Combination Of Simulated Annealing Research Articles

Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management

In this research, an exergetic mathematical model is proposed for closed-loop food supply chain network design considering economic, environmental, and social aspects. The studded closed-loop food supply chain consists of six echelons, four echelons for forwarding the products (suppliers, factory, distribution centers, and customers), and two echelons for returning the products (collecting centers and disposal centers). The exergetic model is based on the extended exergy accounting method, which calculates the consumed exergy in all six echelons by considering the equivalents of labor and capital exergy. This model aims to minimize both the economic and exergetic costs associated with the food supply chain. To solve the proposed mathematical model, an ensemble global–local search metaheuristic algorithm utilizing a combined whale optimization algorithm (WOA) and simulated annealing (SA), called CWOASA, is proposed. In the CWOASA algorithm, first, WOA is used to search the entire search space. Next, SA is applied to further improve the best solution obtained by WOA. A real dataset of a dairy supply chain is used to evaluate the efficiency of the proposed metaheuristic-driven exergy analysis model and claim its benefits over the existing methods. Simulation results revealed that the proposed method leads to a 6.74% reduction in the total exergy consumption of the supply chain. The proposed methodology offers valuable insights, shedding light on the prospective gains in terms of mitigated environmental impact for each incremental economic expenditure, thus laying the foundation for promising avenues of future research.

Application of CSA to loading pattern optimization problems for reload core in nuclear power plant

Reload loading pattern design is the key content to economy, safety and flexibility of the operation after reloading in nuclear power plant. Loading pattern optimization is a typical combinatorial optimization problem with multi-objective and multi-parameter. Furthermore, the reload loading pattern optimization design is a very short time requirement, which makes it difficult for traditional optimization algorithms. In this study, a combined simulated annealing algorithm (CSA algorithm) are proposed. CSA effectively utilizes the characteristic regularity in the optimization space. Additionally, the combined database created by CSA can significantly increase the diversity of the algorithm, and greatly improve the optimization ability. In comparison to the traditional simulated annealing method, the optimization results of CSA are noticeably better. By contrasting the CSA with the improved genetic algorithm (GA), it can be seen that CSA can exploit the characteristic regularity of the reload loading pattern optimization more effectively, and the optimization efficiency and optimization results of the method are noticeably superior to GA.

Optimized low carbon scheduling strategy of integrated energy sources considering load aggregators

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES.

Forecasting the onset of volcanic eruptions using the increase in seismicity during magma ascent

The Failure Forecast Method (FFM) has been used to forecast the onset of volcanic eruptions with varying degrees of success. The method involves fitting its empirical equation to precursory observables, e.g., seismic data. Current models explaining the empirical equation assume that the seismic observables used (e.g., seismic event-rate, seismic moment-rate or seismic energy-rate) are related to accelerated fracture growth. In these models, such fracture growth results in an apparent increase in seismic activity. In this study, however, we propose an alternative explanation for the increase in seismicity culminating in volcanic eruptions. We suggest that the increase in seismicity, particularly in the seismic amplitudes, can result from ascending seismic sources towards the surface and that the increase can be used for eruption forecasts using the FFM. Our argument is based on the need for magma to migrate towards the surface to produce an eruption.To test our argument, we used synthetic and field/real data and fitted them with the FFM model. The synthetic data is generated using the body wave attenuation equation, and the real data is from the Piton de la Fournaise eruption on January 2nd, 2010. We used seismic amplitudes and seismic amplitude ratios as the precursory observables and used a combination of Simulated Annealing and Bayesian Inversion algorithms to obtain the best-fit parameters of the FFM model and their posterior distributions. For the synthetic cases, we found that the method gave reasonable estimates of failure or eruption time, suggesting that the seismic amplitudes and the seismic amplitude ratios can be used for forecasting using the FFM. For the field/real cases, the results gave information about the timing of magma chamber failure, magma propagation, and the eruption onset. Our results highlight an example of modelling volcano monitoring data with the FFM that gives reasonable estimates of eruption time even if the data may not fully represent failure processes. We suggest that the result may still be reasonable owing to the empirical and asymptotic nature of the FFM combined with the underlying physics of magma and seismic migrations.

Mobile edge computing servers deployment with improved genetic algorithm in cellular Internet of Things

Mobile edge computing (MEC) provides services to devices and reduces latency in cellular internet of things (IoT) networks. However, the challenging problem is how to deploy MEC servers economically and efficiently. This paper investigates the deployment problem of MEC servers of the real-world road network by employing an improved genetic algorithm (GA) scheme. We first use the threshold-based K-means algorithm to form vehicle clusters according to their locations. We then select base stations (BSs) based on clustering center coordinates as the deployment locations set for potential MEC servers. We further select BSs using a combined simulated annealing (SA) algorithm and GA to minimize the deployment cost. The simulation results show that the improved GA deploys MEC servers effectively. In addition, the proposed algorithm outperforms GA and SA algorithms in terms of convergence speed and solution quality.

Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study

In this study, we examined ticket pricing and train stop planning for the high-speed railway (HSR), which integrates two key aspects of railway operation and organization. We considered that passenger demand is sensitive to the generalized travel cost (depending on the ticket price and the travel time) and that the train stop plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and train stop planning to maximize HSR’s transport revenue and minimize passengers’ travel time. Based on the high similarity between combinatorial optimization problems and the solid annealing principle, we designed a combined simulated annealing (CSA) algorithm to solve practical problems. The results of a numerical example in the real HSR network showed that the proposed method can improve transport revenue by 5.1% and reduce passengers’ travel time loss by 11.15% without increasing transport capacity.

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

One problem in collaborative pickup delivery problem (PDP) was excessive outsourced jobs. It happened in many studies on the collaborative PDP. Besides, the revenue sharing in it was unclear although important. This work aimed to propose a novel collaborative PDP model which minimizes total travel distance while maintains low outsourced jobs. It proposed several contributions. First, it prioritized internal jobs first rather than full collaborative model. Second, it proposed new revenue sharing model. It adopted cluster-first route-second and mixed pickup and delivery. It was developed by combining the genetic algorithm and nearest distance algorithm where the genetic algorithm was used in the clustering process and the nearest distance was used in the routing process. The simulation result shows that the proposed model was better than the comparing models: (1) combined K-means and genetic algorithm model (KMGA) and (2) combined simulated annealing and last-in first-out (SNLIFO) model. When the number of orders was high (300 units), the total travel distance of the proposed model was 37 percent lower than the KMGA model and 30 percent lower than the SNLIFO model. In average, the outsourcing rate of the proposed model was 70 percent lower than the previous models.

An intelligent optimization method of reload core loading pattern and its application

Reload core loading pattern design is the key content related to safety, economy and flexibility of the operation after refueling in nuclear power plant. The optimization design of the reload loading pattern is a very short time requirements, great difficulty, high experience requirements and repeatable work. According to these above rigorous requirements, based on statistical learning and random sampling, this study proposes an intelligent method of reload core loading pattern optimization——Probability Sampling Loading Pattern Optimization (PSLPO). This method intelligently extracts the characteristic law in the optimization space of the reload core loading pattern by statistical learning method to establish the probability model. And the probability model is embedded into PSLPO by the random sampling method. Finally, PSLPO method is constantly updated and evolved in the optimization process by intelligent means such as dynamic adjustment weight factor, probability model adaptive adjustment mechanism and combined simulated annealing design, which realizes the improvement of the optimization ability of the algorithm continuously. According to the actual reload design verification for cycle 5 of Hainan nuclear power plant unit 2, it is shown that the recommended pattern of PSLPO is better than manual search pattern in the aspect of cycle length and power distribution, and the method can intelligently give multiple design patterns, which users can have many choices. Through the optimization evaluation with a single computer core, the optimization efficiency of the method can meet the needs of core reload design. The global optimization ability can be further improved through parallelization and algorithm parameter optimization.

Hierarchical structure modelling in uncertain emergency location-routing problem using combined genetic algorithm and simulated annealing

Hierarchical structure modelling in uncertain emergency location-routing problem using combined genetic algorithm and simulated annealing

Hierarchical structure modelling in uncertain emergency location-routing problem using combined genetic algorithm and simulated annealing

Hierarchical structure modelling in uncertain emergency location-routing problem using combined genetic algorithm and simulated annealing

Optimization of the Spectrum Splitting and Auction for 5th Generation Mobile Networks to Enhance Quality of Services for IoT from the Perspective of Inclusive Sharing Economy

Fifth generation (5G) mobile networks can accomplish enhanced communication capabilities and desired to connect things in addition to people. By means of optimally splitting the spectrum to integrate more efficient segments, mobile operators can deliver better Quality of Services (QoS) for Internet of Things (IoT), even the nowadays so-called metaverse need broadband mobile communication. Drawing on the Theory of Quality Value Transformation, we developed a 5G ecosystem as a sustainable organic coalition constituted of planners, providers, and users. Most importantly, we put forward the altruism as the ethics drive for the organic cooperative evolution to sustain the inclusive sharing economy to solve the problem of the Theory of Games and Economic Behavior. On the top of the collaboration framework for the coalition game for 5G, we adopted Pareto Optimality as the target situation for the optimization via cooperative evolution and further apply ISO 25000 to define the metrics for the value of 5G corresponding to Pareto Frontier. Based on the collaboration framework as above, we conducted a survey to gather the features and costs for the 5G spectrum in relation to IoT and the financial status of the mobile operators as the constraint for the optimization. Taking Simultaneous Multi-Round Auction (SMRA) as the standard rule for spectrum auction, we developed a novel optimization program of two hybrid metaheuristics with the combination of Simulated Annealing (SA), Genetic Algorithm (GA), and Random Optimization (RO) for the multiple objectives of quality, usability, and costs. The results of the simulation show that the coalition game for 5G spectrum auction is a dynamic group decision in which the government authority and mobile operators can achieve a synergy to maximize the profits, quality score, and usability, and minimize the costs. Last but not least, the hybrid metaheuristic with SA and RO is more efficient and effective than that with GA and BO, from the perspective of inclusive sharing economy. It is the first study of its kind as we know.

Noisy multimodal brain image registration using markov random field model

In this paper, a new scheme is proposed to register brain Magnetic Resonance (MR) and Computed Tomography (CT) images under a noisy environment. The scheme is developed based on the notion of mutual information and this is extended to the feature space-based registration. The 2D joint histogram of the noisy MR and CT images has been computed and this is assumed to be the degraded version of the true 2D joint histogram that corresponds to the CT and MR images. The true 2D joint histogram is modeled as Markov Random Field (MRF) model and is estimated from the degraded one by formulating the problem in Maximum A Posterior (MAP) estimation framework. The MAP estimates are obtained by the combination of Simulated Annealing (SA) and Iterated Conditioned Mode (ICM) algorithms. The Mutual Information (MI) at different values of the parameter vector θ are computed from the estimated joint histogram and is maximized to determine the optimal registration parameter. The MI curve is modeled as multivariate Gaussian distribution and the registration parameter vector is obtained with a confidence interval. The scheme has been tested with the slices of patients from the Retrospective Image Registration Evaluation (RIRE) database. The efficacy of the scheme on MR and CT volumes is demonstrated by rendering the registered slices. The proposed algorithm is successfully tested with different degrees of Gaussian, Rician and Rayleigh noise conditions. Performance measures such as parameter estimation errors, error statistics and landmark based validation are used to demonstrate the efficacy of the proposed algorithm. The scheme is found to exhibit improved performance, especially at high noisy conditions as compared to the existing schemes.

A novel multi-objective optimization algorithm for Pareto design of a fuzzy full state feedback linearization controller applied on a ball and wheel system

The control and stabilization of a ball and wheel system around the equilibrium point are challenging tasks because it is an underactuated, nonlinear, and open-loop unstable plant. In this paper, Pareto design of a Fuzzy Full State Feedback Linearization Controller (FFSFLC) for the ball and wheel system based upon a novel multi-objective optimization algorithm is introduced. To this end, at first, a full state feedback linearization approach is employed to stabilize the dynamics of the system. Next, appropriate fuzzy systems are determined to tune the control gains. Then, a new multi-objective optimization algorithm is utilized to promote the proposed control scheme. This optimization algorithm is a combination of Simulated Annealing (SA) and Artificial Bee Colony (ABC) approaches benefiting advantages of the non-dominated Pareto solutions. To evaluate the capabilities of the suggested algorithm, its optimal solutions of several standard test functions are compared with those of five renowned multi-objective optimization algorithms. The results confirm that the proposed hybrid algorithm yields closer non-dominated Pareto solutions to the true optimal Pareto front with shorter runtimes than other algorithms. After selecting proper objective functions, multi-objective optimization of FFSFLC for the ball and wheel system is performed, and the results are compared with previous works. Simulations illustrate that the proposed strategies can accurately converge the system states to the desired conditions and yield superior robustness against disturbance signals in comparison with former studies.

Herd immunity levels and multi-strain influenza epidemics in Russia: a modelling study

AbstractIn the present paper, we consider a compartmental epidemic model which simulates the co-circulation of three influenza strains, A(H1N1)pdm09, A(H3N2), and B, in a population with the history of exposure to these virus strains. A strain-specific incidence data for the model input was generated using long-term weekly ARI incidence and virologic testing data. The algorithm for model calibration was developed as a combination of simulated annealing and BFGS optimization methods. Two simulations were carried out, assuming the absence and the presence of protected individuals in the population, with 2017– 2018 and 2018–2019 epidemic seasons in Moscow as a case study. It was shown that strain-specific immune levels defined by virologic studies might be used in the model to obtain plausible incidence curves. However, different output parameter values, such as fractions of individuals exposed to particular virus strain in the previous epidemic season, can correspond to similar incidence trajectories, which complicates the assessment of herd immunity levels based on the model calibration. The results of the study will be used in the research of the interplay between the immunity formation dynamics and the circulation of influenza strains in Russian cities.

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

SummarySoftware development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy‐based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global–local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well‐known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

Size Effect of (CuO)n (n = 1–6) Clusters on the Modification of Rutile–TiO2 Photocatalysts

The size‐dependent properties of metal/metal oxide clusters have received increasing interest due to their significant role in promoting heterogeneous catalysis. Herein, an ab initio method is used to investigate the photocatalytic properties of TiO2‐supported (CuO)n nanoclusters (n = 1−6). The molecular configuration and energetic evolution of gas‐phase (CuO)n clusters are first investigated using a combined simulated annealing–density functional theory (DFT) method, and the quantum size effect is found in planar cluster structures due to the scarcity of electron levels. Subsequently, by supporting the (CuO)n clusters on rutile–TiO2 (110) facets, the stability, the light‐absorption ability, the charge separation efficiency, and the reactivity of excited electrons for different (CuO)n−TiO2 heterojunctions are analyzed. It is noted that (CuO)3 and (CuO)4 clusters have the best antiaggregation property, and the small clusters usually possess higher charge separation efficiency, whereas large clusters show better light‐absorption performance. Photocatalytic hydrogen evolution reaction is favored on middle‐sized CuO clusters‐modified TiO2, e.g., (CuO)3−TiO2, due to its proper band alignment, high photoelectron reactivity, good light‐absorption ability, and structural stability.

Hybrid Approaches based on Simulated Annealing, Tabu Search and Ant Colony Optimization for Solving the k-Minimum Spanning Tree Problem

In graph theory, the k-minimum spanning tree problem is considered to be one of the well-known NP hard problems to solve. This paper address this problem by proposing several hybrid approximate approaches based on the combination of simulated annealing, tabu search and ant colony optimization algorithms. The performances of the proposed methods are compared to other approaches from the literature using the same well-known library of benchmark instances.

A novel edge server selection method based on combined genetic algorithm and simulated annealing algorithm

ABSTRACT Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user’s overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms.

An energy‐aware method for task allocation in the Internet of things using a hybrid optimization algorithm

SummaryInternet of Things (IoT) is utilized as an emerging sample for defining the future of technology in which physical items like sensors, radio‐frequency identification tags, mobile phones, actuators, and so on, can have interaction together and have cooperation with their neighbors for obtaining joint objectives. The performance of the deployed tasks and applications on the network is considered as one of the critical goals in this model, which is achieved by the task allocation mechanism. Task allocation in the IoT is so complicated due to the intricate connection among machines. The task allocation problem is considered as an NP‐hard problem, so a new task allocation algorithm in the IoT environment is proposed using the combination of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithm. Also, the issues of the PSO algorithm, such as getting stuck in local optimization and not achieving an optimal response, forced us to present a method based on the combination of SA and the PSO algorithms. The results of simulation in MATLAB environment illustrated that the suggested method performs better compared to the PSO and SA‐based methods.

A combined neural network and simulated annealing based inverse technique to optimize the heat source control parameters in heat treatment furnaces

This study proposes the use of artificial neural network (ANN) based surrogate framework along with simulated annealing (SA) approach to inversely estimate the optimum values of heat source control parameters in a heat treatment furnace. In particular, a two-dimensional radiant furnace with gas fired heaters has been considered and the heat source control parameters for a general gaussian heating profile are estimated to achieve better heat flux uniformity at the specimen. To expedite the forward radiative transfer calculations, ANN based surrogate is developed and coupled with SA. The maximum difference in radiative transfer solution and ANN is found to be less than 6%. Results indicate that the uniformity of fluxes is largely dependent on the emissivity of the specimen and its overall length, the dependence on specimen temperature and gas concentration is minimal. Cross validation of optimum heating profiles with radiative transfer solver shows an excellent match in local heat flux predictions. Overall, combined ANN-SA based algorithm proves to be an accurate and fast tool in heat source control parameter optimization problem.