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During the decommissioning process of nuclear facilities, workers are exposed to radiation and face the risk of exceeding safe dose limits. Ensuring the safety of personnel requires not only enhancing radiation protection measures but also optimizing work paths to minimize exposure time and avoid high-radiation areas. This paper proposes a nested optimization algorithm that combines an ant colony optimization (ACO) with an improved A* algorithm for the decommissioning of a nonradiation source. The algorithm aims to minimize the total radiation dose and transforms the original path optimization problem into an equivalent traveling salesperson problem. The improved A* algorithm is employed in the inner layer to calculate the path with the lowest radiation dose for any given sales order. The ACO operates in the outer layer to determine a set of optimal working paths that traverse all target points. The provided solution example demonstrates that the proposed path optimization algorithm effectively integrates the radiation field and obstacles. It successfully identifies a sequence for dismantling with the lowest dose and corresponding optimal work path while ensuring the completion of the dismantling task. These findings are expected to offer valuable insights for optimizing personnel work paths during the subsequent decommissioning process of nuclear facilities. [ABSTRACT FROM AUTHOR]

Fog computing has grown in popularity in recent years because to its potential to deliver real-time processing, low latency, and reduce network congestion. However, the implementation of Internet of Things (IoT) enabled smart devices in environments using fog computing may lead to resource limitations and higher computational demands. Load balancing and fault tolerance strategies are necessary to tackle these difficulties for optimal resource usage and system availability. In order to accomplish fault tolerance aware load balancing in fog computing, a hybrid meta-heuristic approach that combines the Modified Harris-Hawks Optimization (MHHO) and Ant Colony Optimization (ACO) is proposed through this paper. The MHHO algorithm is utilized for load balancing, whereas the ACO algorithm is used for fault tolerance. By employing the proposed technique, the load on fog nodes is balanced, the makespan time is minimized, energy consumption and execution costs are minimized, and fault tolerance in fog computing environments is ensured. It is evaluated using a simulation framework built on the iFogSim toolkit. In terms of load balancing, fault tolerance, and other factors, the results of the experiments show that the suggested hybrid algorithm performs better than earlier state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper aims to solve the Chinese Postman Problem (CPP) using an Ant Colony Optimization (ACO) algorithm. In graph theory, the CPP looks for the shortest closed path that visits every edge of a connected undirected graph. This problem has many applications, including route optimization, interactive system analysis, and flow design. Although numerous algorithms aimed at solving CPP are present in the literature, very few meta-heuristic algorithms are proposed, and no ACO applications have been proposed to solve them. This paper tries to fill this gap by presenting an ACO algorithm that solves CPP (ACO-CPP). To prove its consistency and effectiveness, ACO-CPP is compared with a Genetic Algorithm (GA) and a recursive algorithm throughout three experiments: (1) recursive-ACO-GA comparisons over randomly generated graphs for the attainment of the global optimum; (2) ACO-GA statistical comparisons over specifically generated graphs; (3) recursive-ACO-GA comparisons by changing ACO hyperparameters over randomly generated graphs for the attainment of the global optimum. The experiments prove that the ACO-CPP algorithm is efficient and exhibits a consistency similar to GA when the number of possible solutions to explore is relatively low. However, when that number greatly exceeds those explored, ACO outperforms GA. This suggests that ACO is more suitable for solving problems with a CPP structure. [ABSTRACT FROM AUTHOR]

The fixed fleet heterogeneous open vehicle routing problem (HFFOVRP) is one of the most practical versions of the vehicle routing problem (VRP) defined because the use of rental vehicles reduces the cost of purchasing and routing for shipping companies nowadays. Also, applying a heterogeneous fleet is recommended due to the physical limitations of the streets and efforts to reduce the running costs of these companies. In this paper, a mixed-integer linear programming is proposed for HFFOVRP. Because this problem, like VRP, is related to NP-hard issues, it is not possible to use exact methods to solve real-world problems. Therefore, in this paper, a hybrid algorithm based on the ant colony algorithm called MACO is presented. This algorithm uses only global updating pheromones for a more efficient search of feasible space and considers a minimum value for pheromones on the edges. Also, pheromones of some best solutions obtained so far are updated, based on the quality of the solutions at each iteration, and three local search algorithms are used for the intensification mechanism. This method was tested on several standard instances, and the results were compared with other algorithms. The computational results show that the proposed algorithm performs better than these methods in cost and CPU time. Besides, not only has the algorithm been able to improve the quality of the best-known solutions in nine cases but also the high-quality solutions are obtained for other instances. [ABSTRACT FROM AUTHOR]

Ant colony optimization is an intelligent evolutionary algorithm that has been widely used in the field of path planning, and the research on its improved methods is proliferating. In this paper, the improvement methods of single and hybrid ant colony optimization in path planning are reviewed in terms of initial pheromone concentration, heuristic function, state transition rule, pheromone update rule, and parameter optimization, respectively. The future research directions of ant colony optimization have been prospected in terms of theoretical aspects, parameter setting and optimization, integration with other algorithms, and multiobjectives. [ABSTRACT FROM AUTHOR]

This paper proposed an ant colony optimization algorithm based on Lévy flight transition rule for the TSP. The algorithm combined the transition rule based on Lévy flight and ACS, and formed a dynamic weight hybrid transition rule, which could effectively help the algorithm escape from local optima and enhance the global search ability. In addition, the random multi-path optimization 3-opt strategy increased the diversity of the algorithm by randomly extracting some paths and combining them with the current optimal path. When the algorithm stagnated, it used the pheromone average random reset strategy to reset the pheromone concentration on the path, which helped the algorithm escape from local optimal. The experimental results show that the proposed algorithm keeps the error within 3% compared with the optimal solution when dealing with multiple TSP instances of different scales, proving that it has excellent convergence and ability to avoid falling into local optima in the TSP problem. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [ABSTRACT FROM AUTHOR]

Since cloud computing has an abundance of users, the system has to execute a wide range of tasks. Task scheduler methods that are both robust and efficient while delivering the best outcomes are required. The task volume and runtime in the cloud vary rapidly, making task assessment and resource mapping difficult. Security issues, communication delays, and data loss are substantial barriers to scheduling. Furthermore, optimization techniques can be utilized to reduce load and assign tasks so that the user can finish tasks faster. This paper offers a hybrid job scheduling technique for cloud computing using adaptive particle swarm optimization and ant colony optimization particle swarm optimization-ant colony optimization (adaptive PSO-ACO). After rapidly finding the initial solution via particle swarm optimization, the ant colony optimization approach establishes its first pheromone distribution. The suggested hybrid algorithm is compared to standalone PSO and ACO algorithms. Compared to ACO, the percentage decrease is 7.9%. Hybrid method has the lowest total cost, 55% less compared to PSO. Tasks vary when virtual machines (VMs) are constant and VMs vary when tasks are constant. Parameters like final cost, makespan, fitness value, computation time and weighted time are assessed to evaluate the performance of the hybrid task scheduling algorithm. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a new revolution of the Internet, which can be thought of as an uprising expansion of internet services. IoT is defined in many different ways. It covers many areas of life, including houses, cities, automobiles, and roads. It also includes devices that track people's behavior and utilize the information gathered for push services. Any object may access the web over a wired or wireless network with the IoT. In addition to authentication and security, extensive research and development have gone into energy awareness. Mobile Internet of Things (MIoT) is the following stage in this situation. Mobile data collecting, data analysis, energy management, security and privacy, and the provision of IoT services are some issues arising when using MIoT. So, this paper proposed an energy-aware technique for resource allocation in MIoT. Due to this problem's NP-hard nature, a new approach is suggested to reduce the network's energy consumption and end-to-end delay in MIoT using selfish node ranking and ant colony optimization algorithms. The proposed method is compared to Whale Optimization Algorithm (WOA) and Task Priority-based Resource Allocation (TPRA) algorithms. The findings show the network's substantial changes in energy consumption and end-to-end delay using the proposed method. The findings of the current work are significant for academics and offer insights into upcoming study areas in this subject. [ABSTRACT FROM AUTHOR]

The development of information technology, wireless communications have become prevalent in every other field we can imagine. Sensor nodes are the fundamental element of wireless communication networks. When they are deployed, they gather environmental data from the surrounding area and send it to the base station for further analysis. This paper focused on creating a routing technique that improved wireless communication energy conservation through the use of fuzzy logic and ant colony optimisation in order to extend battery life. Ant colony optimisation algorithm (HFACO) and hybrid fuzzy logic were used in the construction of the routing protocol. Numerous derived techniques have been adapted to dynamic problems in real variables, stochastic problems, multi-targets, and parallel implementations. Ant colony optimisation algorithms have been applied to a wide range of combinatorial optimisation problems, from quadratic assignment to protein folding or routing vehicles. Several techniques that are based on how ant colonies forage have been used recently to solve challenging discrete optimisation problems.By taking into account the node's energy and traffic load, fuzzy logic was utilised to determine the total node cost to the gateway. Ant colony optimisation (ACO) was used to find the shortest path between the source and destination sensor nodes, and the path's value was determined by measuring its shortest distance. When compared to the ACO's performance in the same conditions, the Matlab simulation's results showed superior energy conservation performance. This improved routing technique can be applied to Wi-Fi sensor networks in industrial environments. [ABSTRACT FROM AUTHOR]

This paper analyzes some details of artificial intelligence algorithms to paper-reviewer assignment problem. In particular, the study on most common algorithms like genetic algorithm (GA), ant colony optimization (ACO) and tabu-search (TS) is made and the performance of these algorithms in paper-reviewer assignment problem is tested. Moreover, two hybrid methods that efficiently combine the above-mentioned algorithms are proposed: the ACO-GA and GA-TS algorithms. To measure the performance of these algorithms, extensive computational experiments are conducted. Evaluation using different data sets shows that the proposed algorithms are effective and achieve good results. [ABSTRACT FROM AUTHOR]

Because logistics companies usually have multiple depots to serve their many dispersed customers, multi-depot vehicle routing problem (MDVRP) has gained significant research attention. To solve an MDVRP model, this paper develops a hybrid ant colony optimization based on a polygonal circumcenter (BPC-HACO). Furthermore, because ACO has been found to fall easily into the local optimum, simulated annealing and three local optimization operations are introduced to encourage the ACO to improve the algorithm's optimization ability. Finally, MDVRP benchmarks and data sets of other papers are employed to verify the effectiveness of the BPC-HACO in solving MDVRP (In 23 instances, BPC-HACO finds 14 BSKs, and 3 results are better than the BSKs), MDVRP with distance constraints (Compared to other papers, the route length is reduced by an average of 17.94%) and dynamic MDVRP (In 10 instances, BPC-HACO finds 3 BSKs, and 2 results are better than the BSKs). Finally, fitness landscape analysis has been applied to analyze the structural features of MDVRP to choose the most appropriate algorithm for MDVRP. [ABSTRACT FROM AUTHOR]

In order to guarantee the authenticity of QoS data in the process of service composition optimization, this paper proposed a trusted framework based on the Hyperledger Fabric. Meanwhile, this paper proposed a service composition optimization method of differential evolution algorithm with ant colony factor to improve the efficiency of service composition optimization. Firstly, this paper deployed corresponding nodes on the Hyperledger Fabric to build a trusted framework that guaranteed the authenticity of the candidate services. Then, in the form of a smart contract on the blockchain, the proposed algorithm solved the optimization problem for the combination of services, enabling the composition process could be execute in a trusted environment. The algorithm helped the algorithm dynamically control the search space and record the key information in the iteration process by introducing various ACO factors, such as the ACO path factor, the optimal ACO factor, the pheromone factor and the difference calculation based on the ACO factors, in order to improve the optimization capability of the algorithm. Finally, the simulation experiments demonstrate that the trustworthy framework can effectively guarantee the trustworthiness of data; ACOF-DE has better optimization efficiency compared with other intelligent optimization algorithms. [ABSTRACT FROM AUTHOR]

The paper develops a statistical model for optimizing the Hydrogen-injected Natural gas (H-NG) high-pressure pipeline network. Gas hydrodynamic principles are utilized to construct the pipeline and compressor station model. The model developed is implemented on a pipeline grid that is supposed to carry Hydrogen as an energy carrier in a natural gas-carrying pipeline. The paper aims to optimize different objectives using ant colony optimization (ACO). The first objective includes a single objective optimization problem that evaluates the maximum permissible hydrogen amounts blended with natural gas (NG) for a set of pipeline constraints. We also evaluated the variations in operational variables on injecting Hydrogen into the natural gas pipeline networks at varying fractions. The study further develops a multi-objective optimization model that includes bi-objective and tri-objective problems and is optimized using ACO. Traditional studies have focused on single-objective optimization with minimal bi-objective issues. In addition, none of the earlier research has shown the effect of introducing Hydrogen to the NG network using tri-objective function evaluations. The bi-objective and tri-objective functions help evaluate the effect of injecting Hydrogen on different operational parameters. The study further attempts to fill the gap by detailing the modelling equations implemented through a bi-objective and tri-objective function for the H-NG pipeline network and optimized through ACO. Pareto fronts that show the tradeoff between the different objectives for the multi-objective problem have been generated. The primary objective of the bi-objective and tri-objective optimization problems is maximizing hydrogen mole percent in natural gas. The other objective chosen is minimizing compressor fuel consumption and maximizing delivery pressure, throughput, and power delivered at the delivery station. The findings will serve as a roadmap for pipeline operators interested in repurposing natural gas pipeline networks to transport hydrogen and natural gas blend (H-NG) and seeking to reduce carbon intensity per unit of energy-delivered fuel. [Display omitted] • Effect of adding hydrogen to a natural gas carrying pipeline grid is examined. • Multi-objective ant colony optimization is used. • Pareto fronts for bi-objective and tri-objective functions are obtained. [ABSTRACT FROM AUTHOR]

Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. However, path planning methods are prone to inefficiently generate unsmooth paths in narrow and large-size scenes, especially considering the chassis model complexity of CLMRs with suspension. To this end, instead of traditional path planning based on an integer order model, this paper proposes fractional-order enhanced path planning using an improved Ant Colony Optimization (ACO) for CLMRs with suspension, which can obtain smooth and efficient paths in narrow and large-size scenes. On one hand, to improve the accuracy of the kinematic model construction of CLMRs with suspension, an accurate fractional-order-based kinematic modelling method is proposed, which considers the dynamic adjustment of the angle constraints. On the other hand, an improved ACO-based path planning method using fractional-order models is introduced by adopting a global multifactorial heuristic function with dynamic angle constraints, adaptive pheromone adjustment, and fractional-order state-transfer models, which avoids easily falling into a local optimum and unsmooth problem in a narrow space while increasing the search speed and success rate in large-scale scenes. Finally, the proposed method's effectiveness is validated in both large-scale and narrow scenes, confirming its capability to handle various challenging scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of a colony of memory-enhanced ants (ME-ANTS) which, starting from a given position, must collaboratively discover the optimal path to the exit point within a grid network. The colony can be divided into groups of different sizes and its objectives are maximizing the number of ants that exit the grid while minimizing path costs. Three distinct analyses were conducted: an overall analysis assessing colony performance across different-sized groups, a group analysis examining the performance of each partitioned group, and a pheromone distribution analysis discerning correlations between temporal pheromone distribution and ant navigation. From the results, a dynamic correlation emerged between the degree of colony partitioning and solution quality within the ACO algorithm framework. [ABSTRACT FROM AUTHOR]

This paper focuses on addressing the "last 100 metres" home delivery in rural areas, using a cooperated delivery method of drones and truck. Considering the constraints of drone load, drone energy consumption and customer time window, a mixed integer linear programming model is established to minimize the delivery cost. Owing to the computational complexity of this problem, a double ant colony optimization with neighbourhood search is proposed. First, the raw data are sorted and encoded. Second, the ant colony optimization with search operators is used to solve drone routes and truck route. Finally, the local search algorithm with search operators is used to solve the connection point between the drones and truck to obtain the cooperated delivery routes. Extensive experiments are conducted on the instances randomly generated in the Solomon dataset, and results demonstrate the proposed algorithm effectively solves problems within reasonable runtimes. Sensitivity analysis is conducted on factors that may affect the delivery cost of the solution and provide insights about drones participating in the "last hundred metres" home delivery service. [ABSTRACT FROM AUTHOR]

This paper investigates a single-machine scheduling problem of a software test with shared common setup operations. Each job has a corresponding set of setup operations, and the job cannot be executed unless its setups are completed. If two jobs have the same supporting setups, the common setups are performed only once. No preemption of any processing is allowed. This problem is known to be computationally intractable. In this study, we propose sequence-based and position-based integer programming models and a branch-and-bound algorithm for finding optimal solutions. We also propose an ant colony optimization algorithm for finding approximate solutions, which will be used as the initial upper bound of the branch-and-bound algorithm. The computational experiments are designed and conducted to numerically appraise all of the proposed methods. [ABSTRACT FROM AUTHOR]

Ant Colony Optimization (ACO) is an algorithm based on Meta-heuristics and swarm intelligence, taking inspiration from the exploring behavior of social insects like ants. Some very challenging optimization problems, like the vehicle routing problem, have been solved by Ant colony optimization. But, the Standard ACO algorithm has problems like trapping in local optimum. This paper discusses an improved version of the ACO algorithm, known as Modified ACO (M-ACO). The main motivation of the proposed algorithm is to use pheromone mutation techniques, such as swap and random, in dynamic environments, to overcome the problems of standard ACO and solve the Travelling Salesman Problem, which is a well-known NP-hard problem. Mutation allows better searching and exploring in other areas, for better results. Also, the exploitation is increased that uses local information to search local regions more intensively for finding minimum distance and increasing the rate of convergence. Evaporation-based Ant colony optimization (E-ACO) framework and population-based ant colony optimization (P-ACO) framework are the two groups into which the algorithms under study are separated. Both frameworks are evaluated on dynamic environments produced using TSPLIB base instances and updated for pheromone values using mutation technique. Experiments and Statistical tests were conducted on the TSPLIB datasets such as pr439 and lin318, in dynamic environments and on comparing ACO with M-ACO and P-ACO, better results were obtained for MACO and P-ACO. Metrics like Poffline have been considered to measure algorithm performance and behavior. [ABSTRACT FROM AUTHOR]

In the field of data exchange, the security of information is the critical aspect. This void in the security helped the evolution of steganography to address the problem of security, especially when highly critical information exchange is the priority. Better and advanced level of security has been attained using the techniques of steganography with cryptography. However, it has been observed that steganographic techniques principled on computationally intelligent or inspired by nature algorithms are gaining much attention. The paper proposes a method for image steganography in transform domain using hybridization of two computationally intelligent algorithms, i.e., firefly optimization algorithm and ant colony optimization algorithm with Huffman encoding to address the gap of high payload. Comparative analysis of the proposed steganographic tool with other existing methods and analysis of the values obtained against some key performance terms like PSNR (peak signal to noise ratio), MSE (mean square error), BER (bit error rate) and SSI (structural similarity index) determines the proposed algorithm as a powerful method. The proposed tool achieves the PSNR of 68.06 dB and the embedding capacity of 985,682 bits. [ABSTRACT FROM AUTHOR]

In order to improve the optimization effect of traditional ant colony algorithm in solving traveling salesman problem, this paper developed a dual-ant colony optimization algorithm with dynamic differentiation and neighborhood induction mechanism. Firstly, the algorithm introduced chaotic random strategy, and changed the original greedy strategy in the initialization stage of the algorithm to chaotic distribute the initial pheromone, so as to maintain the diversity of the population and improve the accuracy of the solution. Secondly, the algorithm divided the isolated ants in the ant colony into isolated ant colony and normal ant colony, and the two groups of ants searched around the current optimal path and the outlier path respectively. It adopted the induction mechanism among the populations. Normal ants were responsible for searching the optimal path, and isolated ants released pheromones randomly in chaos to induce the normal ant colony to a new path, thus effectively balancing the contradiction between the convergence rate and the diversity of solutions. The simulation results of different scale TSP show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of multi-robot task scheduling in Antarctic environments. There are various algorithms for multi-robot task scheduling, but there is a risk in robot operation when applied in Antarctic environments. This paper proposes a practical multi-robot scheduling method using ant colony optimization in Antarctic environments. The proposed method was tested in both simulated and real Antarctic environments, and it was analyzed and compared with other existing algorithms. The improved performance of the proposed method was verified by finding more efficiently scheduled multiple paths with lower costs than the other algorithms. [ABSTRACT FROM AUTHOR]

The purpose of geographic location selection is to make the best use of space. Geographic location selection contains a large amount of spatiotemporal data and constraints, resulting in too many solutions. Therefore, this paper adopts the ant colony algorithm in the meta-heuristic search method combined with the incomplete quadtree to improve the searchability of the space. This paper proposes an improved ant colony algorithm in nonuniform space to solve the P-center facility location problem. The geographic space is divided by the incomplete quadtree, and the ant colony path is constructed on the level of the quadtree division. Ant colonies can leave pheromones on multiple search paths, and optimized quadtree encoding in nonuniform space stores pheromone matrices and distance matrices. The algorithm proposed in this paper improves the pheromone diffusion algorithm and the optimization objective at the same time to update the pheromone in the nonuniform space and obtain the ideal solution. The results show that the algorithm has excellent performance in solving the location problem with good convergence accuracy and calculation time. [ABSTRACT FROM AUTHOR]

This paper considers the planning of logistics missions of hybrid transport systems, which include a car or other vehicle that can move from a base to other locations along a designated route, carrying one unmanned aerial vehicle (UAV). A meaningful formulation and mathematical models of optimization problems of distributing objects to bases, selecting bases, and generating UAV routes during the inspection of maintenance of a given set of objects in the presence of flight resource constraints are proposed. We have developed an algorithm based on ant colony optimization to solve the resulting combinatorial optimization problems. We present the results of a computational experiment. [ABSTRACT FROM AUTHOR]

To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm. [ABSTRACT FROM AUTHOR]

Feature selection is an essential task in the field of machine learning, data mining, and pattern recognition, primarily, when we deal with a large number of features. Feature selection assists in enhancing prediction accuracy, reducing computation time, and creating more comprehensible models. In feature selection, each feature has two possibilities, either it would be taken for computation or not, which implies for n number of features, there are 2 n possible feature subsets. So, identifying a relevant feature subset in a reasonable amount of time is an NP-hard problem, but by using an approximation algorithm, a near-optimal solution can be achieved. However, many of the feature selection algorithms use a sequential search strategy to select relevant features, which adds or removes features from the dataset sequentially and leads to trapped into a local optimum solution. In this paper, we propose a novel clustering-based hybrid feature selection approach using ant colony optimization that selects features randomly and measures the qualities of features by K-means clustering in terms of silhouette index and Laplacian score. The proposed feature selection approach allows random selection of features, which allows a better exploration of feature space and thus avoids the problem of being trapped in a local optimal solution, and generates a global optimal solution. The same is verified when compared with another state-of-the-art method. [ABSTRACT FROM AUTHOR]

Stochastic networks are one of the most prevalent types of networks these days. Therefore, many researchers directed to study them and summarize the essential points and challenges they face in developing these types of networks, especially optimal route path selection. In this paper, a solution to this problem was addressed using the evolutionary algorithm ACO (Ant Colony Optimization), where the path with the lowest cost was obtained according to several scenarios studied in the research, which consider the fact that, the traffic information in the network is available either in a static or in a dynamic form in real-time. The proposed method presented contributions for real networks that can be used in many applications. The results are essential in solving the problem of choosing the optimal route. Also, they can be applied to various scenarios of the stochastic networks that exist in real life. Optimization improves logistics efficiency, which contributes to sustainability by minimizing fuel consumption, reducing emissions, and conserving resources. [ABSTRACT FROM AUTHOR]

Recently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown environment and restricted by some obstacles is one of the most problems facing the operation of drones. The problem of path planning is not only limited to searching for an appropriate path from the starting point to the destination but also related to how to choose an ideal path among all available paths and provide a mechanism for collision avoidance. By considering how to construct the best path, several related issues need to be taken into account, that relate to safety, obstacle avoidance, response speed to overtake obstacles, etc. Swarm optimization algorithms have been used to provide intelligent modeling for drone path planning and enable to build the best path for each drone. This is done according to the planning and coordination dimensions among the swarm members. In this paper, we have discussed the features and characteristics of different swarm optimization algorithms such as ant colony optimization (ACO), fruit fly optimization algorithm (FOA), artificial bee colony (ABC), and particle swarm optimization (PSO). In addition, the paper provides a comprehensive summary related to the most important studies on drone path planning algorithms. We focused on analyzing the impact of the swarm algorithm and its performance in drone path planning. For that, the paper presented one of the most used algorithms and its models employed to improve the trajectory of drones that rely on swarm intelligence and its impact on the optimal path cost of drones. The results of performance analysis for the ACO algorithm in a 3D and 2D-dimensional environment are illustrated and discussed, and then the performance evaluation of the ACO is compared to the enhanced ACO algorithm. The proposed algorithm achieves fast convergence, accelerating the process of path planning. [ABSTRACT FROM AUTHOR]

This paper presents an approach to solve the variant of the well-known Travelling Salesman Problem (TSP) by using a gamesourcing approach. In contemporary literature is TSP solved by wide spectra of modern as well as classical computational methods. We would like to point out the possibility to solve such problems by computer game plying that is called a gamesourcing. Gamesourcing can be understood as a version of game-driven crowdsourcing. The main part and contribution of this paper is a demonstration of gamesourcing use in the game called Labyrinth that reflects TSP structure. The game has a form of a maze-labyrinth that enables players to move through it like the Ant Colony Optimization. The playing of the Labyrinth, thus, by playing, solves the problem. The performance of the "human ant-like system" is then evaluated and compared against some well-known versions of ACO. As we believe, our experiments suggest that this approach can serve as an alternative way that employs gamesourcing to assist a combinatorial optimizer in achieving better results on a well-known NP-hard optimization problem. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning. [ABSTRACT FROM AUTHOR]

Summary: Recently, cloud computing has been recognized as an effective paradigm for offering an on‐demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non‐deterministic Polynomial hard (NP‐hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta‐heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. [ABSTRACT FROM AUTHOR]

Heuristic algorithms are algorithms that can provide near-optimum solutions for very large-scale optimization problems in an acceptable time. They are commonly used methods in terms of being at an acceptable level and reaching a solution easily and quickly, although it cannot be guaranteed that the best solution will be found. Problem solving approaches being used in heuristic methods; decision making, optimization, fuzzy logic, artificial intelligence, machine learning, deep learning. In this paper; chosen method in order to find the best solution of route optimization for the Travelling Salesman Problem (TSP), which has applications in many areas, is that Ant Colony Optimization (ACO). For classification of the data, that is randomly selected, Fuzzy C-Mean Clustering (FMC) Algorithm has being used. The data set has being separated into 3, 4 and 5 clusters and all cluster data sets obtained were evaluated with Multiple ACO and then the results were compared. It was determined that the length of the route obtained by clustering 3 was 4.61% shorter than the cluster of 4 and 2.37% shorter than the cluster of 5. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) assists an extensive range of sensor nodes and enables several real-time uses. Congestion on the WSN is based on high pocket traffic and low wireless communication capabilities under network topology. Highly loaded nodes will consume power quickly and increase the risk of the network going offline or breaking. Additionally, loss of packet and buffer overflows would result in an outcome of increased end-to-end delay, performance deterioration of heavily loaded nodes, and transport communication loss. In this paper, a novel congestion control system is proposed to diminish the congestion on network and to enhance the throughput of the network. Initially, cluster head (CH) selection is achieved by exhausting K-means clustering algorithm. After the selection of cluster head, an efficient approach for congestion management is designed to select adaptive path by using Adaptive packet rate reduction (APTR) algorithm. Finally, Ant colony optimization (ACO) is utilized for enhancement of wireless sensor network throughput. The objective function increases the wireless sensor network throughput by decreasing the congestion on network. The proposed system is simulated with (Network Simulator NS-2). The proposed K-means C-ACO-ICC-WSN attains higher throughput 99.56%, 95.62% and 93.33%, lower delay 4.16%, 2.12% and 3.11% and minimum congestion level 1.19%, 2.33% and 5.16% and the proposed method is likened with the existing systems as Fuzzy-enabled congestion control through cross layer protocol exploiting OABC on WSN (FC-OABC-CC-WSN), Optimized fuzzy clustering at wireless sensor networks with improved squirrel search algorithm (FLC-ISSA-CC-WSN) and novel energy-aware clustering process through lion pride optimizer (LPO) and fuzzy logic on wireless sensor networks (EAC-LPO-CC-WSN), respectively. Finally, the simulation consequences demonstrate that proposed system may be capable of minimizing that congestion level and improving the throughput of the network. [ABSTRACT FROM AUTHOR]

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource competition among these VMs, which results in VM performance reduction. Many VM migration (VMM) methods neglect VM performance reduction. Although some performance-aware VM allocation methods were proposed, the performance optimization objective of these methods mainly aimed at guaranteeing service level agreement or reducing VM downtime during migration, and they did not scientifically analyze how the VM performance degrades. Therefore, how to minimize the VM performance reduction for users when migrating VMs remains a major challenge. This paper proposes a VM Performance-Aware VMM method (PAVMM) for both users and cloud service providers. To maximize VM performance for users, it utilizes the VM performance model, which was built in our previous works, to predict the VM performance after migrating VMs. It then establishes an optimization objective of maximizing VM performance for users. Meanwhile, minimizing the number of active PMs and the total migration cost are regarded as additional optimization objectives for cloud service providers. Therefore, we formulate VMM as a multi-objective optimization problem, which tries to maximize VM performance for users and minimize the number of active PMs and the total migration cost for cloud service providers simultaneously. Then an ant colony optimization (ACO)-based algorithm is proposed to solve the NP-hard VMM problem. Lastly, the experiments are conducted to evaluate PAVMM, and the results verify its efficiency. • VM Performance-Aware VMM method of improving users' experience and cloud service providers' benefits. • Optimizations include: maximizing VM performance, and minimizing the number of active PMs and the total migration cost. • An efficient VMM algorithm based on ant colony optimization. • Experimental results using both CloudSim platform and OpenStack-based cloud platform. [ABSTRACT FROM AUTHOR]

This paper aims to formulate an enhanced ant colony optimization algorithm that helps find a suitable data route that improves energy efficiency and reduces the time consumption for the delivery of packets. Energy equations have been framed to analyze energy used at the transmitting, receiving, and relay nodes. The network is segmented into smaller virtual segments for easier analysis of the proposed algorithm. Each segment is assumed to have nodes with a cluster head. The sink gathers information from different cluster heads as it moves from one segment to another. The nodes not in close connection with the network used the overhearing mechanism to share the information with the sink. The simulation has been done using Cisco Packet Tracer software. The proposed algorithm has been applied to different types of wireless networks to determine their efficiency. The wireless networks considered for this purpose are Bluetooth, Wi-Max, and Wi-Fi. Packet delivery ratio, end-to-end delay, collision, and lifetime are evaluated for the different types of wireless networks. The obtained results are analyzed, and graphs are plotted. [ABSTRACT FROM AUTHOR]

In this paper, the flexural vibration of uniform and non-uniform rotating shafts based on the Timoshenko beam theory is investigated. Considering shear deformation and gyroscopic effects to build an exact framework of a solution, the fourthorder differential equation of vibration is solved by an analytical method. The overall transfer matrix of the system formed by a series of flexible distributed in addition to lumped elements is achieved. The results obtained by the distributed lumped modeling technique (DLMT) are verified with other techniques. The damped frequencies obtained by the hybrid modeling method for a multistep gas turbine rotor system using the Euler-Bernoulli and Timoshenko beam theories are compared with the results of the transfer matrix method (TMM). It is shown that the presented method provides highly accurate results, while with no compromise, it can be simply and effectively employed for complex systems. It is also shown how the Hooke and Jeeves and the ant colony optimization (ACO) besides the direct enumeration method can be applied to obtain the damped natural frequencies of complicated vibrational systems such as the gas turbine rotors. The comparison between the frequency and damping ratio values obtained by the optimization and direct enumeration methods shows less than 1% error for different cases. [ABSTRACT FROM AUTHOR]

Order picking is a crucial operation in the storage industry, with a significant impact on storage efficiency and cost. Responding quickly to customer demands and shortening picking time is crucial given the random nature of order arrival times and quantities. This paper presents a study on the order-picking process in a distribution center, employing a "parts-to-picker" system, based on dynamic order batching and task optimization. Firstly, dynamic arriving orders with uncertain information are transformed into static picking orders with known information. A new method of the hybrid time window is proposed by combining fixed and variable time windows, and an order consolidation batch strategy is established with the aim of minimizing the number of target shelves for picking. A heuristic algorithm is designed to select a shelf selection model, taking into account the constraint condition that the goods on the shelf can meet the demand of the selection list. Subsequently, task division of multi-AGV is carried out on the shelf to be picked, and the matching between the target shelf and the AGVs, as well as the order of the AGVs to complete the task of picking, is determined. A scheduling strategy model is constructed to consider the task completion time as the incorporation of moving time, queuing time, and picking time, with the shortest task completion time as the objective function and AGV task selection as the decision variable. The improved ant colony algorithm is employed to solve the problem. The average response time of the order batching algorithm based on a hybrid time window is 4.87 s, showing an improvement of 22.20% and 40.2% compared to fixed and variable time windows, respectively. The convergence efficiency of the improved ant colony algorithm in AGV task allocation is improved four-fold, with a better convergence effect. By pre-selecting the nearest picking station for the AGVs, the multi-AGV picking system can increase the queuing time. Therefore, optimizing the static picking station selection and dynamically selecting the picking station queue based on the queuing situation are proposed. The Flexsim simulation results show that the queue-waiting and picking completion times are reduced to 34% of the original, thus improving the flexibility of the queuing process and enhancing picking efficiency. [ABSTRACT FROM AUTHOR]

The traveling thief problem (TTP) is a typical combinatorial optimization problem that integrates the computational complexity of the traveling salesman problem (TSP) and the knapsack problem (KP). The interdependent and mutually restrictive relationship between these two sub-problems brings new challenges to the heuristic optimization algorithm for solving the TTP problem. This paper first analyzes the performance of three sub-component combined iterative algorithms: Memetic Algorithm with the Two-stage Local Search (MATLS), S5, and CS2SA algorithms, which all employ the Chained Lin-ighan (CLK) algorithm to generate the circumnavigation path. To investigate the influence of different traveling routes on the performance of TTP solving algorithms, we propose a combinatorial iterative TTP solving algorithm based on the Ant Colony Optimization (ACO) and MAX-MIN Ant System (MMAS). Finally, the experimental investigations suggest that the traveling route generation method dramatically impacts the performance of TTP solving algorithms. The sub-component combined iterative algorithms based on the MMAS algorithm to generate the circumnavigation path has the best practical effect. [ABSTRACT FROM AUTHOR]

In this paper, a smart home energy management system is proposed to improve the efficiency of the electricity infrastructure of residential buildings. To solve the scheduling problem of a smart building, we propose bacterial foraging ant colony optimization (HB-ACO). The primary objective of scheduling is to shift load from on-peak hours to off-peak hours to reduce electricity cost and peak-to-average ratio. A comparison of these algorithms is also presented in terms of performance parameters, electricity cost, reduction of PAR, and user comfort in terms of waiting time. The proposed techniques are evaluated using two pricing schemes: (1) time of use and (2) critical peak pricing. Moreover, coordination among home appliances is presented for real-time scheduling. We represent this as a knapsack problem and solve it through ant colony optimization algorithm. The HB-ACO shows better performance than ACO and BFA in reducing electricity cost, PAR, and increased user comfort, which is evident from the simulation results. [ABSTRACT FROM AUTHOR]

A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did. [ABSTRACT FROM AUTHOR]

Planning tasks are important in construction, manufacturing, logistics, and education. At the same time, scheduling problems belong to the class of NP-hard optimization problems. Ant colony algorithm optimization is one of the most common swarm intelligence algorithms and is a leader in solving complex optimization problems in graphs. This paper discusses the solution to the job-shop scheduling problem using the ant colony optimization algorithm. An original way of representing the scheduling problem in the form of a graph, which increases the flexibility of the approach and allows for taking into account additional restrictions in the scheduling problems, is proposed. A dynamic evolutionary adaptation of the algorithm to the conditions of the problem is proposed based on the genetic algorithm. In addition, some heuristic techniques that make it possible to increase the performance of the software implementation of this evolutionary ant colony algorithm are presented. One of these techniques is parallelization; therefore, a study of the algorithm's parallelization effectiveness was made. The obtained results are compared with the results of other authors on test problems of scheduling. It is shown that the best heuristics coefficients of the ant colony optimization algorithm differ even for similar job-shop scheduling problems. [ABSTRACT FROM AUTHOR]

This paper discusses the load-balanced task allocation problem in the grid transaction processing system to maximize reliability. Finding the solution of optimal load-balanced task allocation is known to be an NP-hard. This paper presents a load-balanced task allocation based cuckoo search-ant colony optimization (LBTA_CSACO) method for this problem. The LBTA_CSACO is based on the cooperative behavior of cuckoo search and ant colony optimization to nd a collection of task allocation solutions. This paper also models the reliability of the system with the help of the availability of the system. The motive of this work is to maximize the reliability of the system. After the simulation, six existing algorithms are taken for the comparison of the results; Honey Bee Optimization (HBO), Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized respectively. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Things (IoT) and ubiquitous presence of sensor nodes, positioning technologies have become a topic of interest among researchers. While the applications of positioning systems are very vast, determining the position of moving sensor nodes, finding missing people in large areas, and object tracking are among the most popular ones. The focus of this paper is to propose a positioning system to locate missing people in theme parks. Currently, radio frequency identification (RFID) systems are used in modern theme parks to locate lost visitors. In these systems, a wristband with active RFID tag is given to each visitor and RFID readers are deployed in predetermined locations. When a visitor is in the communication range of a reader, its location can be estimated based on the location of the reader. Therefore, the accuracy of these systems is relevant to the communication range of readers. Another limitation of RFID-based systems is due to the fact that readers cannot be placed in communication range of each other as they can interference with each other. It is clear that the only way to increase the accuracy of such systems is by increasing the number of readers and decreasing the communication range of each reader. In this paper, a Bluetooth low energy (BLE)-based system is proposed to be used in theme parks for locating lost visitors. The advantage of using BLE is due to the fact that it uses frequency hopping spread spectrum (FHSS) thus readers can be placed in communication range of each other without severe interference. In the proposed method, at first, the optimal places for deploying readers are obtained using ant colony optimization (ACO). Then, a fuzzy approach is used to increase the accuracy of the system. Three different signal levels are defined to be used in our fuzzy system based on which the location of visitors can be estimated. By using three levels of signal strength, the accuracy of the system is increased compared with the similar system with the similar number of readers. The simulation results show that the accuracy of the system is improved using this method, and the cost of the system is decreased as BLE readers are much less expensive than their RFID counterparts. [ABSTRACT FROM AUTHOR]

Mobile Adhoc Networks (MANET) in modern research have many optimal energy conservation mechanisms that can be deployed easily and in a faster manner. The routing approaches associated with energy consumption play a dominant role in routing the data packets between the mobile sensor nodes within the range of optimization. However, major challenges associated with energy consumption in MANETs include reduced lifetime of sensor nodes, poor coverage, and throughput. Most methods tend to reduce the interference of data while traversing between the sensor nodes and increase the capacity of the network. This results in delays while transmitting the packets across the network, and this may result in failure of packets being transmitted. To resolve this issue, in this paper, we propose an ant colony optimization combined with a flower pollination algorithm for minimal energy consumption and throughput maximisation in MANETs. This hybrid meta-heuristic model resolves the issues, including delays, poor coverage, and reduced network lifetime. This hybrid model uses the estimation of neighbourhood distance among the nodes for optimal placement of nodes for effective location. The estimation of location is found using a flower pollination algorithm with a levy flight mechanism. The estimation is carried out in a hyper sphere model that helps in finding the coverage area of the sensor nodes. Depending upon the estimation of neighbourhood distance among the sensor nodes, the consumption of energy among the sensor nodes in MANETs is reduced. The simulation was conducted between the proposed hybrid approach and conventional soft computing heuristics, where the results show that the proposed model achieves a higher rate of energy conservation and reduces delay than other methods. [ABSTRACT FROM AUTHOR]

The vehicle routing problem (VRP) has many applications in goods distribution and goods transportation. Today, many companies have requirements for VRP with multiple pickup and multiple delivery within due time. This problem is called multiple pickup and multiple delivery vehicle routing problem with time window (PDPTW). PDPTW has many constraints and ant colony optimization (ACO) has been used to solve it although ACO creates too many infeasible routes. Moreover, it often gets trapped in local optimum. To solve these problems, this paper proposed an improved ACO by using the route elimination technique and the pheromone reset technique. The ACO with route elimination technique, it has proven to solve the PDPTW problem with increased performance. The proposed technique was tested on datasets from the Li & Lim's PDPTW benchmark problems and provided more satisfactory results compared to other ACO techniques. [ABSTRACT FROM AUTHOR]

By facilitating the data delivery in wireless sensor networks, the movement of mobile sink can enhance the network connectivity and sensory coverage. However, the optimal path determination of mobile sink is a NP-hard optimization problem. By jointly considering the cluster-based routing and sink mobility, this paper proposes an enhanced ACO-based movement scheduling of mobile sink for data gathering in wireless sensor networks. To meet the delay requirements and balance the energy consumption of the sensor nodes, the optimal cluster heads selection is introduced. Then, an enhanced ACO-based movement scheduling algorithm is proposed to obtain the shortest path of mobile sink by traversing the network. The simulation results show that our proposed method can offer a promising performance in terms of reducing data delivery latency and extending the lifetime of the network. [ABSTRACT FROM AUTHOR]

The Josephson junction is a device consisting of two superconducting electrodes connected by a weak junction such as a thin insulation coating. The Josephson junction has chaotic behavior parameters not desirable in high-frequency applications. In this paper, a model predictive control approach based on an ant colony optimization algorithm is proposed to synchronize two Josephson junction models with different parameters. Here, the Josephson junction is described with a nonlinear model, and the synchronization is obtained using the slave-master technique. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using an ant colony optimization algorithm. Numerical simulations are conducted to assess the efficiency of the proposed algorithm. Also, a Monte Carlo evaluation is achieved to compute the statistic performance of the suggested controller. In addition, sensitivity analysis to changes in the number of ants and the number of iteration of the inner loop of the algorithm was performed. The results show that the controller is significantly sensitive to reducing the number of iteration of the inner loop. [ABSTRACT FROM AUTHOR]

In a cloud environment, reducing energy consumption while ensuring diverse quality of service (QoS) guarantees is challenging for task schedulers. Specifically, the energy-efficient scheduling for real-time tasks is more complicated because such tasks have strict time constraints. In this paper, we propose a two-stage scheduling method for deadline-constrained tasks. In the first stage, Enhanced Ant Colony Optimization (EACO) is a global scheduler that allocates incoming cloud tasks to suitable virtual machines (VMs). It can minimize makespan and energy consumption while guaranteeing strict deadline constraints. In the second stage, the Modified Backfilling (MBF) algorithm reorders VM's waiting queue to improve the task completion rate. We conduct two experiment series on synthetic and real trace datasets using the Cloudsim toolkit. Extensive experiments show that compared with other well-known task scheduling methods, our method can effectively reduce makespan by 25.28% and energy consumption by 23% on average. The task completion rate can be increased by 6.27%. The proposed method has a significant improvement compared with other well-known algorithms. [ABSTRACT FROM AUTHOR]