The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in...
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The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this article, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this article consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.
This paper proposed three different Raman optical amplifier architectures that are designed and investigated for 50 x 100 Gbps dense wavelength division multiplexed (DWDM) system at channel spacing of 0.8 nm. The perf...
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This paper proposed three different Raman optical amplifier architectures that are designed and investigated for 50 x 100 Gbps dense wavelength division multiplexed (DWDM) system at channel spacing of 0.8 nm. The performance is determined and compared in terms of gain, gain ripple, noise figure (NF), Q-factor and bit error rate (BER) in the C-band at wavelengths in the vicinity of 1550 nm. Pump powers of the Raman amplifier are selected using multiparameter optimization algorithm to achieve maximum gain with small ripple. The effects of varying input powers on gain, gain ripple and NF are also investigated. Raman amplifier with bidirectional pumping configuration achieves highest average gain of 21.42 dB with lowest gain ripple of 1.24 dB. Average gain of 21.37 dB with gain ripple of 1.55 dB is obtained for forward pumping configuration. For backward pumping configuration, an average gain of 21.32 dB is obtained. All three configurations achieved a good quality factor (> 12.0 dB) at each wavelength. The utility of these Raman optical amplifier architectures in long-distance DWDM systems is validated by the obtained high gain, improved gain flatness, good Q-factor, and low BER.
Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to in...
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Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.
In this article, a new metaheuristic optimization algorithm (named as, student psychology based optimization (SPBO)) is proposed. The proposed SPBO algorithm is based on the psychology of the students who are trying t...
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In this article, a new metaheuristic optimization algorithm (named as, student psychology based optimization (SPBO)) is proposed. The proposed SPBO algorithm is based on the psychology of the students who are trying to give more effort to improve their performance in the examination up to the level for becoming the best student in the class. Performance of the proposed SPBO is analyzed while applying the algorithm to solve thirteen 50 dimensional benchmark functions as well as fifteen CEC 2015 benchmark problems. Results of the SPBO is compared to the performance of ten other state-of-the-art optimization algorithms such as particle swarm optimization, teaching learning based optimization, cuckoo search algorithm, symbiotic organism search, covariant matrix adaptation with evolution strategy, success-history based adaptive differential evolution, grey wolf optimization, butterfly optimization algorithm, poor and rich optimization algorithm, and barnacles mating optimizer. For fair analysis, performances of all these algorithms are analyzed based on the optimum results obtained as well as based on convergence mobility of the objective function. Pairwise and multiple comparisons are performed to analyze the statistical performance of the proposed method. From this study, it may be established that the proposed SPBO works very well in all the studied test cases and it is able to obtain an optimum solution with faster convergence mobility.
Reducing the mismatch loss to increase the output power of the photovoltaic (PV) array is crucial for extending the flight time of stratospheric airships. This paper presents a reconfiguration system for PV arrays bas...
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Reducing the mismatch loss to increase the output power of the photovoltaic (PV) array is crucial for extending the flight time of stratospheric airships. This paper presents a reconfiguration system for PV arrays based on a switch matrix designed for stratospheric airships. The proposed system employs a multilevel optimization reconfiguration algorithm that combines smart choice, greedy, and Munkres' assignment algorithms. Simulations were conducted under single working conditions, full-day sunlight cycles, and full-year PV array reconfigurations, respectively. The results demonstrated that the reconfigured PV array significantly improved the output power with a smooth P-V curve. The instantaneous power under extreme working conditions could be increased by 50.1%. Furthermore, during the 7-day simulation process, the average daily power output of the PV array increased by 14.68%, whereas the output fluctuation during circular cruising was reduced. The reconfiguration system offers greater advantages during months with weak irradiance in high-latitude regions, where the daily output power of the PV array can be increased by up to 24.46%. This significantly reduces the installation area and weight ratio of a stratospheric airship PV array.
Identification procedure of concrete constitutive model is proposed according to measured system dynamic responses. The inverse problem of material characterization is formulated as parameter identification. A set of ...
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Identification procedure of concrete constitutive model is proposed according to measured system dynamic responses. The inverse problem of material characterization is formulated as parameter identification. A set of parameters corresponding to the material property can be found by minimizing objective function which accounts for experimental data and the calculated response of the mechanical model. The performances of the proposed optimization algorithm were investigated with simulating data, and the effectiveness was consequently confirmed. Numerical simulation results show that the proposed identification has good robustness and high estimation precision.
Recent breakthroughs in quantum processing software and hardware have shown substantial development toward operational results. We anticipate quantum computing to have significant applications in some domains ranging ...
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Recent breakthroughs in quantum processing software and hardware have shown substantial development toward operational results. We anticipate quantum computing to have significant applications in some domains ranging from machine learning and optimization to drug development in the future. Quantum computing and designing have gotten a lot of interest from investigators. Physical design is one of the two main stages in designing nano-scale circuits that receive a list of circuit nodes as input and produce the final arrangement in a particular technology. However, it has encountered several design issues and execution limits in the long term. Moreover, designing nano-scale circuits is an NP-hard problem in terms of cost, delay, and energy consumption. This paper has provided a new method for solving the optimal design of nano-scale circuits using a fuzzy-based Fish Swarm algorithm (FSA). The FSA represents fish, hunting, and fish behavior to get the best results. It has a quick integration speed, a powerful global search capacity, and a significant shortcoming. In our proposed method, the fuzzy observer is responsible for detecting the system's state within the defined area and maintaining the state within it. The proposed method has been evaluated against some benchmarks, and cost, delay, energy, and area characteristics have been reduced. Simulation outcomes demonstrated the suggested method's correct performance in terms of "energy," "cost", "area", and "delay" where the suggested technique outperforms others (GA, PSO, FSA and ACO).
Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimizat...
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Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork optimization algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.
With the rapid development of economy and science and technology in our country, the types and quantities of various optimization problems have increased in large scale, and the optimization of the model structure can...
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With the rapid development of economy and science and technology in our country, the types and quantities of various optimization problems have increased in large scale, and the optimization of the model structure can improve the efficiency. Based on this, the group intelligent optimization algorithm and its evaluation were studied. The intelligent homogenization partition was introduced first, and then the calculation of the cluster intelligent alignment probabilities was introduced, and the optimization process of the last group intelligent optimization algorithm was described in detail. The test results show that the optimization model of group intelligent optimization algorithm has more realistic and reasonable application.
Implementing real-time prediction and warning systems is an effective approach for mitigating flash flood disasters. However, there is still a challenge in improving the accuracy and reliability of flood prediction mo...
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Implementing real-time prediction and warning systems is an effective approach for mitigating flash flood disasters. However, there is still a challenge in improving the accuracy and reliability of flood prediction models. This study develops a hydrological prediction model named SCE-GUH, which combines the Shuffled Complex Evolution-University of Arizona optimization algorithm with the general unit hydrograph routing method. Our aims were to investigate the applicability of the general unit hydrograph in runoff calculations and its performance in predicting flash flood events. Furthermore, we examined the influence of parameter variations in the general unit hydrograph on flood simulations and conducted a comparative analysis with the conventional Nash unit hydrograph. The research findings demonstrate that the utilization of the general unit hydrograph method can considerably decrease computational errors and enhance prediction accuracy. The flood peak detection rate was found to be 100% in all four study watersheds. The average Nash-Sutcliffe efficiency coefficients were 0.83, 0.83, 0.84, and 0.87, while the corresponding coefficients of determination were 0.86, 0.85, 0.86, and 0.94, and the absolute errors of peak present time were 0.19 h, 0.40 h, 0.91 h, and 0.82 h, respectively. Moreover, the utilization of the general unit hydrograph method was found to significantly reduce the peak-to-current time difference, thereby enhancing simulation accuracy. Parameter variations have a substantial influence on peak flow characteristics. The SCE-GUH model, which incorporates the topographic and geomorphological features of the watershed along with the optimization algorithm, is capable of effectively characterizing the catchment properties of the watershed and offers valuable insights for enhancing the early warning and prediction of hydrological forecasting.
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