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Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users' jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits. [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 load data features of some electricity-theft consumers during the theft period are similar to those of normal consumers, making these electricity-theft consumers outliers from the cluster of electricity-theft. The current classification method, which uses the mean value to determine the cluster centers, is vulnerable to the influence of outliers. Therefore, this paper proposes a self-decision ant colony clustering algorithm for electricity theft detection method that is targeted to self-decision which samples are used to update the cluster centers. The method constructs a dynamic weighting approach to determine the cluster centers based on the idea of Backpropagation, and updates the weights of each sample in the clusters to reflect the different importance of different samples, thus reducing the influence of outlier samples. A new activation function, Odd, is proposed to enhance the ability of the proposed method to solve linearly indistinguishable problems. A self-decision dropout mechanism is proposed which evolves the mechanism of randomly stopping the work of samples in clusters into a targeted and self-decision mechanism that stops the work of redundant or non-active samples as well as improves the contribution of outlier samples with positive effects. In this paper, the proposed method is tested by the electricity consumption data provided by the State Grid Corporation of China (SGCC) and the Smart* Data Set for Sustainability (SDSS) provided by the UMass Trace Repository, and the experimental results show that the proposed method effectively solves the above problems with higher detection accuracy, it has certain advantages over other current studies. [ABSTRACT FROM AUTHOR]

As a meta-heuristic algorithm, the ant colony algorithm has been successfully used to solve various combinatorial optimization problems. However, the existing algorithm that takes the power of ants to solve distributed constraint optimization problems (ACO_DCOP) is easy to fall into local optima. To deal with this issue, this paper presents an adaptive ant colony algorithm based on local information entropy to solve distributed constraint optimization problems, named LIEAD. In LIEAD, the local information entropy is introduced to help agents adaptively select the pheromone update strategy and value selection strategy, which improves the convergence speed and the quality of the solution. Moreover, a restart mechanism is designed to break the accumulation state of pheromone, which increases the population diversity and helps the algorithm jump out of the local optima. The extensive experimental results indicate that LIEAD can significantly outperform ACO_DCOP and is competitive with the state-of-the-art DCOPs algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε -greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence. [ABSTRACT FROM AUTHOR]

Aiming at the problem of slow search speed and insufficient solution quality of ant colony algorithm, this paper proposed an ant colony algorithm combining dynamic hierarchical clustering and neighborhood interval reorganization. In the ini-tial stage, the algorithm adjusted the hierarchical clustering threshold and iterated to the target cluster based on the principle of minimum inter-cluster distance merging, and then merged the clusters according to the pre-merging coefficient. In the next place it used ant colony system to generate subclass paths, disconnected and reorganized the initial paths to improve the overall convergence speed of the algorithm. Then the algorithm used ant colony system to optimize the solution space, and at the same time, carried out neighborhood interval diffusion reorganization between clusters to increase the diversity of solutions. Furthermore, if the quality of the reconstructed solution was better than the current optimization solution at a fixed number of iterations, the recommendation process would be carried out to improve the accuracy of the solution. When the algorithm stagnated, it introduced an adjustment factor to reduce the differences between pheromones of each path to enhance the ant search ability, which could help the algorithm jump out of the local optimal. The experiment result shows that the accuracy of the algorithm is about 3% when facing large-scale problems, which can effectively improve the accuracy and convergence rate of the solution. [ABSTRACT FROM AUTHOR]

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary: A sensor network is a situation‐based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load‐balanced and fuzzy‐ACO (DLB‐fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree‐ and energy‐balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster‐based hierarchical routing. Communication is performed at two levels. In the first level, node‐to‐cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy‐integrated ACO method is applied for inter‐cluster route formation. The route optimization is here performed under fuzzy‐based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed load‐balanced protocol is analyzed against the LEACH, LEACH‐C, LEACH‐CC, M‐LEACH, Fuzzy‐PSO, Fuzzy‐ACO, Fuzzy‐Cuckoo, and FMCB‐ER protocols. The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms. [ABSTRACT FROM AUTHOR]

Summary: The widely deployed wireless sensor networks, incorporated with the capabilities of sensing, processing, and communicating, require more effective routing to maintain the balance between the performance parameters and to be prevented from the security attacks. The cryptographic methods used were effective but consumed more energy on the authentication, the encryption, and the decryption. So the routing addressing the security problems, with the reduced energy consumption, latency, and PDR losses, maximizing the network lifetime is proposed in the paper. This multiobjective routing problem of the sensor networks is handled using the ant colony routing techniques, taking into consideration the remaining energy of the nodes, the distance between the nodes, and the trust credentials of the nodes, to reduce the energy consumption and enhance the lifetime of the sensor networks. The further performance evaluation using the network simulator‐2 reveals the efficiency and the quality of the service of proposed routing technique over existing techniques on the grounds of energy utilization, PDR, lifetime of sensor networks, and the security enhancement rate. [ABSTRACT FROM AUTHOR]

To overcome the lengthy search time, massive space occupation, and overlong planned path of the traditional A* algorithm, this paper integrates the bidirectional search with the intelligent ant colony algorithm to obtain the heuristic function selection factor, and uses the factor to improve the evaluation function of the algorithm. The simulation results show that the improved algorithm achieved better dynamic navigation than the traditional A* algorithm both in search time and distance, featuring shorter path searching time and the algorithm running time. Therefore, the result of this research has effectively reduced the search time and enhanced the dynamic search. [ABSTRACT FROM AUTHOR]

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. [ABSTRACT FROM AUTHOR]

Traffic congestion continues to be a persistent problem throughout the world. As vehicle-to-vehicle communication develops, there is an opportunity of using cooperation among close proximity vehicles to tackle the congestion problem. The intuition is that if vehicles could cooperate opportunistically when they come close enough to each other, they could, in effect, spread themselves out among alternative routes so that vehicles do not all jam up on the same roads. Our previous work proposed a decentralized multiagent based vehicular congestion management algorithm entitled Congestion Avoidance and Route Allocation using Virtual Agent Negotiation (CARAVAN), wherein the vehicles acting as intelligent agents perform cooperative route allocation using inter-vehicular communication. This paper focuses on evaluating the practical applicability of this approach by testing its robustness and performance (in terms of travel time reduction), across variations in: (a) environmental parameters such as road network topology and configuration; (b) algorithmic parameters such as vehicle agent preferences and route cost/preference multipliers; and (c) agent-related parameters such as equipped/non-equipped vehicles and compliant/non-compliant agents. Overall, the results demonstrate the adaptability and robustness of the decentralized cooperative vehicles approach to providing global travel time reduction using simple local coordination strategies. [ABSTRACT FROM AUTHOR]

The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user’s Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user’s QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time. [ABSTRACT FROM AUTHOR]

As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved. [ABSTRACT FROM AUTHOR]

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system’s gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics. [ABSTRACT FROM AUTHOR]

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks. [ABSTRACT FROM AUTHOR]

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques. [ABSTRACT FROM AUTHOR]

Decreasing the route rediscovery time process in reactive routing protocols is challenging in mobile ad hoc networks. Links between nodes are continuously established and broken because of the characteristics of the network. Finding multiple routes to increase the reliability is also important but requires a fast update, especially in high traffic load and high mobility where paths can be broken as well. The sender node keeps re-establishing path discovery to find new paths, which makes for long time delay. In this paper we propose an improved multipath routing protocol, called Receiver-based ad hoc on demand multipath routing protocol (RB-AOMDV), which takes advantage of the reliability of the state of the art ad hoc on demand multipath distance vector (AOMDV) protocol with less re-established discovery time. The receiver node assumes the role of discovering paths when finding data packets that have not been received after a period of time. Simulation results show the delay and delivery ratio performances are improved compared with AOMDV. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a lustering algorithm based on nt olony ptimization (ACO) for VAs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. [ABSTRACT FROM AUTHOR]