In view of the deficiency of genetic algorithm in local search ability, a simulated annealing algorithm for optimization of physical distribution routing problem is constructed. Compared with the ...traditional optimization algorithm, the hybrid genetic algorithm has faster convergence speed, better distribution result and better application value.
In recent years, the share of renewable energy sources in current energy production has been increasing due to the depletion of fossil fuel resources, increasing energy needs and environmental ...concerns. Solar energy, one of the most important renewable energy sources depending on being clean, sustainable and environmentally friendly energy source. Understanding the solar radiation value is crucial for maximizing the potential of solar energy and ensuring the efficient operation of solar energy systems. In this paper, it is aimed to develop a new global solar radiation (GSR) prediction model by using simulated annealing algorithm (SAA). Modeling solar radiation data with significant variability is a challenging task that necessitates the use of nonlinear approaches. Although the SAA is widely used in engineering and natural sciences, it has not previously been used to develop a GSR prediction model. While developing the SAA forecast model for the Adana region, long-term sunshine duration and solar radiation data obtained from the Turkish General Directorate of State Meteorology were used, as well as geographical features such as latitude and longitude of the selected region. The performance and applicability of the proposed model was examined by comparing it with different GSR estimation methods developed in the literature. The primary contribution of this study lies in the introduction of an inventive model, which has been constructed for the first time in the assessment of GSR, to the current collection of literature. The produced results were statistically compared with the observed data using six separate performance and error measures. The results of all years showed that the relative percentage error (RPE) less than 11.66 %, the mean percentage error (MPE) does not exceed 12.55 %, the correlation coefficient (R2) greater than 0.98, the mean absolute percentage error (MAPE) test results appear to vary close to the value 10, the sum of squared relative error (SSRE) test results seem to approaching 0 and the t-statistic test less than 4.06 for the annual GSR. It has been observed that the developed SAA GSR forecasting model has a successful performance when compared to different forecasting models popularly used in the literature. According to the statistical error results, the estimated GSR data from the novel SAA-GSR forecast model aligns well with the measured meteorological values. Given the efficacy of the established SAA model in GSR estimation, this study is anticipated to offer valuable insights and contributions to the application of the SAA approach in other energy applications.
As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In order to balance production and green ...sustainable development, this paper puts forward man–machine dual resource constraint mechanism. In addition, a multi-objective flexible job shop scheduling problem model constrained by job transportation time and learning effect is constructed, and the goal is to minimize processing time energy consumption and noise. Subsequently, a hybrid discrete multi-objective imperial competition algorithm (HDMICA) is developed to solve the model. The global search mechanism based on the HDMICA improves two aspects: a new initialization method to improve the quality of the initial population, and the empire selection method based on Pareto non-dominated solution to balance the empire forces. Then, the improved simulated annealing algorithm is embedded in imperial competition algorithm (ICA), which overcomes the premature convergence problem of ICA. Therefore, four neighborhood structures are designed to help the algorithm jump out of the local optimal solution. Finally, an example is used to verify the feasibility of the proposed algorithm. By comparing with the original ICA and other four algorithms, the effectiveness of the proposed algorithm in the quality of the first frontier solution is verified.
•This paper jointly considers the vehicle routing and loading.•The versions with item rotation are studied, and the benefits are investigated.•A new open space based packing method is proposed.•An ...efficient data structure to record the loading information is developed.•Simulated annealing with repeatedly cooling and rising of temperature is proposed.
This paper studies the well-known capacitated vehicle routing problem with two-dimensional loading constraints (2L-CVRP). It requires designing a set of min-cost routes, starting and terminating at the central depot, to satisfy customer demands which involve a set of two-dimensional, rectangular, weighted items. A simulated annealing algorithm with a mechanism of repeatedly cooling and rising the temperature is proposed to solve the four versions of this problem, with or without the LIFO constraint, and allowing rotation of goods or not. An open space based heuristic is employed to identify the feasible loading patterns. In addition, the data structure Trie is used to accelerate the procedure by keeping track of the packing feasibility information of routes examined, and also by controlling the effort spent on different routes. The proposed algorithm is tested on the widely used instances of 2L-CVRP. The results show that our approach outperforms all existing algorithms on the four problem versions, and reaches or improves the best-known solutions for most instances. Furthermore, we compared the impact of different loading constraints, and observed some interesting results.
and accurate prediction of clean energy can supply an important reference for governments to formulate social and economic development policies. This paper begins with the logistic equation which is ...the whitening equation of the Verhulst model, introduces the Riccati equation with constant coefficients to optimize the whitening equation, and establishes a grey prediction model (CCRGM(1,1)) based on the Riccati equation. This model organically combines the characteristics of the grey model, and flexibly improves the modelling precision. Furthermore, the nonlinear term is optimized by the simulated annealing algorithm. To illustrate the validation of the new model, two kinds of clean energy consumption in the actual area are selected as the research objects. Compared with six other grey prediction models, CCRGM(1,1) model has the highest accuracy in simulation and prediction. Finally, this model is used to predict the nuclear and hydroelectricity energy consumption in North America from 2019 to 2028. The results predict that nuclear energy consumption will keep rising in the next decade, while hydroelectricity energy consumption will rise to a peak and subsequently fall back, which offers important information for the governments of North America to formulate energy measures.
•The novel grey Verhulst model CCRGM(1,1) is proposed.•The optimal nonlinear terms of the novel model are determined by simulated annealing algorithm.•The comparative studies show that the new model is superior to the other six benchmark models.•The nuclear and hydroelectricity energy consumptions of North America from 2019 to 2028 are projected.
Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated ...promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test (<inline-formula> <tex-math notation="LaTeX">P </tex-math></inline-formula>-value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.
It is well known that radome has adverse effects on the performance of the antenna array while protecting it from outside environments. In some extreme cases, e.g. when an airborne radome is ablated ...during the flight of a high-speed vehicle, the impaired radome will severely alter the radiation pattern (RP) of the antenna array. To counteract this effect, it is desirable that the RP of the array can be recovered to satisfy the preset criteria even in presence of the impaired radome. In this letter, we investigate the method of fast recovery of the RP of an enclosed antenna array when the dielectric radome is damaged. An improved simulated annealing (ISA) algorithm is combined with the active RPs (ARP) of antenna elements in the array- radome system (ARS) to fast restore the RP of the array. Both the mutual coupling among array elements and the interactions between the array and impaired radome are taken into account rigorously, so the RP of the array can be restored with good accuracy. The effectiveness of the method is validated by the numerical and experimental results for the RP recovery of a real-life linear antenna array enclosed by an impaired radome.
Tailings dams are usually ponds bounded by valleys or surrounding topography to store mining or other chemical industrial waste. On 25 January 2019, the collapse of a tailings dam at the Córrego do ...Feijao iron ore mine (Brumadinho, Minas Gerais, Brazil) released about 12 million m3 of tailings, killing over 240 people and posing a considerable and ongoing environmental threat. The stability of tailings dam monitoring is very important and in the present paper, a new InSAR (Synthetic Aperture Radar Interferometry) time series approach is proposed to derive ground displacement maps for use in dam safety monitoring. Compared with the other solutions, the unique feature of the proposed method is that: 1) the new Measurement Pixel (MP) selection criteria has the potential to include relatively more accurate MP pixels and build a more robust network, 2) the multi-level grading system makes it possible to add the MP pixels into the main network step-by-step with external control, and 3) the computing efficiency can be improved by strategically reducing the iteration times. The proposed approach was tested on both simulated and real data. Results show that the Simulated Annealing (SA) method normally has a more accurate estimation as compared to the Quasi-Newton (QN) method, despite its longer processing time. Detailed analysis of the displacement maps was conducted to determine the subsidence processes that result from dam construction.
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•Risk assessment for tailings dams in Brumadinho of Brazil using InSAR time series approach•The proposed multi-level grading system can select the measurement pixels step-by-step with control.•The proposed approach was tested on both simulated and real data.•Stability of surrounding infrastructures near the tailings dam is evaluated.•A comparison was conducted between InSAR time series and land type map.
•An efficient multi-objective algorithm based on SA is presented to solve MORAP.•The algorithm called knowledge-based archive MOSA (KBAMOSA) algorithm.•KBAMOSA used a memory matrix to reinforce the ...neighborhood structure.•KBAMOSA algorithm dominated the solutions obtained by NSGA-II.•KBAMOSA is superior to AMOSA algorithm based on standard metrics.
Redundancy allocation problem (RAP) is one of the best-developed problems in reliability engineering studies. This problem follows to optimize the reliability of a system containing s sub-systems under different constraints, including cost, weight, and volume restrictions using redundant components for each sub-system. Various solving methodologies have been used to optimize this problem, including exact, heuristic, and meta-heuristic algorithms. In this paper, an efficient multi-objective meta-heuristic algorithm based on simulated annealing (SA) is developed to solve multi-objective RAP (MORAP). This algorithm is knowledge-based archive multi-objective simulated annealing (KBAMOSA). KBAMOSA applies a memory matrix to reinforce the neighborhood structure to achieve better quality solutions. The results analysis and comparisons demonstrate the performance of the proposed algorithm for solving MORAP.
•A new selective maintenance for systems executing multiple missions is developed.•The uncertainty associated with the effective ages of components is quantified.•The multiple integrals are resolved ...by the Gaussian quadrature and Riemann sum.•A customized SAGA with two solution-improving algorithms are proposed.
Selective maintenance is extensively implemented in industrial and military environments. With it, a subset of feasible maintenance actions may be strategically selected for a repairable system to achieve maximum success in subsequent missions with limited maintenance resources. In this study, a new selective maintenance model for systems that execute multiple consecutive missions is developed. In each break, multiple optional maintenance actions, from perfect replacements down to imperfect and minimal repairs, can be chosen for each component. Because of the uncertainties associated with the operation time of each component and the durations of future missions, the effective age of each component at the beginning of the next mission is also uncertain, posing a new challenge in terms of evaluating the success of subsequent missions. Such uncertainties are quantified by evaluating the probabilities of a system in successfully completing future missions. The computational burden resulting from the use of multi-dimensional integrals is alleviated with the introduction of the Gaussian quadrature and Riemann sum. Consequently, the selective maintenance problem is formulated as a max-min optimization model. Moreover, the simulated annealing-based genetic algorithm is customized to solve the resulting optimization problem. Two illustrative examples are presented to demonstrate the advantages of the proposed approach.
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