimization (ACO) and genetic algorithm (GA) to efficiently compose the services over the cloud. The GA is used to tune the ACO’s parameters automatically and the ACO adapts its performance based on the parameters tuning. The main contribution of this work is to help the ACO algorithm to avoid stagnation problem and enhance the performance of the ACO where this performance is affected by the value of the ACO’s parameters. The experimental results on 15 different real datasets have shown the effectiveness of the proposed algorithm to search comparable solutions compared to five competitors.

Aiming at the problems that the ant colony algorithm is easy to fall into local optimum and the convergence speed is slow in traveling salesman problem (TSP), dynamic grouping ant colony algorithm combined with cat swarm optimization is proposed. Firstly, the ants are distributed manually in different cities evenly when the population is initialized. Secondly, based on the division of labor in the cat swarm optimization, a dynamic grouping mechanism is introduced into the ant colony system to classify the ants into two types: search ants and tracking ants. Search ants increase the algorithm??s diversity in the early stage by improving the path construction rules, tracking ants use pheromone diffusion mechanism to adaptively update local pheromone to highlight the role of superior sub-paths, and avoid the algorithm falling into local optimum. Finally, the convergence speed is accelerated by the pheromone global update mechanism. Simulation experiments are carried out on multiple sets of instances in TSPLIB through Matlab. The simulation results show that the improved algorithm balances the diversity and conver-gence speed, and effectively imporves the quality of the solutions.

The goal of data centers in the cloud computing environment is to provision the workloads and the computing resources as demanded by the users without the intervention of the providers. To achieve this, virtualization based server consolidation acts as a vital part in virtual machine placement process. Consolidating the Virtual Machines (VMs) on the Physical Machines (PMs) cuts down the unused physical servers, decreasing the energy consumption, while keeping the constraints for CPU and memory utilization. This technique also reduces the resource wastage and optimizes the available resources efficiently. Ant Colony Optimization (ACO) that is a well-known multi objective heuristic algorithm and Grey Wolf Algorithm (GWO) has been used to consolidate the servers used in the virtual machine placement problem. The proposed Fuzzy HAGA algorithm outperforms the other algorithms MMAS, ACS, FFD and Fuzzy ACS compared against it as the number of processors and memory utilization are lesser than these algorithms.

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

In this paper, a hybrid optimization controller that combines a genetic algorithm (GA) and ant colony optimization (ACO) called GA-ACO algorithm is proposed. It is applied to a photovoltaic module array (PVMA) to carry out maximum power point tracking (MPPT). This way, under the condition that the PVMA is partially shaded and that multiple peaks are produced in the power-voltage (P-V) characteristic curve, the system can still operate at the global maximum power point (GMPP). This solves the problem seen in general traditional MPPT controllers where the PVMA works at the local maximum power point (LMPP). The improved MPPT controller that combines GA and ACO uses the slope of the P-V characteristic curve at the PVMA work point to dynamically adjust the iteration parameters of ACO. The simulation results prove that the improved GA-ACO MPPT controller is able to quickly track GMPP when the output P-V characteristic curve of PVMA shows the phenomenon of multiple peaks. Comparing the time required for tracking to MPP with different MPPT approaches for the PVMA under five different shading levels, it was observed that the improved GA-ACO algorithm requires 19.5~35.9% (average 29.2%) fewer iterations to complete tracking than the mentioned GA-ACO algorithm. Compared with the ACO algorithm, it requires 74.9~79.7% (average 78.2%) fewer iterations, and 75.0~92.5% (average 81.0%) fewer than the conventional P&O method. Therefore, it is proved that by selecting properly adjusted values of the Pheromone evaporation rate and the Gaussian standard deviation of the proposed GA-ACO algorithm based on the slope scope of the P-V characteristic curves, a better response performance of MPPT is obtained.

Feature selection and feature extraction have always been of utmost importance owing to their capability to remove redundant and irrelevant features, reduce the vector space size, control the computational time, and improve performance for more accurate classification tasks, especially in text categorization. These feature engineering techniques can further be optimized using optimization algorithms. This paper proposes a similar framework by implementing one such optimization algorithm, Ant Colony Optimization (ACO), incorporating different feature selection and feature extraction techniques on textual and numerical datasets using four machine learning (ML) models: Logistic Regression (LR), K-Nearest Neighbor (KNN), Stochastic Gradient Descent (SGD), and Random Forest (RF). The aim is to show the difference in the results achieved on both datasets with the help of comparative analysis. The proposed feature selection and feature extraction techniques assist in enhancing the performance of the machine learning model. This research article considers numerical and text-based datasets for stroke prediction and detecting hate speech, respectively. The text dataset is prepared by extracting tweets consisting of positive, negative, and neutral sentiments from Twitter API. A maximum improvement in accuracy of 10.07% is observed for Random Forest with the TF-IDF feature extraction technique on the application of ACO. Besides, this study also highlights the limitations of text data that inhibit the performance of machine learning models, justifying the difference of almost 18.43% in accuracy compared to that of numerical data.

Technical advancement has propelled the proliferation of unmanned vehicles. Out of the multiple paths between origin (O) and destination (D), the optimal O-D path should be selected in the light of travel distance, travel time, fuel cost and pollutant emissions. This paper proposes a dynamic path planning strategy based on fuzzy logic (FL) and improved ant colony optimization (ACO). Firstly, the classic ACO was improved into the rank-based ant system. The rank-based ant system works well in static environments, but cannot adapt well to dynamic environments. Considering the difficulty in accurate digitization of dynamic factors, the improved ACO was integrated with the FL into the fuzzy logic ant colony optimization (FLACO) to find the optimal path for unmanned vehicles. Finally, the FLACO, the classic ACO and the improved ACO were separately applied to find the optimal path in a road network, with a novel concept called virtual path length. The results show that the FLACO output the shortest virtual path among the three algorithms, i.e. identified the most cost-effective path. This mean the FLACO can find the most efficient and safe path for unmanned vehicles in a dynamic manner.

Selecting the optimal satellite subset for positioning from all satellites in view can not only achieve positioning accuracy but also reduce the computational burden. In this article, a navigation satellites selection method is proposed based on ant colony optimization with the improvement of polarized feedback (ACO-PF). Firstly, the satellite selection problem is described as a combinatorial optimization problem, and the noise weighted geometric dilution of precision (NWGDOP) is defined as the criterion for satellite selection. Then the ant colony optimization (ACO) is incorporated to solve the problem, and a polarized feedback mechanism is presented to improve the convergence speed of algorithm. Meanwhile, a perturbation operator is designed to improve the global searching ability of the algorithm. The numerical experimental results show that ACO-PF can select the superior satellites combination which provides high-precision positioning. And its convergence outperforms the related algorithms by up to 50%. Besides, the achieved NWGDOP of ACO-PF is usually 0.065 smaller than ACO. Therefore, the ACO-PF method can be considered as a promising candidate for satellite selecting in navigation applications.

This paper aims to solve the last-mile distribution of rural e-commerce logistics (RECL) for the survival of third-party logistics enterprise. Considering the features of the RECL (long transport chain and low consumption density), A route optimization model is constructed for RECL's last-mile distribution to maximize the profit of the logistics enterprise, which is subsidized by the government. To solve the model, the ant colony optimization (ACO) was improved to suit the RECL's last-mile distribution by modifying the heuristic information, the update rule of pheromone, and the solution construction. Next, the optimal combinations of the default parameters in the improved ACO were determined through Matlab tests on five test datasets in different sizes. The other parameters were configured according to the scale of the RECL. On this basis, the improved ACO was proved effective through example analysis on the said test datasets. The analysis results also reflect how the number of vehicles affects the maximum profit of the logistics enterprise and the coverage of the RECL logistics network.

Based on automated guided vehicle (AGV), the intelligent parking system provides a novel solution to the difficulty of parking in large cities. The automation of parking/pick-up in the system hinges on the path planning efficiency of the AGV. Considering the numerous disconnected paths in intelligent parking systems, this paper introduces the fallback strategy to improve ant colony optimization (ACO) for path planning in AGV-based intelligent parking system. Meanwhile, the valuation function was adopted to optimize the calculation process of the heuristic information, and the reward/penalty mechanism was employed to the pheromone update strategy. In this way, the improved ACO could plan the optimal path for the AGV from the starting point to the destination, without sacrificing the search efficiency. Next, the optimal combination of ACO parameters was identified through repeated simulations. Finally, a typical parking lot was abstracted into a topological map, and used to compare the path planning results between the improved ACO and the classic ACO. The comparison confirms the effectiveness of the improved ACO in path planning for AGV-based intelligent parking system.

In this paper, an Improved Ant Colony Optimization (IACO) algorithm optimized fuzzy PID (FPID) controller is proposed for Load Frequency Control of multi area systems. The nonlinear incremental evaporation rate and improvement of pheromone increment updating are proposed in the IACO algorithm to improve the quality of solution. And, a modified objective function using integral time multiply absolute error (ITAE), overshoot, undershoot and settling time with appropriate weight coefficients is proposed to improve the performance of the controller. Initially, a two-area non-reheat thermal system is applied and the FPID controller parameters are optimized by the IACO algorithm with five different objective functions. The modified objective function has better performances than four conventional objective functions. To demonstrate the robustness of the proposed control method, sensitivity analysis is implemented under wide variation of operating conditions and system parameters. Further, the proposed approach is also extended to two-area four-sources hydro thermal power system with/without High Voltage Direct Current (HVDC) link. The superiority of the proposed approach is shown by comparing the results with ZN, GA and hPSO-PS algorithms. The robustness of the proposed method is verified under random load disturbances. Finally, the proposed approach is extended to a two-area power system with governor dead band nonlinearity and results show that the proposed approach can cope with nonlinearity well. The results obtained from all simulations show that the proposed algorithm and modified objective function achieves better performances, such as minimum objective values (ITAE = 0.0255, ITSE = 9.30e-5, ISE = 1.91e-4 and IAE = 0.0273) obtained for the two-area non-reheat thermal system.

The paper presents results of ship's safe trajectory planning algorithms verification. Real navigational data registered from a radar with an Automatic Radar Plotting Aid on board the research and training ship Horyzont II were used as input data to the algorithms. The algorithms verified in the presented research include the Ant Colony Optimization algorithm (ACO), the Trajectory Base Algorithm (TBA), the Visibility Graph-search Algorithm (VGA) ant the Discrete Artificial Potential Field algorithm (DAPF). Details concerning data registration and exemplary results obtained with the use or real navigational data are introduced and summarized in the paper. Presented results prove the applicability of proposed algorithms for solving the ship's safe trajectory planning problem.

Edge detection is very important technique to reveal significant areas in the digital image, which could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of pre and post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system’s performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition. Also for evaluating the robustness of the system, some types of noises such as Gaussian, Salt and pepper, Poisson and Speckle are added to images, to shows proposed system power in any condition. The goal is reaching to best edges possible and to do this, more computation is needed, which increases run time computation just a bit more. But with today’s systems this time is decreased to minimum, which is worth it to make such a system. Acquired results are so promising and satisfactory in compare with other methods available in validation section of the paper.

The current situation in the energy market contributes not only to the sales growth of photovoltaic panels (PV) but also to the intense search for possibilities for its improvement. The purpose of this research was to develop a model to determine, where possible, the most beneficial modifications to improve products. The model used combination techniques, i.e., the SMARTER method, brainstorming (BM), the 7 ± 2 rule, questionnaire, ant colony optimization (ACO), and importance-performance analysis (IPA). In addition, an algorithm supporting ACO was proposed in the MATLAB program. The test was carried out on PV and showed that it is possible to determine the way of product (PV) improvement by determining a sequence of modifications for product criteria states while simultaneously considering customers’ expectations. It was shown that each state of the short-circuit electricity and peak power was satisfactory for customers. It was necessary to modify the maximum current and idle voltage. In addition, the selected modification states of the weight and dimensions will be more satisfactory compared to the current states. The proposed model is based on searching for the best changes in product criteria to achieve the highest possible customer satisfaction (i.e., product quality level). Originality is the ability to define a product improvement method (PV) depending on customer expectations but also taking into account the requirements of the company.

In service-oriented computing, web services composition is the process of translating user requirements into a workflow. This workflow comprises many tasks, each of which includes an abstract definition for some of the user requirements. Web services can be aggregated to handle the workflow. Many of these services are available from various providers for each task; they are referred to, in aggregate, as the candidate list. The web service selection (WSS) problem centers on selecting the best service from these candidates based on the quality of service (QoS) features. In this paper, we propose an enhancement to the ant colony optimization (ACO) algorithm based on a swap concept for the QoS-aware WSS problem. The aim of the enhancement to the ACO is to avoid the trap of local optima and reduce the search duration. We believe that the integration of many potent solutions will help the ACO algorithm yield a better solution and avoid stagnation. Several experiments were conducted to compare the proposed algorithm with the ACO and flying ACO (FACO) algorithms. Two different types of experiments using 22 datasets were done with 30 independent repetitions. The first type of experiment's results shows that the proposed algorithm is better than ACO by 12% and FACO by 11% in terms of quality of solutions. The results in the second type of experiment show that the proposed algorithm continuously outperforms both algorithms in terms of quality of solutions.

Detection of epistatic interactions, which are referred to as nonlinear interactive effects of single nucleotide polymorphisms (SNPs), is increasingly being recognized as an important route in capturing the underlying genetic causes of complex diseases. Its methodological and computational challenges have been well understood, and many methods also have been proposed from different perspectives. Among them ant colony optimization (ACO)-based methods are promising due to their controllable time complexities, heuristic positive feedback search, and high detection power. Nevertheless, there is no comprehensive overview of them so far. This paper, therefore, provides a systematic review of 25 ACO-based epistasis detection methods. First, the generic ACO algorithm, as well as how it is applied to detect epistatic interactions, is briefly described. Then, an in-depth review of ACO-based methods for detecting epistatic interactions is discussed from four aspects, including path selection strategies, pheromone updating rules, fitness functions, and two-stage designs. Finally, this paper analyzes the strengths and limitations of involved methods, provides guidelines for applying them, and gives several views on the future directions of epistasis detection methods.

In this paper, a novel hybrid iterative algorithm based on imperial competition algorithm (ICA) and Ant Colony Optimization (ACO) is proposed to achieve multi-parameters unified-optimization of millimeter wave (mmWave) microstrip antenna. After assimilation of ICA, the pheromone guidance mechanism is added to update the position of ant, which avoids the ICA solution falling into a local optimum. Two input impedance models of microstrip antenna are solved by different algorithms, which show the proposed algorithm (ICACO) converges faster than three compared algorithms. The multi-parameters unified-optimization of mmWave microstrip antenna under the operation of TM10 and TM02 modes is carried out by ICACO to enhance impedance bandwidth and improve bore-sight gain. The proposed algorithm is applicable in antenna multi-parameters design problems as a reliable approach with faster convergence rate.

Agricultural machinery rental is a new service form that uses big data in agriculture to improve the utilization rate of agricultural machinery and promote the development of the agricultural economy. To realize agricultural machinery scheduling optimization in cloud services, a dynamic artificial bee-ant colony algorithm (DABAA) is proposed to solve the above problem. First, to improve the practicability of the mathematical model in agricultural production, a dynamic coefficient is proposed. Then the mutation operation is combined with the artificial bee colony (ABC) algorithm to improve the algorithm. Then, iterative threshold adjustment and optimal fusion point evaluation are used to combine the ABC algorithm with the ant colony optimization (ACO) algorithm, which not only improves the search precision but also improves the running speed. Finally, two groups of comparison experiments are carried out, and the results show that the DABAA can obviously improve the running speed and accuracy of cloud services in agricultural machinery rental.

The cryptographic hash algorithms play a significant role in preserving the data security in the wireless networks. The most frequently used hash algorithms such as Message Digest (MD5) and Secure Hash Algorithm (SHA1) need high computational overhead. The energy-starved network does not afford such high computational overhead. The major constraints in the network are communication, computation and storage overheads. To overcome these issues, this paper develops a Light-Weight Two-Way Cryptographic Hash Algorithm (LTCHA) for generating a hash-digest of minimum length for the energy-starved network. This paper proposes an LTCHA for two-way authentication between the user and Cloud Service Provider (CSP). The Third Party Auditor (TPA) approves the CSP if the identity (ID) and hashcode of the user matches with the generated ID and hashcode. After receiving the approval, the user can upload the file to the service provider. File splitting is performed. After splitting the file, it is allocated to the suitable Virtual Machine (VM) in the cloud by using the ACObased task allocation algorithm. The proposed LTCHA algorithm requires lower energy consumption, overall execution time, key size, time for encryption and file uploading than the existing schemes.

Over a decade, Vehicular Adhoc Networks (VANETS) has been evolved and the field of vehicular communication has become a promising area for potential research. The challenges vary from a vehicle to vehicle communication, an indication during the event of a collision, and to enhance the drive and passenger safety. This paper aims at improving the performance of VANETs in terms of capacity, size, topological changes and maintaining the shortest routes. A new scheme termed as Ant Queue Optimization Scheme (AQO) has been introduced by extending the traditional Ant Colony Optimization (ACO). The proposed Ant Queue optimization Scheme combines both proactive and reactive mechanisms. Unlike the ACO, the AQO dynamically makes decision in choosing shortest best route in highly congested areas. Route selection is dynamic at each intersection irrespective of the size of the traffic. Encouraging results have been achieved in using the Ant Queue Optimization even at high vehicular density scenarios.

A network aggregated of wirelessly connected vehicles is recognized as vehicular ad hoc networks (VANETs). Clustering in vehicular network is a technique among many others, which targets to improve communication proficiency in VANETs. In each cluster, there is one cluster head (CH) used to manage the whole cluster. All the communications are accomplished by the CHs, i.e., inter-cluster and the intra-cluster communications. The efficiency of a network is measured by number of CHs, load on each CH and lifetime of clusters. In this paper, a novel Clustering Algorithm centered on Moth-Flame Optimization for VANETs (CAMONET) is anticipated. This is a nature-inspired algorithm. CAMONET generates optimized clusters for robust transmission. CAMONET is evaluated experimentally with renowned techniques, such as multiobjective particle swarm optimization, clustering algorithm based on ant colony optimization for VANETs, and comprehensive learning particle swarm optimization. To assess the comparative efficiency of these algorithms, numerous experiments are performed. The results are accomplished by modifying the values of grid size of the network, the number of nodes in the network, and the transmission range of nodes. The speed, direction, and transmission range of the nodes are the notable factors considered for optimized clustering. The results indicate that CAMONET delivers near optimal results that develops it into an efficient method to perform vehicular clustering in order to improve the overall performance of the network.

Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%.

Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the design cost. In the current study, a comprehensive assessment criterion has been developed to assess the performance of meta-heuristic (MH) solutions in the domain of structural design. Thereafter, the proposed criterion was employed to compare five different variants of Ant Colony Optimization (ACO). It was done by using a well-known structural optimization problem of laminate Stacking Sequence Design (SSD). The initial results of the comparison study reveal that the Hyper-Cube Framework (HCF) ACO variant outperforms the others. Consequently, an investigation of further improvement led to introducing an enhanced version of HCFACO (or EHCFACO). Eventually, the performance assessment of the EHCFACO variant showed that the average practical reliability became more than twice that of the standard ACO, and the normalized price decreased more to hold at 28.92 instead of 51.17.

In edge computing, scheduling heterogeneous workloads with diverse resource requirements is challenging. Besides limited resources, the servers may be overwhelmed with computational tasks, resulting in lengthy task queues and congestion occasioned by unusual network traffic patterns. Additionally, Internet of Things (IoT)/Edge applications have different characteristics coupled with performance requirements, which become determinants if most edge applications can both satisfy deadlines and each user’s QoS requirements. This study aims to address these restrictions by proposing a mechanism that improves the cluster resource utilization and Quality of Service (QoS) in an edge cloud cluster in terms of service time. Containerization can provide a way to improve the performance of the IoT-Edge cloud by factoring in task dependencies and heterogeneous application resource demands. In this paper, we propose STaSA, a service time aware scheduler for the edge environment. The algorithm automatically assigns requests onto different processing nodes and then schedules their execution under real-time constraints, thus minimizing the number of QoS violations. The effectiveness of our scheduling model is demonstrated through implementation on KubeEdge, a container orchestration platform based on Kubernetes. Experimental results show significantly fewer violations in QoS during scheduling and improved performance compared to the state of the art.

The paper presents a new approach for solving a path planning problem for ships in the environment with static and dynamic obstacles. The algorithm utilizes a heuristic method, classified to the group of Swarm Intelligence approaches, called the Ant Colony Optimization. The method is inspired by a collective behaviour of ant colonies. A group of agents - artificial ants searches through the solution space in order to find a safe, optimal trajectory for a ship. The problem is considered as a multi-criteria optimization task. The criteria taken into account during problem solving are: path safety, path length, the International Regulations for Preventing Collisions at Sea (COLREGs) compliance and path smoothness. The paper includes the description of the new multi-criteria ACO-based algorithm along with the presentation and discussion of simulation tests results.

A maximum power point tracking (MPPT) controller was used to make the photovoltaic (PV) module operate at its maximum power point (MPP) under changing temperature and sunlight irradiance. Under partially shaded conditions, the characteristic power–voltage (P–V) curve of the PV modules will have more than one maximum power point, at least one local maximum power point and a global maximum power point. Conventional MPPT controllers may control the PV module array at the local maximum power point rather than the global maximum power point. MPPT control can be also implemented by using soft computing methods (SCM), which can handle the partial shade problem. However, to improve the robustness and speed of the MPPT controller, a hybrid MPPT controller has been proposed that combines two SCMs, the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Matlab was used in a simulation of a GA-ACO MPPT controller where four SunPower SPR-305NE-WHT-D PV modules with a maximum power of 305.226 W connected in series were used under conditions of partial shade to investigate the performance of the proposed MPPT controller. The results obtained were analyzed and compared with others obtained under perturb and observe (P&O) MPPT and conventional ACO MPPT controllers were observed.

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.

This paper describes the use of ArF immersion lithography to verify the feasibility of a self-developed freeform illumination source that exposes features on masks and forms resist patterns. After development inspection (ADI) was used to measure the results and inspect whether the critical dimension (CD), process window, line-edge roughness (LER), and line-width roughness (LWR) of the resist pattern attained the standards of existing manufacturing processes. The testing features include nine horizontal and eight vertical features. Ant colony optimization (ACO) can provide optimal freeform illumination sources for these horizontal and vertical features. This paper considers each feature's particular weighting in order to test the complexity and adaptability of the ACO algorithm applied to semiconductor manufacturing processes. The results of source calculations show that all 17 testing features can be satisfied with the X symmetry freeform source. When using this freeform source in an ArF lithography system, the ADI resist CD of all testing features is located within ±10% target CD tolerance. The ADI target CD set by dense patterns is 40 nm. Resist pattern ADI CD measurements show that, under a through-focus condition, the resist pattern CD between 40 and 42 nm is within tolerance, the depth of focus can reach up to 0.1 μm , LER is approximately 5 nm, and LWR is approximately 7 nm. These ADI CD measurements show that this ACO-based freeform source can be integrated into existing advanced semiconductor lithography processes.

A cover-grammar of a finite language is a context-free grammar that accepts all words in the language and possibly other words that are longer than any word in the language. In this paper, we describe an efficient algorithm aided by Ant Colony System that, for a given finite language, synthesizes (constructs) a small cover-grammar of the language. We also check its ability to solve a grammatical inference task through the series of experiments.

In this paper, a novel approach for the estimation of global solar irradiance is proposed based on a combination of empirical correlation and ant colony optimization. Empirical correlation has been used to estimate monthly average of daily global solar irradiance on a horizontal surface. The Ant Colony Optimization (ACO) algorithm has been applied as a swarm-intelligence technique to tune the coefficients of linear and nonlinear empirical models. . The performance of the models is investigated for the estimation of global solar irradiance at four different climatic regions of Iran based on statistical indicators like coefficient of determination (R2) and root mean square error (RMSE). The results obtained from the proposed model are superior in comparison with the other well established models.