7,359 research outputs found

    Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

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    The microservice-based smart grid service (SGS) organization and the heterogeneous multi-cores-based computing resource supply are the development direction of edge computing in smart grid with high penetration of renewable energy sources and high market-oriented. However, their application also challenges the service schedule for edge computing apparatus (ECA), the physical carrier of edge computing. In the traditional scheduling strategy of SGS, an SGS usually corresponds to an independent application or component, and the heterogeneous multi-core computing environment is also not considered, making it difficult to cope with the above challenges. In this paper, we propose an SGS scheduling strategy for the ECA. Specifically, we first present an SGS scheduling framework of ECA and give the essential element of meeting SGS scheduling. Then, considering the deadline and importance attributes of the SGS, a microservice scheduling prioritizing module is proposed. On this basis, the inset-based method is used to allocate the microservice task to the heterogeneous multi-cores to utilize computing resources and reduce the service response time efficiently. Furthermore, we design the scheduling unit dividing module to balance the delay requirement between the service with early arrival time and the service with high importance in high concurrency scenarios. An emergency mechanism (EM) is also presented for the timely completion of urgent SGSs. Finally, the effectiveness of the proposed service scheduling strategy is verified in a typical SGS scenario in the smart distribution transformer area

    Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: an application to fish aggregating devices

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    The paper addresses the synergies from combining a heuristic method with a predictive technique to solve the Dynamic Traveling Salesman Problem (DTSP). Particularly, we build a genetic algorithm that feeds on Newton's motion equation to show how route optimization can be improved when targets are constantly moving. Our empirical evidence stems from the recovery of fish aggregating devices (FADs) by tuna vessels. Based on historical real data provided by GPS buoys attached to the FADs, we first estimate their trajectories to feed a genetic algorithm that searches for the best route considering their future locations. Our solution, which we name Genetic Algorithm based on Trajectory Prediction (GATP), shows that the distance traveled is significantly shorter than implementing other commonly used methods.European Regional Development Fund | Ref. 10SEC300036PRMinisterio de Economía y Competitividad | Ref. ECO2013-45706

    A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

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    With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches

    Integrating forecasting in metaheuristic methods to solve dynamic routing problems: evidence from the logistic processes of tuna vessels

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    The multiple Traveling Salesman Problem (mTSP) is a widespread phenomenon in real-life scenarios, and in fact it has been addressed from multiple perspectives in recent decades. However, mTSP in dynamic circumstances entails a greater complexity that recent approaches are still trying to grasp. Beyond time windows, capacity and other parameters that characterize the dynamics of each scenario, moving targets is one of the underdeveloped issues in the field of mTSP. The approach of this paper harnesses a simple prediction method to prove that integrating forecasting within a metaheuristic evolutionary-based method, such as genetic algorithms, can yield better results in a dynamic scenario than their simple non-predictive version. Real data is used from the retrieval of Fish Aggregating Devices (FADs) by tuna vessels in the Indian Ocean. Based on historical data registered by the GPS system of the buoys attached to the devices, their trajectory is firstly forecast to feed subsequently the functioning of a genetic algorithm that searches for the optimal route of tuna vessels in terms of total distance traveled. Thus, although valid for static cases and for the Vehicle Routing Problem (VRP), the main contribution of this method over existing literature lies in its application as a global search method to solve the multiple TSP with moving targets in many dynamic real-life optimization problems.Ministerio de Economía y Competitividad | Ref. ECO2016-76625-RXunta de Galicia | Ref. GRC2014/02

    An Improvement of Load Flow Solution for Power System Networks using Evolutionary-Swarm Intelligence Optimizers

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    Load flow report which reveals the existing state of the power system network under steady operating conditions, subject to certain constraints is being bedeviled by issues of accuracy and convergence. In this research, five AI-based load flow solutions classified under evolutionary-swarm intelligence optimizers are deployed for power flow studies in the 330kV, 34-bus, 38-branch section of the Nigerian transmission grid. The evolutionary-swarm optimizers used in this research consist of one evolutionary algorithm and four swarm intelligence algorithms namely; biogeography-based optimization (BBO), particle swarm optimization (PSO), spider monkey optimization (SMO), artificial bee colony optimization (ABCO) and ant colony optimization (ACO). BBO as a sole evolutionary algorithm is being configured alongside four swarm intelligence optimizers for an optimal power flow solution with the aim of performance evaluation through physical and statistical means. Assessment report upon application of these standalone algorithms on the 330kV Nigerian grid under two (accuracy and convergence) metrics produced PSO and ACO as the best-performed algorithms. Three test cases (scenarios) were adopted based on the number of iterations (100, 500, and 1000) for proper assessment of the algorithms and the results produced were validated using mean average percentage error (MAPE) with values of voltage profile created by each solution algorithm in line with the IEEE voltage regulatory standards. All algorithms proved to be good load flow solvers with distinct levels of precision and speed. While PSO and SMO produced the best and worst results for accuracy with MAPE values of 3.11% and 36.62%, ACO and PSO produced the best and worst results for convergence (computational speed) after 65 and 530 average number of iterations. Since accuracy supersedes speed from scientific considerations, PSO is the overall winner and should be cascaded with ACO for an automated hybrid swarm intelligence load flow model in future studies. Future research should consider hybridizing ACO and PSO for a more computationally efficient solution model

    The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems

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    The Travel Salesman Problem (TSP) consists in finding the minimal-length closed tour that connects the entire group of nodes of a given graph. We propose to solve such a combinatorial optimization problem with the AddACO algorithm: it is a version of the Ant Colony Optimization method that is characterized by a modified probabilistic law at the basis of the exploratory movement of the artificial insects. In particular, the ant decisional rule is here set to amount in a linear convex combination of competing behavioral stimuli and has therefore an additive form (hence the name of our algorithm), rather than the canonical multiplicative one. The AddACO intends to address two conceptual shortcomings that characterize classical ACO methods: (i) the population of artificial insects is in principle allowed to simultaneously minimize/maximize all migratory guidance cues (which is in implausible from a biological/ecological point of view) and (ii) a given edge of the graph has a null probability to be explored if at least one of the movement trait is therein equal to zero, i.e., regardless the intensity of the others (this in principle reduces the exploratory potential of the ant colony). Three possible variants of our method are then specified: the AddACO-V1, which includes pheromone trail and visibility as insect decisional variables, and the AddACO-V2 and the AddACO-V3, which in turn add random effects and inertia, respectively, to the two classical migratory stimuli. The three versions of our algorithm are tested on benchmark middle-scale TPS instances, in order to assess their performance and to find their optimal parameter setting. The best performing variant is finally applied to large-scale TSPs, compared to the naive Ant-Cycle Ant System, proposed by Dorigo and colleagues, and evaluated in terms of quality of the solutions, computational time, and convergence speed. The aim is in fact to show that the proposed transition probability, as long as its conceptual advantages, is competitive from a performance perspective, i.e., if it does not reduce the exploratory capacity of the ant population w.r.t. the canonical one (at least in the case of selected TSPs). A theoretical study of the asymptotic behavior of the AddACO is given in the appendix of the work, whose conclusive section contains some hints for further improvements of our algorithm, also in the perspective of its application to other optimization problems

    The Viability of Domain Constrained Coalition Formation for Robotic Collectives

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    Applications, such as military and disaster response, can benefit from robotic collectives' ability to perform multiple cooperative tasks (e.g., surveillance, damage assessments) efficiently across a large spatial area. Coalition formation algorithms can potentially facilitate collective robots' assignment to appropriate task teams; however, most coalition formation algorithms were designed for smaller multiple robot systems (i.e., 2-50 robots). Collectives' scale and domain-relevant constraints (i.e., distribution, near real-time, minimal communication) make coalition formation more challenging. This manuscript identifies the challenges inherent to designing coalition formation algorithms for very large collectives (e.g., 1000 robots). A survey of multiple robot coalition formation algorithms finds that most are unable to transfer directly to collectives, due to the identified system differences; however, auctions and hedonic games may be the most transferable. A simulation-based evaluation of three auction and hedonic game algorithms, applied to homogeneous and heterogeneous collectives, demonstrates that there are collective compositions for which no existing algorithm is viable; however, the experimental results and literature survey suggest paths forward.Comment: 46 pages, 9 figures, Swarm Intelligence (under review
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