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• Propose an effective design method for urban drainage systems (UDSs). • Improve the optimization efficiency for UDS design problems significantly. • Enhance the solution practicality of UDS design problems. • Demonstrate the utility of the proposed method by two real-world UDSs. In the context of climate change and urbanization, urban floods have been one of the major issues around the world, causing significant impacts on the society and environment. To effectively handle these floods, an appropriate design of the urban drainage system (UDS) is highly important as its function can significantly influence the flooding severity and distribution. In recent years, evolutionary algorithms (EAs) have been increasingly used to design UDS due to their great ability in identifying optimal solutions. However, low computational efficiency and low solution practicality (i.e. the final solutions do not satisfy the design criteria) are major challenges for the majority of EA-based methods. To this end, this paper proposes an improved ant colony optimization (ACO, a typical type of EAs) based method to enhance the UDS design effectiveness, where the optimization efficiency is enhanced by initializing the ACO using an approximate design solution identified by the engineering design method, and the solution practicality is improved by explicitly accounting for the design criteria within the optimization using a proposed sampling method. The utility of the proposed method is demonstrated using two real-world UDSs with different system complexities. Results show that the proposed method can identify design solutions with significantly improved efficiency and solution practicality compared to the traditional design approach, with advantages being more prominent for larger UDS design problems. The proposed method can be used by researchers/ practitioners to explore and develop better understanding of the UDS design alternatives under various challenges of climate change and rapid urbanization. [ABSTRACT FROM AUTHOR]

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network's lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively. [ABSTRACT FROM AUTHOR]

Road Sign Detection and Recognition (RSDR) is aimed to enable drivers maintain basic functionality with the aim of identifying and notifying driver through the existing restrictions so that the process is a success on the present widened road. Examples for RSDR include 'traffic light ahead' or 'pedestrian crossing' signs. An innovative RSDR system has been introduced which comprises of pre-processing, edge detection, feature extraction, features selection and Ensemble Fuzzy Support Vector Machine (EFSVM) classifier. Feature selection is carried out successfully by deployment of Ant Colony Optimization (ACO) algorithm to determine most prominent and definitive features. These features are then fed into the ensemble SVM to enable both road side traffic detection as well as recognition. Suggested system's performance is analyzed and evaluated with respect to road signs having a capable recognition rate. [ABSTRACT FROM AUTHOR]

In traditional disassembly sequence planning (DSP), a disassembly sequence is planned based on the description of the product design and personal experience. Research on disassembly sequence planning needs to take into account the relationship between the parts and related factors, such as changes in orientation and tools, so as to optimize the order of the disassembly sequence. Among the numerous feasible disassembly sequences, it is important for researchers to find the most economical DSP, which becomes more difficult as the number of parts increases. In this study, a hybrid bidirectional ant colony optimization (Hybrid BACO) algorithm is proposed and compared with four related algorithms. Simulated cases show that the hybrid BACO algorithm provides a better solution than other ant algorithms. The five ant algorithms are also compared from the viewpoint of a reverse assembly sequence, and the results again show that the Hybrid BACO algorithm provides the best solution quality. • A novel hybrid bidirectional ant colony algorithm for disassembly planning is explored. • A BACO divides the ants into two groups and two routes of the algorithm are integrated. • The proposed algorithm performs better than other ant systems in solution quality. [ABSTRACT FROM AUTHOR]

Abstract An organization uses a symbol as its representation in the market for ease of identification and uniqueness. Logos are used to identify and retrieve the materials, even in a complex environment for further analysis. Algorithms based on support vector machine and neural networks provide better results in retrieval of the document from small dataset. But inlarge data sets the existing models lags in their classification performance. This proposed model uses ant colony optimization (ACO) along with the local descriptor scale-invariant feature transform (SIFT), as a hybrid model for retrieving document from dataset. This hybrid model enhances the performance of the retrieval model in terms of increased efficiency, leading to an accuracy of 95.86% with a high output precision of 97.67%. [ABSTRACT FROM AUTHOR]

• A mathematical model of the UAV route planning problem is established. • A hybrid ACO-IWD algorithm, which combines the advantages of the ACO and IWD algorithms, is proposed to solve the UAV route planning problem. • A novel node selection strategy is designed to guide the attempts of the agents to search for routes in a reasonable direction and increase their search efficiency. • The advantages of the proposed algorithm over the state-of-the-art algorithms are demonstrated by extensive experimental results. Route planning is a crucial element in unmanned aerial vehicle (UAV) systems, particularly in autonomous UAV technology. In the past decades, various algorithms have been proposed for UAV route planning. However, they still have defects, such as stagnation and slow search rates. In this study, a novel hybrid algorithm which integrates ant colony optimization (ACO) and intelligent water drop (IWD) is proposed for UAV route planning. First, the advantages of the IWD and ACO algorithms are combined in an iterative strategy, to ensure mutual cooperation via exchange of information for route optimization. Initially, the water drops optimize the soil and pheromones within the environment simultaneously to generate good approximate solutions and an initial pheromone distribution for the ant colony. Based thereupon, the ant colony roams the solution space to further optimize the routes, thus combining the exploration potential and exploitability of two types of agents. Additionally, a novel node selection strategy is proposed to guide the agents' route planning along a reasonable direction. Compared with state-of-the-art algorithms, the convergence accuracy, success rate, and stability of the proposed algorithm exhibited significant improvements of approximately 8.25%, 4.20%, and 66.20%, respectively. [ABSTRACT FROM AUTHOR]

Abstract Extensible Business Reporting Language (XBRL) is an XML-based language developed for enhancing interoperability among the entities involved in process of business reporting. Although this language is adopted by various regulators all around the world and has contributed greatly to semantic interoperability in this field, the variations between taxonomies and also between elements of instance documents, still cause many inconsistencies between elements. Although some existing approaches suppose the conversion of XBRL to ontologies and then resolve the inconsistencies by applying some mapping techniques, it does not seem practical because of low precision and incompleteness of these conversions. In this paper, a novel approach is proposed which utilizes Ant Colony Optimization (ACO) in order to detect best semantic mappings between inconsistent concepts of two XBRL documents. This approach analyzes the possible mappings with respect to various factors like concept names, all label texts, presentation and calculation hierarchies and so on. This makes the approach capable of finding mappings, which were not easily discoverable otherwise. The proposed approach is implemented and applied to actual XBRL reports. The results are measured with aid of well-known criteria (precision, recall and F-measure) and are compared with the well-known Hungarian algorithm and illustrate the better performance in accordance with these three criteria. [ABSTRACT FROM AUTHOR]

Abstract This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 208 cores over 21 days. The new evolved LSTM cells showed an improvement of 1.34%, reducing the mean prediction error from 5.61% to 4.27% when predicting excessive engine vibrations 10 s in the future, while at the same time dramatically reducing the number of weights from 21,170 to 13,150. The optimized LSTM also performed significantly better than traditional Nonlinear Output Error (NOE), Nonlinear AutoRegression with eXogenous (NARX) inputs, and Nonlinear Box–Jenkins (NBJ) models, which only reached error rates of 15.73%, 12.06% and 15.05%, respectively. Furthermore, the LSTM regularization method was used to validate the ACO. The ACO LSTM out performed the regularized LSTM by 3.35%. The NOE, NARX, and NBJ models were also regularized for cross validation, and the mean prediction errors were 8.70%, 9.40%, and 9.43% respectively, which gives credit for the ant colony optimized LSTM RNN. Highlights • Long Short Term Memory RNN to predict excessive turbine engine vibration events. • Ant Colony Optimization to optimize the best previously used LSTM RNN fixed topology. • Evolved LSTM reduced engine vibration mean prediction error from 6.38% to 5.01%. • ACO dramatically reduced the number of weights from 21,170 to 11,650. [ABSTRACT FROM AUTHOR]

In modern era WSN, data aggregation technique is the challenging area for researchers from long time. Numbers of researchers have proposed neural network (NN) and fuzzy logic based data aggregation methods in Wireless Environment. The main objective of this paper is to analyse the existing work on artificial intelligence (AI) based data aggregation techniques in WSNs. An attempt has been made to identify the strength and weakness of AI based techniques. In addition to this, a modified protocol is designed and developed. And its implementation also compared with other existing approaches ACO and PSO. Proposed approach is better in terms of network lifetime and throughput of the networks. In future an attempt can be made to overcome the existing challenges during data aggregation in WSN using different AI and Meta heuristic based techniques. [ABSTRACT FROM AUTHOR]

As green energy becomes more popular and is integrated into the power system, their integration causes unnecessary burden on transmission lines. It is important to evaluate the performance of transmission lines in the worst case scenario in order to ensure the reliability and security of the power system. The performance of a transmission line is evaluated under contingency conditions, a fuzzy-based composite contingency ranking index is proposed here. There can be no primary key to analyzing the stability of transmission lines based only on the voltage stability index. A composite index is proposed here for overall transmission line security analysis that includes an active and reactive power flow violation index as well as an index of voltage stability. In this paper, contingency ranking of the transmission line and tie-lines of a multi-area power system network is done by using fuzzy logic based composite contingency ranking index. For an IEEE-39 bus system, the indices are calculated using the optimal power flow program in a MATLAB environment. The sink area condition is also improved by analyzing area-wise contingency ranking. An ant colony optimization (ACO) algorithm has been proposed for the first time in order to minimize the transmission active and reactive power losses during contingencies in order to increase the power flow through other transmission lines. The proposed algorithm can be used for any multi area power system network for contingency ranking and reducing the active, reactive line losses. [ABSTRACT FROM AUTHOR]

Pipeline transportation is cost-optimal in refined product transportation. However, the optimization of multi-product pipeline scheduling is rather complicated due to multi-batch sequent transportation and multi-point delivery. Even though many scholars have conducted researches on the issue, there is hardly a model settling the discontinuous constraints in the model as a result of batch interface migration. Moreover, through investigation, there is no self-learning approach to pipeline scheduling optimization at present. This paper considers batch interface migration and divides the model into time nodes sequencing issue and a mixed-integer linear programming (MILP) model with the known time node sequence. And a self-learning approach is proposed through the combination of fuzzy clustering analysis and ant colony optimization (ACO). This algorithm is capable of self-learning, which greatly improves the calculation speed and efficiency. At last, a real pipeline case in China is presented as an example to illustrate the reliability and practicability of the proposed model. [ABSTRACT FROM AUTHOR]

Asset management and automation are acknowledged by distribution utilities as a useful strategy to improve service reliability, but the challenge facing decision makers is how to maximize the long-term return on a project whilst minimizing the investment and operation costs. The objective of this paper is reliability improvement by installing the optimum number of sectionalizing switches at appropriate locations in an electricity distribution network. This is achieved by adding new switches and the relocation of existing switches. Determining the number and location of switches become an optimization problem efficiently solved using an ant colony optimization (ACO) algorithm. The performance of this approach is assessed in the paper and illustrated using various case studies on a typical UK distribution network. Test results show that the proposed ACO methodology provides an optimum solution based on a trade-off region between the reliability indices and cost of multiple switches. [ABSTRACT FROM AUTHOR]

For dimensioning of wound fiber reinforced high pressure vessels the design engineer has to consider various requirements. Since in addition to the strength requirements, in particular the manufacturing constraints must be taken into account. In order to support the design engineer in the complex dimensioning process of such high-pressure vessels a holistic design strategy was developed on the basis of an ant colony optimization algorithm. The aim was to do this winding-optimization without costly dedicated software and instead develop basic design criteria. For this it was necessary in both carry out a holistic comparative quality evaluation of different solutions as well as providing a customizable objective function. [ABSTRACT FROM AUTHOR]

A critical issue in the applications of cognitive diagnosis models (CDMs) is how to construct a feasible test that achieves the optimal statistical performance for a given purpose. As it is hard to mathematically formulate the statistical performance of a CDM test based on the items used, exact algorithms are inapplicable to the problem. Existing test construction heuristics, however, suffer from either limited applicability or slow convergence. In order to efficiently approximate the optimal CDM test for different construction purposes, this paper proposes a novel test construction method based on ant colony optimization (ACO-TC). This method guides the test construction procedure with pheromone that represents previous construction experience and heuristic information that combines different item discrimination indices. Each test constructed is evaluated through simulation to ensure convergence towards the actual optimum. To further improve the search efficiency, an adaptation strategy is developed, which adjusts the design of heuristic information automatically according to the problem instance and the search stage. The effectiveness and efficiency of the proposed method is validated through a series of experiments with different conditions. Results show that compared with traditional test construction methods of CDMs, the proposed ACO-TC method can find a test with better statistical performance at a faster speed. [ABSTRACT FROM AUTHOR]

• AI promotes the sustainability development in higher education. • A soft-computing technique extracts key factors from large amounts of data. • DEMATEL analysis accounts for dependence and feedback among factors. • A framework of AI-enabled Higher Education was proposed. • "Intelligent instructional systems" is the most important criterion. The application of AI in higher education has greatly increased globally in the dynamic digital age. The adoption of developmentally appropriate practices using AI-enabled techniques for facilitating the performance of teaching and learning in the higher education domain is thus a necessary task, especially in the COVID 19 pandemic era. The development and implementation of such techniques involve many factors and are related to the classical multiple criteria decision-making (MCDM) issue; however, these factors surrounding supervisors will confuse them and may result in misjudgment. To clarify the relevant issues and illustrate the cause-and-effect relationships among factors, a hybrid soft-computing technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) and a DEMATEL approach was proposed in this study, which can help decision makers capture the best model necessary for achieving aspiration-level in a higher education management strategy. In the results submitted, the improvement priority for dimensions is based on the measurement of the influences, running in order of tutors for learners (A), skills and competences (B), interaction data to support learning (C), and universal access to global classrooms (D), and which can serve as a reference for the plan of AI-enabled teaching/learning for higher education. [ABSTRACT FROM AUTHOR]

In this paper, to reduce the congestion rate at the city center and increase the traveling quality of experience (QoE) of each user, the framework of long-range autonomous valet parking is presented. Here, an Autonomous Vehicle (AV) is deployed to pick up, and drop off users at their required spots, and then drive to the car park around well-organized places of city autonomously. In this framework, we aim to minimize the overall distance of AV, while guarantee all users are served with great QoE, i.e., picking up, and dropping off users at their required spots through optimizing the path planning of the AV and number of serving time slots. To this end, we first present a learning-based algorithm, which is named as Double-Layer Ant Colony Optimization (DLACO) algorithm to solve the above problem in an iterative way. Then, to make the fast decision, while considers the dynamic environment (i.e., the AV may pick up and drop off users from different locations), we further present a deep reinforcement learning-based algorithm, i.e., Deep Q-learning Network (DQN) to solve this problem. Experimental results show that the DL-ACO and DQN-based algorithms both achieve the considerable performance. [Display omitted] • We aim to optimize path planning of AV and minimize the overall distance. • We propose two solutions, i.e., DL-ACO and DQN-based algorithms. • The DL-ACO can be applied to a new scenario or unfamiliar environment. • The DQN-based model can be applied to scenarios where prior training can be carried out. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a fusion model of an odor sensor and highly advanced optical sensor to evaluate total volatile basic nitrogen (TVB-N) content in chicken meat. Here, the aroma or the odor data variables obtained from the odor sensor i.e. colorimetric sensor and the spectral as well as textural data variables obtained from the optical sensor i.e. HSI, were fused together for further data processing. 36 odor variables obtained via the low-level data abstraction (LLA) were simply concatenated with the 30 texture feature variables obtained by middle/intermediate level data abstraction (ILA) totaling to a 66 variables’ dataset. This approach of multiple level data fusion (MLF) produced the better PCA-BPANN prediction results than either of the individual system did, with the higher R p of 0.8659, lower RMSEP of 4.587 mg/100 g along with the increased calibration model efficacy. Furthermore, the prediction level escalated with R p of 0.8819 and RMSEP of 4.3137 mg/100 g when the data fusion technique was improved by applying Pearson’s correlation analysis and uncorrelated data variables were removed from each of the dataset at the statistical level of significance. This step reduced the data variables but not the original information. Therefore, the results highly encourage multiple sensor fusion and the improved MLF technique for better model performance to evaluate chicken meat’s freshness. [ABSTRACT FROM AUTHOR]

Design changes are unavoidable in new mechanical product development, and the propagation of changes imposes negative impacts on the design cycle and cost. Due to the non-uniqueness of changing propagation paths, searching for the optimal change propagation path with minimum change-related impact tends to be a serious challenge before the implementation of the change. In this paper, a mathematical programming model is presented to route change propagation. First, the design change analysis model (DCAM) is built based on the design property network. In the DCAM, the design properties are connected by linkages, and the weights of these linkages are objectively estimated by mining the change records. In addition, the change propagation intensity (CPI) is defined by quantifying the change propagation impact. The CPI is indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal influence propagation path, which corresponds to the minimized maximum of accumulated CPI, is sought with a modified ant colony optimization (ACO) algorithm. A case study and solution comparison verify the feasibility and validity of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a newly developed hybrid optimization algorithm for solving the problem of Economic Dispatch (ED) for a multi-generator system. The hybrid algorithm named ACO–ABC–HS combines the framework of Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Harmonic Search (HS) algorithms to find the optimized solution for the system. The ACO algorithm is used to find the initial solution set, the ABC algorithm is employed to test and improve each of the probable solutions provided by the ACO module, while the HS module is used to discard the inferior solutions from the solution set and replace them with better ones. The performance of this hybrid algorithm is compared with those of conventional ED solving techniques like Gradient Search as well as other evolutionary algorithms namely ABC, ACO, HS and Particle Swarm Optimization (PSO). Valve point loading, environmental emissions, line losses and ramping rate constraints have been included in the ED analysis to provide more practical results. The algorithm’s performance is also tested for Multi-Area Economic Dispatch (MAED) with tie-line constraints. The results obtained clearly point out the superiority of the hybrid algorithm in finding out the optimum results, while satisfying the constraints of minimizing the generation costs, reducing the emissions as well as tie-line costs and transmission losses. [ABSTRACT FROM AUTHOR]

Up to now, there is no available sensor which can assess meat quality based on a single measuring process. Currently, the pH values measured 45 min (pH 45 ) as well as 24 h (pH 24 ) postmortem (p.m.) are used as quality indicators. The herein presented approach aims to tackle this problem by combining Raman spectroscopy with the ant colony optimization (ACO) metaheuristics that included a priori metabolic knowledge of pH-correlated Raman peaks in the modeling process. For the validation of this method, Raman spectra and pH values from 96 porcine hams recorded in a slaughterhouse 1–2 h p.m. as well as 24 h p.m. were used. By applying the ACO procedure, a segment of 25 wavenumbers out of a full Raman spectrum of 1024 wavenumbers was selected for the prediction of the pH value. For the segments, different fingerprint locations were selected according to the different metabolic information required to predict pH 45 and pH 24 . From the pre-rigor spectra, the pH 45 can be predicted with a root mean square error of cross-validation (RMSECV) of 0.13 pH units and the pH 24 with an RMSECV of 0.21 pH units. Similar results were achieved with the Raman spectra measured after 24 h. Hence, it can be concluded that the meat's quality markers pH 45 and pH 24 information is inherently present in both, the pre-rigor (1–2 h) and the post-rigor (24 h) Raman spectra. Additionally, the results of the pH decline prediction with a RMSECV of 0.21 pH units suggest that this information could be virtually present in any spectra taken within 24 h p.m. This might be of interest for the meat production industry where assessing meat quality in a fast and non-invasive way is desired. [ABSTRACT FROM AUTHOR]

RIPPER is certainly one of the best rule induction algorithms. In RIPPER, the order in which the rules are learned is important because the first rule to be fired determines the class of the instance. However, the correct class may be identified by another rule further down the list, which is ignored and, thus never examined. This paper offers a contribution to address the mentioned shortcoming. An Ant Colony Optimization (ACO) algorithm is developed for finding the optimal order of rules in the decision list. This algorithm is called ACO for Rule Induction (ACORI). To the best of our knowledge, this is the first paper that devises an optimization method to determine the (near) optimal order of rules in the decision list. The performance of the proposed algorithm is compared to that of RIPPER using 10 data sets. Experimental results and non-parametric statistical tests show that the proposed algorithm significantly outperforms the original RIPPER. [ABSTRACT FROM AUTHOR]

Content Based Image Retrieval is one of the most promising method for image retrieval where searching and retrieving images from large scale image database is a critical task. In Content Based Image Retrieval many visual feature like color, shape, and texture are extracted in order to match query image with stored database images. Matching the query image with each image of large scale database results in large number of disc scans which in turns slows down the systems performance. The proposed work suggested an approach for post clustering Content Based Image Retrieval, in which the database images are clustered into optimized clusters for further retrieval process. Various clustering algorithms are implemented and results are compared. Among all, it is found that hybrid ACPSO algorithm performs better over basic algorithms like k-means, ACO, PSO etc. Hybrid ACPSO has the capability to produce good cluster initialization and form global clustering. This paper discusses work-in-progress where we have implemented till clustering module and intermediate results are produced. These resulted clusters will further be used for effective Content Based Image Retrieval. [ABSTRACT FROM AUTHOR]

Classification is a supervised learning task where a training set is used to construct a classifi- cation model, which is then used to predict the class of unforeseen test instances. It is often beneficial to use only a subset of the full training set to construct the classification model, and Instance Selection is the task of selecting the most appropriate subset of the training set. In many cases, the classification model induced from the reduced training set can have bet- ter predictive accuracy on test instances. ADR-Miner is a recently introduced Ant Colony Optimization algorithm for Instance Selection that aims to produce classification models with improved test set predictive accuracy. In this paper, we present an extension of ADR-Miner, where one classification algorithm is employed in the instance selection process, and potentially a different algorithm is employed in the final model construction phase. We evaluate perfor- mance using 37 UCI datasets, and we note the combinations of algorithms which produce the best results. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods. [ABSTRACT FROM AUTHOR]

An accurate estimation of aquifer hydraulic parameters is required for groundwater modeling and proper management of vital groundwater resources. In situ measurements of aquifer hydraulic parameters are expensive and difficult. Traditionally, these parameters have been estimated by graphical methods that are approximate and time-consuming. As a result, nonlinear programming (NLP) techniques have been used extensively to estimate them. Despite the outperformance of NLP approaches over graphical methods, they tend to converge to local minima and typically suffer from a convergence problem. In this study, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) methods are used to identify hydraulic parameters (i.e., storage coefficient, hydraulic conductivity, transmissivity, specific yield, and leakage factor) of three types of aquifers namely, confined, unconfined, and leaky from real time–drawdown pumping test data. The performance of GA and ACO is also compared with that of graphical and NLP techniques. The results show that both GA and ACO are efficient, robust, and reliable for estimating various aquifer hydraulic parameters from the time–drawdown data and perform better than the graphical and NLP techniques. The outcomes also indicate that the accuracy of GA and ACO is comparable. Comparing the running time of various utilized methods illustrates that ACO converges to the optimal solution faster than other techniques, while the graphical method has the highest running time. [ABSTRACT FROM AUTHOR]

Recent technologies and innovations have encouraged users to adopt cloud-based environment. Network intrusion detection (NID) is an important method for network system administrators to detect various security holes. The performance of traditional NID methods can be affected when unknown or new attacks are detected. For instance, existing intrusion detection systems may have overfitting, low classification accuracy, and high false positive rate (FPR) when faced with significantly large volume and variety of network data. For that reason, this system has been agreed by many establishments to allure the users with its suitable features. Because of its design, it is exposed to malicious attacks. An Intrusion Detection System (IDS) is required to handle these issues which can detect such attacks accurately in a cloud environment. To analyze the IDS datasets some of the predominant choices are Deep learning and Machine learning (ML) algorithms. By adopting nature-inspired algorithms, the problems concerning the data quality and the usage of high-dimensional data can be managed. In this study the datasets KDD Cup 99 and NSL-KDD are used. The dataset is cleaned using the min-max normalization technique and it is processed using the 1-N encoding approach for achieving homogeneity. Dimensionality reduction is done using the Ant colony optimization (ACO) algorithm and further processing is done using the deep neural network (DNN). To minimize the energy consumption we have adopted the Dynamic Voltage and Frequency Scaling (DVFS) mechanism to the system. The main reason to set up this concept is to develop a balance between the energy consumption and the time of different modes of VMs or hosts. The proposed model is validated and compared with ACO and Principal component analysis (PCA)-based (Naïve Bayes) NB models, the experimental outcomes proved the superiority of the ACO-DNN model over the existing methods. • Cloud computing based IDS using high dimensional data. • Max-Min normalization and 1-N encoding is suggested. • High dimensionality is reduced using nature inspired algorithm- ACO. • To minimize the energy consumption, DVFD method is used. • The proposed method is compared to state-of-the-art works like ACO, PCA and NB models. [ABSTRACT FROM AUTHOR]

Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are treated as graph nodes to construct a graph model and are fully connected to each other. In this graph, each node has two sub-nodes, one for selecting and the other for deselecting the feature. Ant colony algorithm is used to select nodes while ants should visit all features. The use of several statistical measures is examined as the heuristic function for visibility of the edges in the graph. At the end of a tour, each ant has a binary vector with the same length as the number of features, where 1 implies selecting and 0 implies deselecting the corresponding feature. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), CatfishBPSO, Improved Binary Gravitational Search Algorithm (IBGSA), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods. [ABSTRACT FROM AUTHOR]

Environmental legislation, with its increasing pressure on the energy sector to control greenhouse gases, is a driving force to reduce CO2 emissions, forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the economic power dispatch problem has become a multi-objective optimization problem. This paper sets up an new algorithm, ant colony optimization (ACO), to solve the optimization with combined economic emission dispatch. This problem has been formulated as a multi-objective problem by considering economy, emission and wind energy simultaneously. The feasibility of the proposed approach was tested on 3-unit and 15-unit systems. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques. [ABSTRACT FROM AUTHOR]

Highlights: [•] The paper reviews applications of hybrid wavelet–AI models in hydro-climatology. [•] Efficiency of hybrid models regarding processes and model type were investigated. [•] Survey shows wavelet pre-processor capability to enhance AI models performance. [•] Organized information about wavelet–AI models can show the future research pass. [ABSTRACT FROM AUTHOR]

Abstract: Based on Ant Colony Optimization for Continuous Domains ( ) and Particle Filter (PF), an intelligent particle filter, namely Ant Colony Estimator (ACE), is proposed in this paper. Modeling and search abilities of are incorporated into the standard particle filtering framework to improve the estimation performance and overcome the well-known problems of Degeneracy and Sample Impoverishment. operators implicitly use measurement and previous particle information, to generate probably better particles. Simulation results are given for two examples and ACE is compared to other types of particle filters. The obtained results confirm the efficiency and applicability of ACE. [Copyright &y& Elsevier]

The emergence of unmanned aerial vehicle (UAV)-enabled technology in the Internet of Things (IoT) era leads to a significant reduction in data collection delays when accumulating sensory data from ground IoT nodes (INs). As a flying data collector, the UAV hovers at a limited number of Access Points (APs) to collect data, outperforming ground data collectors in terms of transmission energy consumption, data delivery reliability, and timeliness. However, the INs have a finite amount of buffer capacity to store the data that must be collected before they overflow. As a result, the data gathering route for UAVs should be adaptable to INs' buffer deadline in order to minimize data loss. In this paper, a buffer-aware dynamic UAV trajectory design protocol is proposed for data collection from resource-constrained INs. A distributed AP nomination strategy is proposed in order to reduce UAV hovering latency. Furthermore, using machine learning approaches, a modified ant colony optimization algorithm is constructed to minimize the data loss penalty due to buffer overflow. Finally, the performance of the proposed scheme is evaluated against several state-of-the-art protocols with regards to parameters such as data loss penalty, packet delivery ratio, and network lifetime. [Display omitted] • The UAV Access points selected ensure one-hop connectivity with IoT nodes. • To reduce the data collection latency, a minimal set of Access points are nominated. • A modified ACO algorithm is used to create a dynamic UAV trajectory. [ABSTRACT FROM AUTHOR]

Highlights: [•] Proposed a hybrid algorithm to classify the breast cancer images into two outcomes: Benign or Malignant. [•] An improved version of the classical ant-based clustering algorithm to segment the region of interest of breast images. [•] The overall accuracy offered by the employed hybrid technique confirms that the effectiveness and performance of the proposed hybrid system is high. [Copyright &y& Elsevier]

Highlights: [•] Major assumption-entropy is the driving force similar to the convection forces. [•] Outperforms the hybrid ACO2/PSO/BBO classifier. [•] Classifies homogeneous regions more efficiently than others developed till date. [•] Proves to be the best known classifier developed till date. [ABSTRACT FROM AUTHOR]

Abstract: Estimating tender data for specific project is the most essential part in construction areas as of contractor’s view such as: proposed project duration with corresponding gross value and cash flows. This paper focuses on how to calculate tender data using Optimizing Strategy Software (OSS) for repetitive construction projects with identical activity’s duration in case of single number of crew such as: project duration, project/bid price, project maximum working capital, and project net present value of the studied project. A simplified multi-objective optimization software (OSS) will be presented that creates best tender data to contractor compared with more feasible options generated from multi-mode resources in a given project. OSS is intended to give more scenarios which provide practical support for typical construction contractors who need to optimize resource utilization in order to minimize project duration, project/bid price, and project maximum working capital while maximizing its net present value simultaneously. OSS is designed by java programing code system to provide a number of new and unique capabilities, including: (1) Ranking the obtained optimal plans according to a set of planner specified weights representing the relative importance of duration, price, maximum working capital and net present value in the analyzed project; (2) Visualizing and viewing the generated optimal trade-off; and (3) Providing seamless integration with available project management calculations. In order to provide the aforementioned capabilities of OSS, the system is implemented and developed in four main modules: (1) A user interface module; (2) A database module; (3) A running module; (4) A connecting module. At the end of the paper, an illustrative example will be presented to demonstrate and verify the applications of the proposed software (OSS) to an optimization expressway of repetitive construction project. [Copyright &y& Elsevier]

Highlights: [•] MACO is a robust and stable algorithm in dynamic topology optimization problems. [•] MACO is more effective and suitable comparing with the standard ACO. [•] Convergence rate of MACO is competitive with that of BESO. [•] Optimal natural frequencies from MACO are slightly higher than those from BESO. [ABSTRACT FROM AUTHOR]

Abstract: Studies related to biologically inspired optimization techniques, which are used for daily operational scheduling of thermoelectric generation systems, indicate that combinations of biologically inspired computation methods together with other optimization techniques have an important role to play in obtaining the best solutions in the shortest amount of processing time. Following this line of research, this article uses a methodology based on optimization by an ant colony to minimize the daily scheduling cost of thermoelectric units. The proposed model uses a Sensitivity Matrix (SM) based on the information provided by the Lagrange multipliers to improve the biologically inspired search process. Thus, a percentage of the individuals in the colony use this information in the evolutionary process of the colony. The results achieved through the simulations indicate that the use of the SM results in quality solutions with a reduced number of individuals. [Copyright &y& Elsevier]

Abstract: This paper proposes genetic ant algorithm through the research of the traditional genetic algorithm and ant colony optimization. This algorithm use the results of the genetic algorithm to initialize the pheromone distribution,use its strong adaptability and rapid global convergence and then get the optimal solution through the colony algorithm that has parallelism, positive feedback system and good solution efficiency. The simulation results of 0-1 knapsack and QoS demonstrate that this algorithm has higher converging speed, stability and global optimization ability. [Copyright &y& Elsevier]

Abstract: To build awareness of the development of ant colony optimization (ACO), this study clarifies the citation and bibliometric analysis of research publications of ACO during 1996–2010. This study analysed 12,960 citations from a total of 1372 articles dealing with ACO published in 517 journals based on the databases of SCIE, SSCI and AH&CI, retrieved via the Web of Science. Bradford Law and Lotka’s Law, respectively, examined the distribution of journal articles and author productivity. Furthermore, this study determines the citation impact of ACO using parameters such as extent of citation received in terms of number of citations per study, distribution of citations over time, distribution of citations among domains, citation of authors, citation of institutions, highly cited papers and citing journals and impact factor of 12,960 citations. This study can help researchers to better understand the history, current status and trends of ACO in the advanced study of it. [Copyright &y& Elsevier]

Abstract: Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy. [Copyright &y& Elsevier]

Abstract: The minimum weight vertex cover problem is an interesting and applicable NP-hard problem that has been investigated from many different aspects. The ant colony optimization metaheuristic is a relatively new technique that was successfully adjusted and applied to many hard combinatorial optimization problems, including the minimum weight vertex cover problem. Some kind of hybridization or exploitation of the knowledge about specific problem often greatly improves the performance of standard evolutionary algorithms. In this article we propose a pheromone correction heuristic strategy that uses information about the best-found solution to exclude suspicious elements from it. Elements are suspicious if they have some undesirable properties that make them unlikely members of the optimal solution. This hybridization improves pure ant colony optimization algorithm by avoiding early trapping in local convergence. We tested our algorithm on numerous test-cases that were used in the previous research of the same problem and our algorithm uniformly performed better, giving slightly better results in significantly shorter time. [Copyright &y& Elsevier]

Abstract: Metal Oxide Surge Arrester (MOSA) accurate modeling and its parameter identification are very important aspects for arrester allocation, system reliability determination and insulation coordination studies. In this paper, Modified Particle Swarm Optimization (MPSO) algorithm is used to estimate the parameters of surge arrester models. The convergence to the local optima is often a drawback of the Particle Swarm Optimization (PSO). To overcome this demerit and improve the global search capability, Ant Colony Optimization (ACO) algorithm is combined with PSO algorithm in the proposed algorithm. The suggested algorithm selects optimum parameters for the arrester model by minimizing the error among simulated peak residual voltage values given by the manufacturer. The proposed algorithm is applied to a 120 kV MOSA. The validity and the accuracy of estimated parameters are assessed by comparing the predicted residual voltage with experimental results. [Copyright &y& Elsevier]

Abstract: Sensor deployment is one of the most important issues in wireless sensor networks, because an efficient deployment scheme can reduce the deployment cost and enhance the detection capability of the wireless sensor networks. In addition, it can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. Ant colony optimization (ACO) algorithm provides a natural and intrinsic way of exploration of search space for multiple knapsack problem (MKP). In this work, we consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. We model the deployment problem as the multiple knapsack problem. Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. The simulations show that our algorithm can prolong the lifetime of the network. [Copyright &y& Elsevier]

Abstract: This paper presents a novel clustering and pattern classification of power signal disturbances using a variant of S-transform, which is termed as a phase corrected wavelet transform. This variant is obtained by taking the inverse Fourier transform of S-transform and is known as time–time transform (TT-transform). The output from the TT-transform based power signal processing is a set of relevant features that is used for visual localization, detection, and disturbance pattern classification. The TT-transform is a method of dividing a primary time series into a set of secondary, time-localized time series, through use of a translatable, scalable Gaussian window. These secondary time series resemble ordinary windowed time series, except that higher frequencies are more strongly concentrated around the midpoint of the Gaussian, as compared with lower frequencies. In this paper the TT-transform is generalized to accommodate arbitrary scalable windows. The generalized TT-transform can be useful in resolving the times of event initiations when used jointly with a related time-frequency distribution, the generalized S-transform. The extracted features are the input to a fuzzy C-means clustering algorithm (FCA) to generate a decision tree for power signal disturbance pattern classification. To improve the pattern classification of the fuzzy C-means decision tree, the cluster centers are updated using a hybrid ant colony optimization technique (HACO). Further a comparative assessment of power signal disturbance pattern classification accuracy for different population based optimization approach like the genetic algorithm (GA) and particle swarm optimization technique are presented in this paper. The various computational simulations presented in this paper reveal significant improvement in the pattern classification accuracy, the average number of function evaluations and processing time, etc. [ABSTRACT FROM AUTHOR]

Abstract: Airline crew scheduling is an NP-hard constrained combinatorial optimization problem, and an effective crew scheduling system is essential for reducing operating costs in the airline industry. Ant colony optimization algorithm (ACO) has successfully applied to solve many difficult and classical optimization problems especially on traveling salesman problems (TSP). Therefore, this paper formulated airline crew scheduling problem as Traveling Salesman Problem and then introduce ant colony optimization algorithm to solve it. Performance was evaluated by performing computational tests regarding real cases as the test problems. The results showed that ACO-based algorithm can be potential technique for airline crew scheduling. [ABSTRACT FROM AUTHOR]

Abstract: Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO) that causes inter-carrier interference (ICI). In this paper, a particle filter (PF) method augmented with ant colony optimization (ACO) is developed to estimate the CFO. The ACO for continuous domains is incorporated into PF to optimize the sampling process. Unlike the standard PF, resampling is not required in the method. Moreover, it does not require the noise distribution. Simulation results show that the proposed method is effective when estimating the CFO and can effectively combat the effect of ICI in OFDM systems. [Copyright &y& Elsevier]

Abstract: With the growing trend toward remote security verification procedures for telephone banking, biometric security measures and similar applications, automatic speaker verification (ASV) has received a lot of attention in recent years. The complexity of ASV system and its verification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we concentrate on optimizing dimensionality of feature space by selecting relevant features. At present there are several methods for feature selection in ASV systems. To improve performance of ASV system we present another method that is based on ant colony optimization (ACO) algorithm. After feature reduction phase, feature vectors are applied to a Gaussian mixture model universal background model (GMM-UBM) which is a text-independent speaker verification model. The performance of proposed algorithm is compared to the performance of genetic algorithm on the task of feature selection in TIMIT corpora. The results of experiments indicate that with the optimized feature set, the performance of the ASV system is improved. Moreover, the speed of verification is significantly increased since by use of ACO, number of features is reduced over 80% which consequently decrease the complexity of our ASV system. [Copyright &y& Elsevier]

Abstract: Three-dimension path planning of uninhabited combat air vehicle (UCAV) is a complicated optimal problem, which mainly focuses on optimizing the flight route considering the different types of constrains under complicated combating environments. A new hybrid meta-heuristic ant colony optimization (ACO) and differential evolution (DE) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the pheromone trail of the improved ACO model during the process of ant pheromone updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the three-dimensional mesh while avoiding the threats area and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic ACO. The realization procedure for this hybrid meta-heuristic approach is also presented in detail. In order to make the optimized UCAV path more feasible, the к-trajectory is adopted for smoothing the path. Finally, series experimental comparison results demonstrate that this proposed hybrid meta-heuristic method is more effective and feasible in UCAV three-dimension path planning than the basic ACO model. [Copyright &y& Elsevier]