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The automated design of Ant Colony Optimization (ACO) algorithms has become increasingly significant, particularly in addressing complex combinatorial optimization problems. Although existing methods have achieved some success, they still face limitations, particularly the high dependency on expert knowledge, pre-solved data, and challenges in interpretability. Genetic Programming (GP), as a proven technology, has shown potential in optimizing the automated design state transition rules of ACO. However, existing research on GP-ACO is insufficient, particularly in terms of experimental validation and systematic evaluation. To address these issues, this study conducts comprehensive experiments to explore several key questions: the generality of GP-ACO on homogeneously distributed maps, the impact of different ACO variants on the learning capabilities of GP-ACO, the effect of 2-opt local search on the learning capabilities of GP-ACO, the enhancement of learning capabilities through the addition of more global information, and the interpretability of GP-ACO. The findings indicate that GP-ACO exhibits robust generality; variations among ACO variants have minimal impact on learning performance; 2-opt local search can somewhat diminish the performance of GP-ACO in the Max–Min Ant System; additional global information can significantly enhance the learning capabilities of GP-ACO; and GP-ACO has good interpretability. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology has facilitated the deployment of autonomous sensors in remote and challenging environments, enabling substantial advancements in environmental monitoring and data collection. IoT sensors continuously gather data, transmitting it to a central Base Station (BS) via designated Cluster Heads (CHs). However, data flow encounters frequent congestion at CH nodes, negatively impacting network performance and Quality of Service (QoS). This paper introduces a novel congestion control strategy tailored for Wireless Sensor Networks (WSNs) to balance energy efficiency and data reliability. The proposed approach follows an eight-step process, integrating Generative Adversarial Networks (GANs) for enhanced clustering and Ant Colony Optimization (ACO) for optimal CH selection and routing. GANs simulate realistic node clustering, achieving better load distribution and energy conservation across the network. ACO then selects CHs based on energy levels, distance, and network centrality, using pheromone-based routing to adaptively manage data flows. A congestion factor (CF) threshold is also incorporated to dynamically reroute traffic when congestion risks arise, preserving QoS. Simulation results show that this approach significantly improves QoS metrics, including latency, throughput, and reliability. Comparative evaluations reveal that our method outperforms existing frameworks, such as Fuzzy Structure and Genetic-Fuzzy (FSFG), Deep Reinforcement Learning Cache-Aware Congestion Control (DRL-CaCC), and Adaptive Cuckoo Search Rate Optimization (ACSRO). [ABSTRACT FROM AUTHOR]

In recent years, reservoir models based on the Connection Element Method (CEM) have gained extensive application in reservoir development. This mesh-free modeling approach effectively captures all flow paths and flow-splitting coefficients between nodes, providing a clear view of flow interactions and accurately identifying primary connectivity pathways between injection and production wells. However, the traditional approach of traversing flow paths and splitting coefficients imposes a significant computational load, particularly when applied to large reservoirs with numerous virtual wells. To enhance simulation efficiency, this paper introduces a novel method leveraging the Ant Colony Optimization (ACO) algorithm to efficiently identify the path with the highest splitting coefficient between well pairs. This approach rapidly calculates and filters the dominant connectivity paths between injection and production wells in CEM models. A comparative analysis shows that, while the ACO algorithm provides limited benefit with a small number of connectivity paths, it significantly outperforms the conventional depth-first search algorithm as the number of experimental wells increases. [ABSTRACT FROM AUTHOR]

Nonlinear pushover analysis involves an extremely iterative process necessary for satisfying the design requirements of performance-based codes. This analysis also demands significant computational resources and advanced scientific efforts. In this study, we introduce a computer-based method for 2D-braced steel buildings that incorporates pushover analysis, optimization techniques, and optimality criteria methods to automatically design the pushover drift performance. An ant colony metaheuristic optimization algorithm is employed to achieve optimal performance-based designs for columns, chevron braces, and beams in steel moment frames. The initial phase includes implementing optimization codes in MATLAB and OpenSees for conducting the nonlinear static analysis of the 2D-braced steel frames. Several optimal configurations are produced for each brace and frame by addressing the nonlinear optimization problem. In the second step, a nonlinear pushover analysis is conducted in accordance with the provisions of the FEMA 356 code. This analysis takes into account constraints on relative displacement and plastic hinge rotation to ensure that the structure achieves the specified performance levels. Finally, the third step involves selecting the optimal design for each frame and subsequently plotting the pushover, drift and convergence curves for each frame and performance levels. This selection process ultimately aims to satisfy the criteria of performance-based design, including life safety, collapse prevention, and immediate occupancy, while minimizing the total weight for three 2D steel chevron frames: a 5-story, a 9-story, and a 13-story configuration. [ABSTRACT FROM AUTHOR]

With the rapid growth and development of Internet of Things (IoT) and smart mobile devices, the volume of data generated at the network edge has increased significantly. Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational limitations of these devices by bringing cloud capabilities closer to end users. However, MEC still faces two critical challenges: edge server placement (ESP) and load distribution. The first problem involves determining the optimal locations for a set of heterogeneous computational servers within a given network of base stations (BSs) and their associated workload. The objective is to strategically position these servers to ensure efficient computational resource allocation and to enhance the quality of service (QoS) for end users. The second problem is the distribution of computational tasks/requests, which itself comprises two sub-problems: (a) identifying the most suitable server for the requests arriving at each BS, and (b) scheduling these requests on the selected servers. This paper addresses these challenges through a comprehensive approach. We model these problems using a mixed-integer nonlinear programming (MINLP) framework. For the ESP problem, the goal is to achieve load balancing among servers. For the load distribution problem, the objectives are to minimize response time and ensure that user request deadlines are met. To address these problems, we propose an ant colony optimization (ACO) algorithm to efficiently solve the ESP problem, ensuring balanced load among servers. For the load distribution problem, we introduce two heuristic algorithms: one for selecting the most suitable server to distribute incoming workloads from devices, and another for scheduling requests on each server based on their urgency and importance. We validate our proposed approach through extensive experiments using real-world data from Shanghai Telecom base stations. The experimental results demonstrate that our approach significantly outperforms state-of-the-art methods in terms of load standard deviation, average request response time, and the percentage of deadlines successfully met. Specifically, our method improves load balancing by 57.6%, reduces the average response time by 50.7%, and increases deadline compliance by 35.2%. These findings underscore the efficacy of our proposed methods in enhancing MEC system performance, thereby delivering superior QoS and user experience in IoT and mobile computing applications. [ABSTRACT FROM AUTHOR]

Features within the dataset carry a significant role; however, resource utilization, prediction-time, and model weight are increased by utilizing high-dimensional data in intrusion-detection paradigm. This paper aims to design a novel lightweight intrusion detection system in two phases utilizing a swarm intelligence-based technique. In 1st-phase, essential features are selected using particle swarm optimization algorithm by considering imbalanced dataset. Ant colony optimization algorithm is utilized in 2nd-phase for extracting information-rich and uncorrelated features. Additionally, genetic algorithm is employed for fine-tuning each detection model. Proposed model's performance is evaluated on different base and ensemble classifiers, and it is observed that xgboost achieves best accuracy with 90.38%, 92.63%, and 97.87% on NSL-KDD, UNSW-NB15, and CSE-CIC-IDS2018 datasets, respectively. The proposed model also outperforms other traditional dimensionality reduction and state-of-the-art approaches with statistical validation. This paper also analyses objective function of each metaheuristic algorithm used in this paper, applying convergence graphs, box, and swarm plots. [ABSTRACT FROM AUTHOR]

The rapid growth in the smart era of Internet of Things (IoT) relies on the various applications that lead to the design wide range of routing protocols utilizing Machine learning techniques. Third party interference in the open network to perform malicious activities by using location information of the node is high. Many researchers have designed a wide range of protocols to improve security and energy efficiency but the dynamic nature of the Internet of Things suppressed the performance of those algorithms. This may lead to data drop, node death, delay, less network lifetime, and increased third party malicious activities. In this paper, a novel routing mechanism is developed to preserve source location privacy and prevent adversaries from doing backtracking attacks and traffic analysis for energy preservation. The proposed model consists of two key functions Node/Network Condition based Dynamic Phantom Node selection (NCDPNS) and Ant colony optimization Algorithm Aided Multi-Path based Routing (ACOMPR). Here, NCDPNS selects the phantom node based on the node/network conditions like node availability, link availability, node energy level, distance from other nodes in the network, and number of neighboring hops to preserve the location privacy. ACOMPR selects the path based on the ant colony optimization algorithm to choose more than one path for data transmission with very less common resources shared among multiple paths between the source and destination for energy efficient data transmission. The proposed mechanism is achieving the source location privacy at the first stage and energy efficient routing at the second stage. The proposed mechanism is implemented using a Network Simulator-2 (NS2) simulator with predefined network parameters. The results depict that it achieves high throughput, less delay, increased network lifetime, and low energy dissipation for data transmission by preserving the location of the node. The dynamic nature of the IoT is considered in the proposed work to make it more suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) is the process of mapping virtual machines to physical machines, which is very important for resource utilization in cloud data centres. As such, VM placement is an NP-class problem, and therefore, researchers have frequently applied meta-heuristics for this. In this study, we applied a hybrid meta-heuristic that combines ant colony optimisation (ACO) and grey wolf optimisation (GWO) to minimise resource wastage, energy consumption, and bandwidth usage. The performance study of the proposed work is conducted on variable number of virtual machines with different resource correlation coefficients. According to the observations, there is 2.85%, 7.61%, 15.78% and 19.41% improvement in power consumption, 26.44%, 57.83%, 77.90% and 83.89% improvement in resource wastage and 2.94%, 8.20%, 9.99% and 10.72% improvement in bandwidth utilisation as compared to multi-objective GA, ACO, FFD and random based algorithm respectively. To study the convergence of the proposed method, it is compared with few recent hybrid meta-heuristic algorithms, namely ACO–PSO, GA–PSO, GA–ACO and GA–GWO which exhibits that the proposed hybrid method converges faster. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-strategy genetic algorithm (DIHA-GA) to address the is- sues of local optima and low efficiency in solving multi-robot task allocation (MRTA) using genetic algorithm (GA). Firstly, a dual chromosome coding strategy was adopted to simplify the coding process. Secondly, the population was divided into three parts to enhance the quality of chromosomes while maintaining randomness. Then, heuristic crossover operators were used to expand the search range of the solution and increase the algorithm's ability to jump out of local optima. Finally, adaptive crossover probability and mutation probability were used to make the algorithm find the optimal solution faster. The results showed that in the cases of 20 and 40 tasks, compared to the hybrid particle swarm optimization (HPSO), the average distance of the pro- posed DIHA-GA is reduced by 14. 46 m and 17.36 m. respectively, and the minimum distance is reduced by 14.89 m and 20,86 m, respectively. This indicates that the solution obtained by DI-HA-GA is closer to the optimal solution, The average distance obtained by DIHA-GA in this article is reduced by 21, 32 m and 18.73 m respectively compared to the improved ant colony optimization (IACO), and the minimum distance is reduced by 23.43 m and 22. 32 m respectively. This is due to the premature convergence of IACO and its difficulty in jumping out of local optima. The effectiveness of DIHA-GA in solving MRTA problems has been verified through comparison. [ABSTRACT FROM AUTHOR]

A more extensive attack surface for cyber incursions has resulted from the fast expansion of Internet of Things (IoT) devices, calling for more stringent security protocols. This research introduces a new method for protecting Internet of Things (IoT) networks against intrusion assaults by combining Game Theory with Ant Colony Optimization (ACO). Various cyber dangers are becoming more common as a result of the networked nature and frequently inadequate security measures of IoT devices. Because these threats are ever-changing and intricate, traditional security measures can't keep up. An effective optimization method for allocating resources and pathfinding is provided by ACO, which takes its cues from the foraging behavior of ants, while Game Theory provides a strategic framework for modeling the interactions between attackers and defenders. Attackers and defenders in the proposed system are modeled as players in a game where the objective is to maximize their payout. Minimizing damage by anticipating and minimizing assaults is the defender's task. The monitoring pathways are optimized and resources are allocated effectively with the help of ACO. In response to changes in network conditions, the system dynamically modifies defensive tactics by updating the game model in real time. The results of the simulation show that the suggested method successfully increases the security of the Internet of Things. Compared to 87.4% using conventional approaches, the detection accuracy increased to 95.8%. From 10.5 seconds down to 7.3 seconds, the average reaction time to identified incursions was cut in half. Furthermore, there was a 20% improvement in resource utilization efficiency, guaranteeing that defensive and monitoring resources were allocated optimally. Internet of Things (IoT) network security is greatly improved by combining Game Theory with Ant Colony Optimization. In addition to enhancing detection accuracy and reaction times, this combination method guarantees resource efficiency. The results demonstrate the practicality of this approach, which offers a solid foundation for protecting Internet of Things devices from ever-changing cyber dangers. [ABSTRACT FROM AUTHOR]

In new technologies like fog computing, edge computing, cloud computing, and the Internet of Things (IoT), cybersecurity concerns and cyber-attacks have surged. The demand for better threat detection and prevention systems has increased due to the present global uptick in phishing and computer network attacks. In order to identify irregularities and attacks on the network, which have increased in scale and prevalence, threat identification is essential. However, the community is forced to investigate and create novel threat detection approaches that are capable of detecting threats using anomalies due to the increase in network threats, the growth of new methods of attack and computations, and the requirement to ensure security measures. A novel mechanism is employed to identify threats in a data based on optimized deep learning. The main aim of this paper is the usage of data classification system based on Deep Learning (DL). The proposed mechanism employed the TCP (Transmission Control Protocol) communication protocol to extract data from loud IoT (Internet of Things) networks for the purpose of threat detection. To perform feature extraction an Ant Colony Optimization (ACO) is utilised, through Recurrent Neural Network (RNN), the attacks in data are classified and detected. Additionally, the suggested approach has been evaluated and trained using the BOUN DDoS contemporary dataset, which comprises a variety of attack types and allows for the effectiveness of the framework to be determined to compare it to previous approaches. The Findings indicate that the suggested approach achieved higher accuracy in DDoS attack identification in comparison with Traditional deep learning methods. The existing method detects the generic attack with lower efficiency however; the proposed mechanism achieves better accuracy in both the detection of the DDoS attack and the detection of regular traffic. [ABSTRACT FROM AUTHOR]

The evolution of the management of smart grids, which mainly aims to integrate various high-capacity energy sources, more specifically photovoltaic units, on the one hand, as well as optimal control of loads connected to the network in order to ensure coordination between production and consumption, leads not only to the management of production units but also to the management of consumption power using different static converters. This makes this operation possible, and feasible but complex because of their effects on the quality of energy and the need for reliable control. There are currently many studies focusing on power management from the consumption side. This article then proposes a new controller for managing the requested power by combining an optimization technique, which is the Ant Colony Optimization (ACO), with an adaptive PI regulator, which is the Result-Adaptive PID (RAPID) Control, as the controller of the operation of the battery. The battery represents an essential part in solar installations, enabling energy-efficient operation of the network by regulating the operation of controllable loads. To adjust the RAPID regulator using the ACO algorithm, we used an objective function to minimize the error between the input and output of the new regulator. Demonstrated on an isolated DC-AC network, the performance of the proposed controller for energy management on the consumption side is established after comparison with standard controllers such as the classic PI based trial & error methods and the RAPID tuned by ACO. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The nodes in a Wireless Ad hoc Network (WANET) intercommunicate via wireless links directly or by depending completely on other nodes as routers. Malicious packet dropping and link errors are the major reasons for packet losses in WANET. In some cases, a node does not work appropriately; subsequently, damages the packet transmission. Thus, in WANET, an active Packet Dropping Attack (PDA) detection technique has been proposed by utilizing Elliptic Curve Cryptography (ECC)-centric Ad-hoc On-Demands Vector (AODV)-Ant Colony Optimization (ACO) protocol (ECC-centric AODV-ACO protocol) to trounce the aforementioned complications. Primarily, to detect the PDA, by utilizing the ECC algorithm, security is ensured for every single node. Next, several possible solutions have been engendered by utilizing AODV. From these varied solutions, the finest shortest path is taken with the aid of the ACO algorithm. The attack, which drops the packet, is identified by utilizing the ECC-centric AODV-ACO protocol. Link error and the prevalence of malicious attacks are the sources of packet loss. To permit certain performance, the authentication service is presented in this protocol. Lastly, the proposed methodology's performance is analogized with the other prevailing models. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Previous research in millimeter-wave antenna technology has focused on designs for specific frequency bands, such as 28 GHz, 32 GHz, 60 GHz, and 78 GHz. This paper analyzes an asymmetric dual-step antipodal petunia-shaped antenna capable of operating across the entire mm-wave frequency range from 30 GHz to 300 GHz. In the initial phase, a K-Band antenna is designed using various single radiators, including shapes such as a ball, balloon, axle, and petunia, in conjunction with a rectangular ground structure. A single radiator petunia design is enhanced to become a dual antipodal petunia radiator to achieve higher operating frequencies. This antipodal design generates multiple resonances at adjacent frequencies, resulting in broadband characteristics. To further improve performance, modifications are made to the ground structure. A novel technique inspired by radiation contouring of the antenna is proposed, which enhances impedance matching and overall antenna performance. Subsequently, a dual antipodal petunia radiator with an asymmetric step ground is optimized using the particle swarm optimization method to cover the entire millimeter-wave band (30-300 GHz). The antenna's physical dimensions are 30x30 mm², with a peak gain of 6 dBi and radiation efficiency ranging from 89% to 92% across the entire mm-wave spectrum. Notably, this proposed antenna exhibits an exceptionally low level of side lobes and excellent cross-polar discrimination, showcasing its unique design. The potential applications of this antenna span various fields, including military and imaging systems, automotive radar, telecommunications, remote sensing, security screening, and energy harvesting technology. [ABSTRACT FROM AUTHOR]

Improving direct current microgrid (DC-MG) performance is achieved through the implementation in conjunction with a hybrid energy storage system (HESS).The microgrid's operation is optimized by fuzzy logic, which boosts stability and efficiency. By combining many storage technologies, the hybrid energy storage system offers dependable and adaptable energy solutions. The performance of DC microgrids is improved by this combination strategy. In this paper, we proposed the fuzzy logic controller (FLC) and ant colony optimization (ACO) in DC-MG to enhance the performance. The interfaces between the PV, PEMFC, and battery with DC-MG are demonstrated in this research. The approach integrates an achievable phasor alternative from modern power systems. As a result, we evaluate the performance of FLC, ACO, and HESS-DC-MG based on the three layers such as SLGF, DLGF, and TLGF. In SLGF, FLC in the HESS-MG achieved a 98% injection value, surpassing the 97% obtained with ACO. Without HESS-DC-MG, the voltage sag reduction was limited to 75%. In DLGF, FLC in HESS-MG yielded a 97% injection value, while ACO resulted in 95%. Without HESS-DC-MG, the voltage sag reduction was 70%. In TLGF, FLC achieved a 96% injection value, compared to 94% with ACO. The absence of HESS-DC-MG led to a voltage sag reduction of 69%. Overall, HESS-DC-MG technology proves effective in eliminating voltage sag across different scenarios. When comparing these three models, the FLC, ACO, and HESS-DC-MG exhibit superior performance in SLGFS. [ABSTRACT FROM AUTHOR]

Lung cancer is still ranked among the major killers and current techniques used in the diagnosis of this condition do not assist in early detection. In this paper, research seeks to work towards resolving the early detection of lung cancer using ACO and more specifically, the hybrid learning models. The ACO optimization algorithm based on foraging ants is used to select the features that contributes to model simplification and increased performance. The developed approach incorporates ACO with DNN in order to enhance the diagnostic performance of the system. The performance of the proposed approach has been evaluated and analyzed for different scenarios using experimental results, it has been shown that the accuracy of the DNN model is relatively increased and achieved up to 0. 91 to 0. 97 that indicates that application of ACO in breast cancer detection has the ability of minimizing false positives and thus improving the chances of early diagnosis. This line of work is a major advance towards more accurate diagnosis of lung cancer, and can be extrapolated to the field of more targeted health care. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

A wireless sensor network is a wireless communication network, and each sensor node has several sensors to collect environmental information. Wireless sensor network nodes have limited energy resources and need optimal routing protocols to reduce energy consumption. Failure to reduce energy consumption by sensor nodes reduces network life and efficiency. The main problem in routing is finding optimal paths for sending packets by reducing energy consumption in sensor nodes. This paper proposes an optimal routing method to reduce energy consumption in wireless sensor networks. In the first step, wireless sensor nodes are clustered with the Water strider algorithms (WSA), and cluster heads are selected for routing. In the second step, a mobile sink collects the packets from the cluster heads and sends them to the base station. The mobile sink uses the Ant colony optimization (ACO) algorithms to travel a shorter path between the cluster heads. The authors contribute to presenting a discrete version of the WSA algorithm for cluster head selection to reduce energy consumption. The authors contribute by providing a more comprehensive objective function for clustering network nodes considering error rate, energy consumption, PDR rate, and Euclidean distance. Cluster head traversal with a version of the ACO algorithm to reduce energy consumption and cluster head traversal coding like the TSP problem is the contribution of other authors. The paper aims to reduce energy consumption, reduce the error rate of sending packets and increase the lifetime of the wireless sensor network. Experiments are simulated on several simulated scenarios in Matlab. Criteria such as energy consumption, Packet delivery ratio (PDR), package loss rates, and the number of alive nodes to evaluate the proposed method are used. Experiments show that the proposed algorithm reduces the energy consumption and loss rates of packages of the wireless sensor network by optimally selecting cluster heads and increasing the PDR and number of alive nodes. Comparisons show In terms of energy consumption, Packet delivery ratio (PDR), Loss rates of packages, and the number of alive nodes, the proposed method is more efficient than Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), Information-centric wireless sensor networks, and Cluster based routing (CBR) routing methods. The PDR index in the proposed method is equal to 97.3% and is higher than PSO, GWO, and CS algorithms. The delay of the proposed method in routing is 25.97%, 5.78%, and 17.98% less than HHO, WOA, and GWO algorithms, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data collection using Mobile Sink (MS) is one of the best approaches to address the hot spot issue resulting from multihop data collection and extend the lifetime of Wireless Sensor Networks wherein the MS tours a few specific locations called sojourn locations that serve as data collecting points (DCPs). The best choice of these locations is an NP-hard problem, and the optimum or nearly optimum results can be achieved by applying meta-heuristic optimization methods. It is challenging to create an effective algorithm that allows MS for data collection irrespective of the network topology changes caused by node failures since these changes affect node coverage, data transmission, and network lifespan. Hence, an effort must be made to ensure a trade-off between the MS trajectory and the number of hops. Different MS-based techniques have been proposed; however, most of them fell short of addressing the above goals. With this inspiration, we propose iCapS-MS, which is an integrated approach that utilizes an improved Capuchin Search Algorithm (iCapSA) to determine the best set of DCPs and enhanced Ant Colony Optimization (e-ACO)-based MS trajectory design. Using iCapSA, the best DCPs are selected such that almost every node is served in one-hop communication with the shortest feasible hop distance and minimum coverage intersection between DCPs. The best trajectory for MS is established using e-ACO method. The results demonstrate that iCapS-MS outperforms existing methods based on several performance metrics. [ABSTRACT FROM AUTHOR]

The Weibull distribution is considered one of the important distributions used in reliability and in the distribution of survival times and in neutrosophic prediction. This paper contained an estimate of a two-parameter Weibull distribution using three estimation methods. One of these methods for estimating parameters is the traditional method, which represents the estimation of the greatest possibility, and the other two methods are estimation using the sine algorithm. and cosine (SCA) and the Ant Colony Algorithm (ACO). The simulation method was used to compare the methods, and it was found that the best method for estimating the parameters of the Weibull distribution is the sine and cosine algorithm (SCA) method. Then, real data was used, represented by the intensity of the earthquake in Japan (Richter) for the period July 1, 2023, to July 16, 2023, to estimate the rate of earthquake intensity in Japan, since Japan is one of the countries most exposed to earthquakes, and it was shown from The results are that the average magnitude of the earthquake in Japan in the period studied is 3.328593, which can be said. Weak buildings may be greatly damaged, but strong buildings are not greatly damaged. Also, a neutrosophic simulation of the same set of data will be suggested for future applications. [ABSTRACT FROM AUTHOR]

In this paper, a novel robust controller is presented for fractional order model of doubly-fed induction generator (DFIG) based on variable speed wind turbine. Due to the nonlinear dynamics of the system, external disturbances, and parameter uncertainties, robust controller must be designed. To achieve a high- performance system, the generator speed and currents are controlled by a novel fractional order backstepping sliding mode controller. The input control terms are derived based on fractional calculus to reduce chattering phenomenon and increase system robustness. Also, to achieve more precise description of the system, the fractional order model of the system is investigated; in addition, total disturbance of the system is estimated by novel fractional-order sliding mode disturbance observer (FOSM-DO). The controller parameters are obtained using Ant Colony Optimization (ACO) algorithm. The most important purpose is to extract wind turbine maximum power point. Furthermore, the suggested method performance is compared with the backstepping sliding mode control (BSMC). The simulations of the proposed strategy illustrate the efficiency of the controller as well as robustness against parameter uncertainties, external disturbances and changing of the operating point. [ABSTRACT FROM AUTHOR]

Interference minimization whether on topology or routing level has been a prominent concern in wireless sensor networks (WSNs). Existing graph-based approaches focus on minimizing whole topology and network interference. Work on having an interference minimum routing path on bidirectional connected graph models of WSNs is still left to explore under different interference models. So, this paper focuses on establishing an efficient routing path from the source to the destination node of minimum total link interference and maximum receiver node interference by optimally balancing between both the interference levels on any bidirectional connected graph model of WSN. We have proposed a dual-interference model based optimization (DIMO) routing algorithm by engaging ant colony optimization technique with two distinct interference models simultaneously that are link SUM interference model and receiver interference model. Moreover, a new interference model named as Disc-SUM model has also been developed whose implementation with Dijkstra's algorithm results in a dual-interference minimized routing path in quadratic time complexity. Simulation work has been carried out to validate the DIMO algorithm and the Disc-SUM model for implementing a routing path between the source to the destination node with optimally reduced total link interference, maximum receiver node interference, total transmission cost, and hop counts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The incentive for many developments and scientific progresses within the field of scheduling has emerged from industrial environments, and naturally, it could be utilized in expressing the scheduling concepts regarding terms used in the industry. Generally speaking, scheduling problems are known as limited optimization issues through which decisions related to the machines' assignment and works processing sequence are probed. Thus, following a review of the related literature, the major goal of this research is to design a mathematical model and to solve it through a meta-heuristic for no-wait flow shop scheduling problem using different machines for the purpose of minimizing the time required to complete the work using whale and ant colony optimization (ACO) algorithms in Sanat-Gostar-e-Hamgam Shoe Company. The ACO and whale algorithm methods are used to compare and predict scheduling activities in manufacturing line of shoe industry. The results showed an ACO algorithm with two stages in mean ideal distance (MID) end amounting to 76.65 and 77.38, respectively. Also, regarding the amounts of standard error mean squares, it could be claimed that the model designed using the improved whale algorithm has a better prediction, and the minimum time required to complete works using the whale algorithm is estimated to be equal to 86.1071. This could lead to an optimal state in achieving the predetermined goals. [ABSTRACT FROM AUTHOR]

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.

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.

Wind energy systems are pollution free and clean form of the renewable energy production. The dynamic model of a wind turbine system based on a doubly fed induction generator (DFIG) is exposed to external disturbances, uncertainties, and nonlinear dynamics. In this paper to ensure the system robustness against external disturbance and uncertainty in system parameters, a novel optimized fractional order robust adaptive sliding mode controller is proposed by utilizing a disturbance observer. The controller's main goal is to track the maximum power point of the wind turbine. In order to show the superiority of the proposed method, the results under normal conditions and in the presence of disturbance and uncertainty have been compared with the classical sliding mode control (SMC) and adaptive sliding mode control (ASMC). The parameters of all three controllers have been optimized by ant colony optimization (ACO) algorithm. The proposed method does not need the knowledge of the upper bounds of model uncertainty and disturbance. Also by using the fractional order operators in the control signal of the proposed method, its robustness against model uncertainty and disturbance is increased and it can extract the maximum power than the other compared methods. [ABSTRACT FROM AUTHOR]

This paper presents a skin cancer classification model that combines a pre-trained Convolutional Neural Network (CNN) with a nature-inspired feature optimization algorithm. A custom dataset comprising both malignant and benign skin cancer microscopic illustrations is derived from the ISIC dataset of dermoscopic images. Several preprocessing steps are performed on the input pictures, such as histogram equalization, gamma correction, and white balance adjustment, to improve visibility, quality, and make color corrections. Deep feature extraction and pattern recognition are conducted on both enhanced and original dataset images using the pre-trained CNN model EfficientNetB0. As a result of fusing these features, the model can capture rich details from both dataset versions at the same time. Ant Colony Optimization (ACO), a nature-inspired feature selection algorithm is applied to perform model optimization by keeping the most relevant features and discarding the unnecessary ones. The optimized feature vector is then used with various SVM classifier kernels for the skin cancer classification task. The maximum achieved accuracy of the proposed model exceeded 98% through CB-SVM while maintaining an excellent prediction speed and reduced training time. [ABSTRACT FROM AUTHOR]

Allowing variability in flight flying time in aircraft rerouting can achieve improved flight connection opportunities, and thus higher aircraft utilization and enhanced schedule flexibility. However, similar impacts on aircraft routing are underexplored. In this study, a new operational aircraft maintenance routing problem (OAMRP) model that incorporates cruise speed control to realize the flying time variability is formulated. A graph reduction procedure is developed to reduce the excessive model size. Then, the model is solved using a novel ant colony optimization algorithm that considers node attractions in the state transition rule. Computational experiments show that the proposed model, compared with the traditional model, can generate aircraft maintenance routes with higher aircraft utilization, and thereby save aircraft. Moreover, both schedule stability and flexibility are maintained in the proposed model. [ABSTRACT FROM AUTHOR]