Symbiotic Organisms Search (SOS) algorithm is an effective new metaheuristic search algorithm, which has recently recorded wider application in solving complex optimization problems. SOS mimics the ...symbiotic relationship strategies adopted by organisms in the ecosystem for survival. This paper, presents a study on the application of SOS with Simulated Annealing (SA) to solve the well-known traveling salesman problems (TSPs). The TSP is known to be NP-hard, which consist of a set of (n−1)!/2 feasible solutions. The intent of the proposed hybrid method is to evaluate the convergence behaviour and scalability of the symbiotic organism's search with simulated annealing to solve both small and large-scale travelling salesman problems. The implementation of the SA based SOS (SOS-SA) algorithm was done in the MATLAB environment. To inspect the performance of the proposed hybrid optimization method, experiments on the solution convergence, average execution time, and percentage deviations of both the best and average solutions to the best known solution were conducted. Similarly, in order to obtain unbiased and comprehensive comparisons, descriptive statistics such as mean, standard deviation, minimum, maximum and range were used to describe each of the algorithms, in the analysis section. The Friedman's Test (with post hoc tests) was further used to compare the significant difference in performance between SOS-SA and the other selected state-of-the-art algorithms. The performances of SOS-SA and SOS are evaluated on different sets of TSP benchmarks obtained from TSPLIB (a library containing samples of TSP instances). The empirical analysis’ results show that the quality of the final results as well as the convergence rate of the new algorithm in some cases produced even more superior solutions than the best known TSP benchmarked results.
Edge computing is a new architecture to provide computing, storage, and networking resources for achieving the Internet of Things. It brings computation to the network edge in close proximity to ...users. However, nodes in the edge have limited energy and resources. Completely running tasks in the edge may cause poor performance. Cloud data centers (CDCs) have rich resources for executing tasks, but they are located in places far away from users. CDCs lead to long transmission delays and large financial costs for utilizing resources. Therefore, it is essential to smartly offload users' tasks between a CDC layer and an edge computing layer. This work proposes a cloud and edge computing system, which has a terminal layer, edge computing layer, and CDC layer. Based on it, this work designs a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are strictly met. In each time slot, this work jointly considers CPU, memory, and bandwidth resources, load balance of all heterogeneous nodes in the edge layer, maximum amount of energy, maximum number of servers, and task queue stability in the CDC layer. Considering the abovementioned factors, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based migrating birds optimization procedure to obtain a close-to-optimal solution. The proposed method achieves joint optimization of computation offloading between CDC and edge, and resource allocation in CDC. Realistic data-based simulation results demonstrate that it realizes higher profit than its peers. Note to Practitioners -This work considers the joint optimization of computation offloading between Cloud data center (CDC) and edge computing layers, and resource allocation in CDC. It is important to maximize the profit of distributed cloud and edge computing systems by optimally scheduling all tasks between them given user-specific response time limits of tasks. It is challenging to execute them in nodes in the edge computing layer because their computation resources and battery capacities are often constrained and heterogeneous. Current offloading methods fail to jointly optimize computation offloading and resource allocation for nodes in the edge and servers in CDC. They are insufficient and coarse-grained to schedule arriving tasks. In this work, a novel algorithm is proposed to maximize the profit of distributed cloud and edge computing systems while meeting response time limits of tasks. It explicitly specifies the task service rate and the selected node for each task in each time slot by considering resource limits, load balance requirement, and processing capacities of nodes in the edge, and server and energy constraints in CDC. Real-life data-driven simulations show that the proposed method realizes a larger profit than several typical offloading strategies. It can be readily implemented and incorporated into large-scale industrial computing systems.
Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. To improve the probability of system successfully completing the next mission, ...maintenance action is carried out on components during the breaks. In this work, a selective maintenance model with stochastic maintenance quality for multi-component systems is investigated. At each scheduled break, a set of maintenance actions with different degrees of impact are available for each component. The impact of a maintenance action is assumed to be random and follow an identified probability distribution. The corresponding maintenance cost and time are modelled based on the expected impact of the maintenance action. The objective of selective maintenance scheduling is to find the cost-optimal maintenance action for each component at every scheduled break subject to reliability and duration constraints. A simulated annealing algorithm is used to solve the complicated optimisation problem where both multiple maintenance actions and stochastic quality model are taken into account. Two illustrative numerical examples and a real case study have been solved to demonstrate the performance of the proposed approach. A comparison with deterministic maintenance shows the importance of considering the proposed stochastic quality in selective maintenance scheduling.
In this paper, four simulated annealing based multiobjective algorithms—SMOSA, UMOSA, PSA and WMOSA have been used to solve multiobjective optimization of constrained problems with varying degree of ...complexity along with a new PDMOSA algorithm. PDMOSA algorithm uses a strategy of Pareto dominant based fitness in the acceptance criteria of simulated annealing and is improved. In all algorithms, the current solution explores its neighborhoods in a way similar to that of classical simulated annealing. The performance and computational cost for all algorithms have been studied. All algorithms are found to be quite robust with algorithmic parameters and are capable of generating a large number of well diversified Pareto-optimal solutions. The quality and diversification of Pareto-optimal solutions generated by all algorithms are found to be problem specific. The computational cost is least by WMOSA and is followed by PDMOSA. The algorithms are simple to formulate and require reasonable computational time. Hence, the simultaneous use of all algorithms is suggested to obtain a wider spectrum of efficient solutions.
Boson sampling devices are a prime candidate for exhibiting quantum supremacy, yet their application for solving problems of practical interest is less well understood. Here we show that Gaussian ...boson sampling (GBS) can be used for dense subgraph identification. Focusing on the NP-hard densest k-subgraph problem, we find that stochastic algorithms are enhanced through GBS, which selects dense subgraphs with high probability. These findings rely on a link between graph density and the number of perfect matchings-enumerated by the Hafnian-which is the relevant quantity determining sampling probabilities in GBS. We test our findings by constructing GBS-enhanced versions of the random search and simulated annealing algorithms and apply them through numerical simulations of GBS to identify the densest subgraph of a 30 vertex graph.
Network traffic forecasting provides key information for network management, resource allocation, traffic attack detection. However, traditional linear and non-linear network traffic forecasting ...models cannot achieve enough prediction accuracy for future traffic prediction. In order to resolve this problem, a network traffic prediction method based on SA (Simulated Annealing) optimized ARIMA (Autoregressive Integrated Moving Average model)-BPNN (Back Propagation Neural Network) is proposed in this paper, which makes comprehensive use of linear model ARIMA, non-linear model BPNN and optimization algorithm SA. With enhancement of the BPNN global optimization ability, it can fully realize the potential of mining linear and non-linear laws of historical network traffic data, hence improving the prediction accuracy. This paper selects the historical network traffic data of two different sampling points in the WIDE project to predict, and utilizes the MAE(Mean Absolute Error), RMSE(Root Mean Square Error), and the MAPE(Mean Absolute Percentage Error) as the evaluation index of the prediction effect. Experimental results show that our proposed method outperformed traditional network traffic prediction model, with several improvements in network traffic prediction accuracy.
•The addition of a supplementary derivative control to the hydro-governors through a network control system and its distributive control action obtained by the application of an evolutionary game ...theory, the replicator dynamics.•The proper tuning of the control parameters that guarantee an optimal response in the frequency control application.•The combination of two powerful methods to improve the primary frequency control to mitigate the integration of the renewables into the grid.•The application of the methods in two systems, an aggregated model and a large power system.
Sweden, a country with abundant hydro power, has expectations to include more wind power into its electrical system. Currently, in order to improve the frequency response requirements of its electrical system, the country is considering upgrading its hydro-governors. This effort is part of maintaining the system frequency and reaction within their limits following any disturbance events. To partially compensate for increased frequency fluctuations due to an increased share of renewables on its system, the frequency response of hydro-governors should be improved. This paper proposes an innovative network control system, through a supplementary control, for the improvement of the hydro-governor’s action. This supplementary control allows having more flexibility over the control action and improves the primary frequency control, and thereby the overall system frequency response. The proposed supplementary control, based on an evolutionary game theory strategy, uses remote measurements and a hierarchical dynamic adjustment of the control. Additionally, in order to guarantee an optimal response, a Simulated Annealing Algorithm (SAA) is combined with the supplementary control. This paper illustrates the analysis and design of the proposed methodology, and is tested on two power systems models: (i) an aggregated model that represents the frequency response of Sweden, Norway and Finland, and (ii) The Nordic 32 test system.
Fuel consumption accounts for a large and increasing part of transportation costs. In this paper, the Fuel Consumption Rate (FCR), a factor considered as a load dependant function, is added to the ...classical capacitated vehicle routing problem (CVRP) to extend traditional studies on CVRP with the objective of minimizing fuel consumption. We present a mathematical optimization model to formally characterize the FCR considered CVRP (FCVRP) as well as a string based version for calculation. A simulated annealing algorithm with a hybrid exchange rule is developed to solve FCVRP and shows good performance on both the traditional CVRP and the FCVRP in substantial computation experiments. The results of the experiments show that the FCVRP model can reduce fuel consumption by 5% on average compared to the CVRP model. Factors causing the variation in fuel consumption are also identified and discussed in this study.
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•Progress in flash-flood assessment by novel Ensembles and Bayesian based models.•Elimination of the redundant variables by the simulated annealing (SA) method.•Identification of 9 ...features among 15 using the SA method as key predictors.•High performance for all the proposed predictive models (Accuracy greater than 90%).
Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the mostdevastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. Results of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy = 90–92%, Kappa = 79–84%, Success ratio = 94–96%, Threat score = 80–84%, and Heidke skill score = 79–84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions.
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