Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data...
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Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classifica-tion accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern optimization algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization.
In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the ...
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In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO's performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO's exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.
Dams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water...
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Dams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water resources. In areas where water supply exhibits significant temporal variation when compared with the demand, the challenge is to bridge the gap and achieve an optimal match between the water supply and demand patterns. Therefore, the release of water from reservoirs should be controlled to ensure that the operation rule for the available water storage in the reservoir is optimized to satisfy the future water demands. This level of optimal control can only be achieved using an efficient optimization algorithm to optimally derive the operation rule for such a complex water system. Herein, two main methods have been considered to tackle this water resource management problem. First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. In addition, two different optimization algorithms, namely crow search algorithm and master-slave algorithm, have been introduced to generate an optimal rule for water release policy. Further, the proposed optimization algorithms have been applied to one of the most critical dam and reservoir water systems, namely the Aswan High Dam (AHD), which controls almost 95% of Egypt's water resources. The current operation of AHD using the existing optimization rules resulted in a mismatch between the water supply and water demand. In other words, the water availability could be higher than the water demand during a certain period, whereas it could be less than the water demand during another period. The results denoted that the master-slave algorithm outperforms the remaining algorithms and generates an optimization rule that minimizes the mismatch between the water supply and water demand.
During the exercise process, such as jumping, and running, the angles of different joints of the athlete's body will produce different changes, resulting in obvious mismatches in different joint feature points. Th...
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During the exercise process, such as jumping, and running, the angles of different joints of the athlete's body will produce different changes, resulting in obvious mismatches in different joint feature points. The chaotic matching error makes the calculation results of the basic shape features of the athletes in different parts have large deviations. In this paper, an improved algorithm of CEPS (Chaos embedded particle swarm) optimization algorithm interpolation is introduced, which can improve the interpolation precision and reduce the calculation time of interpolation. Through interpolation, the fast Fourier transform can be used to realize the fast reconstruction of the target 3d image based on biomechanical characteristics. The experimental and simulation results show that the three-dimensional image reconstruction algorithm based on the interpolation method can reconstruct the three-dimensional image of the target, achieve the detection of the target and have good resolution, and improve the authenticity of the human motion image sequence three-dimensional dynamic simulation. (C) 2020 Elsevier B.V. All rights reserved.
The insulated core transformer (ICT) power supply is widely employed in electron beam accelerators (EBAs) due to its high power, heightened efficiency, and stable operation. However, the segmented-core structure of th...
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The insulated core transformer (ICT) power supply is widely employed in electron beam accelerators (EBAs) due to its high power, heightened efficiency, and stable operation. However, the segmented-core structure of the ICT power supply increases magnetic leakage, which leads to it adversely affecting the consistency of the output voltages in the rectifier stages. Currently, numerous studies focus on stage voltage compensation, including turns compensation, capacitor compensation, dummy primary winding compensation, and full-parameter compensation. This paper presents a unified simulation model and an improved gradient-based genetic algorithm, which can also optimize the parameters of the four compensation methods. Based on this, the performance of the power supply using the four compensation methods under different ICT energy levels and power supply requirements is studied, and the selection suggestions are given. This work fills the gap in the performance comparison and application research of various compensation methods.
Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and ...
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Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (dFDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the dFDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (dFDBARO) algorithm. Moreover, the practicability of the proposed dFDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed dFDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the dFDBARO algorithm is available at https://***/matlabcentral/filee xchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm.
An optical transparent metasurface for wideband backward scattering reduction with a synthetic optimization method is proposed, which makes full use of the simultaneity of electromagnetic absorption and interference s...
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An optical transparent metasurface for wideband backward scattering reduction with a synthetic optimization method is proposed, which makes full use of the simultaneity of electromagnetic absorption and interference suppression, so that backward scattering can be flexibly manipulated. To validate the design procedure, a metasurface with optical transparency is designed, fabricated and experimentally tested, which significantly enhances the efficiency in the frequency range of 6.2-20.8 GHz and exhibits about 79.5% averaged transmittance of optical transparency in the wavelength range of 380-780 nm. The proposed optical transparent wideband backward scattering reduction metasurface reveals an alternative opportunity for effective manipulation of microwaves.
Three-dimensional path planning for autonomous underwater vehicles (AUVs) in underwater environments is the key to ensuring safe navigation and reliable mission completion. To obtain a safe and smooth three-dimensiona...
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Three-dimensional path planning for autonomous underwater vehicles (AUVs) in underwater environments is the key to ensuring safe navigation and reliable mission completion. To obtain a safe and smooth three-dimensional path for an AUV in ocean currents and seabed obstacle environments, an improved compression factor particle swarm optimization algorithm is proposed. First, a three-dimensional seabed environment model and Lamb vortex current environment model are constructed. Second, by considering optimization objectives such as travel distance cost, seabed terrain constraints and ocean current constraints, a three-dimensional path planning mathematical model is constructed. Finally, an improved compression factor particle swarm optimization algorithm is proposed and applied to solve the multi-objective nonlinear optimization problem. To verify the optimization performance of the new algorithm, its optimization results are compared with those of other algorithms by minimizing the fitness value. The experimental results reveal that the improved compressed factor particle swarm optimal algorithm has better planning efficiency, path quality, and shorter planning time, which provides a new effective method for path planning of autonomous underwater vehicle.
Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance re...
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Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.
A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and...
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A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals;these included real and artificial noise. For comparison purposes, both model and non-model-based methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.
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