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Two-Dimensional CS Adaptive FIR Wiener Filtering Algorithm for the Denoising of Satellite Images

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第12期10卷 5245-5257页

作者： Suresh, Shilpa Lal, Shyam Natl Inst Technol Karnataka Dept Elect & Commun Engn Mangaluru 575025 India

In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adaptive finite-impulse response (FIR) filters driven by an optimization algorithm to self-adjust the filter coefficients, with applications in different domains of research. For signal processing applications, FIR Wiener filters are commonly used for noisy signal restorations by computing the statistical estimates of the unknown signal. In this paper, a novel 2D-Cuckoo search adaptive Wiener filtering algorithm (2D-CSAWF) is proposed for the denoising of satellite images contaminated with Gaussian noise. Till date, study based on 2-D adaptive Wiener filtering driven by metaheuristic algorithms was not found in the literature to the best of our knowledge. Comparisons are made with the most studied and recent 2-D adaptive noise filtering algorithms, so as to analyze the performance and computational efficiency of the proposed algorithm. We have also included comparisons with recent adaptive metaheuristic algorithms used for satellite image denoising to ensure a fair comparison. All the algorithms are tested on the same satellite image dataset, for denoising images corrupted with three different Gaussian noise variance levels. The experimental results reveal that the proposed novel 2D-CSAWF algorithm outperforms others both quantitatively and qualitatively. Investigations were also carried out to examine the stability and computational efficiency of the proposed algorithm in denoising satellite images.

关键词： Adaptive filter algorithm cuckoo search (CS) algorithm metaheuristic optimization algorithms satellite image denoising two-dimensional finite-impulse response (2-D FIR) Wiener filter

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An improvement on the Migrating Birds optimization with a problem-specific neighboring function for the multi-objective task allocation problem

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EXPERT SYSTEMS WITH APPLICATIONS 2017年第0期67卷 304-311页

作者： Oz, Dindar Yasar Univ Software Engn Dept TR-35100 Izmir Turkey

Allocating tasks to processors is a well-known NP-Hard problem in distributed computing systems. Due to the lack of practicable exact solutions, it has been attracted by the researchers working on heuristic based suboptimal search algorithms. With the recent inclusion of multiple objectives such as minimizing the cost, maximizing, the throughput and maximizing the reliability, the problem gets even more complex and an efficient approximate method becomes more valuable. In this work, I propose a new solution for the multi-objective task allocation problem. My solution consists in designing a problem-specific neighboring function for an existing metaheuristic algorithm that is proven to be successful in quadratic assignment problems. The neighboring function, namely greedy reassignment with maximum release (GR-MR), provides a dynamic mechanism to switch the preference of the search between the exploration and exploitation. The experiments validate both that the quality of the solutions are close to the optimal and the proposed method performs significantly better comparing to three other metaheuristic algorithms. Neighboring functions being the common reusable components of metaheuristic algorithms, GR-MR can also be utilized by other metaheuristic-based solutions in the future. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distributed systems Task allocation problem metaheuristic optimization algorithms Stochastic search algorithms

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metaheuristic algorithms for the design of multiplier-less non-uniform filter banks based on frequency response masking

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SOFT COMPUTING 2014年第8期18卷 1529-1547页

作者： Bindiya, T. S. Elias, Elizabeth Natl Inst Technol Calicut Dept Elect & Commun Engn Calicut 673601 Kerala India

In this paper, multiplier-less nearly perfect reconstruction tree structured non-uniform filter banks (NUFB) are proposed. When sharp transition width filter banks are to be implemented, the order of the filters and hence the complexity will become very high. The filter banks employ an iterative algorithm which adjusts the cut off frequencies of the prototype filter, to reduce the amplitude distortion. It is found that the proposed design method, in which the prototype filter is designed by the frequency response masking method, gives better results when compared to the earlier reported results, in terms of the number of multipliers when sharp transition width filter banks are needed. To reduce the complexity and power consumption for hardware realization, a design method which makes the NUFB totally multiplier-less is also proposed in this paper. The NUFB is made multiplier-less by converting the continuous filter bank coefficients to finite precision coefficients in the signed power of two space. The filter bank with finite precision coefficients may lead to performance degradation. This calls for the use of suitable optimization techniques. The classical gradient based optimization techniques cannot be deployed here, because the search space consists of only integers. In this context, meta-heuristic algorithm is a good choice as it can be tailor made to suit the problem under consideration. Thus, this design method results in near perfect NUFBs which are simple and multiplier-less and have linear phase and sharp transition width with very low aliasing. Also, different non-uniform bands can be obtained from the tree structured filter bank by rearranging the branches.

关键词： Tree structured filter banks Frequency response masking Canonic signed digit metaheuristic optimization algorithms Genetic algorithm Differential evolution Artificial bee colony algorithm Gravitational search algorithm Harmony search algorithm

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Seismic energy dissipation-based optimum design of tuned mass dampers

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2018年第6期58卷 2517-2531页

作者： Bilondi, Mohammad Reza Shayesteh Yazdani, Hessam Khatibinia, Mohsen Univ Birjand Dept Civil Engn Birjand Iran Howard Univ Dept Civil & Environm Engn Washington DC 20059 USA

Tuned mass dampers (TMDs) are a subclass of dynamic vibration absorbers that consist of a mass-spring-damper unit that is attached to a structure to adjust its response to seismic and wind loads. The efficacy, performance and optimum design of a TMD strongly depend not only on its mass, stiffness and damping as well as the input energy and the structure characteristics, but also on the structural response parameter(s) that the TMD is intended to mitigate. In that respect, this study evaluates the suitability of four objective functions for the optimum design of the TMD of an inelastic, steel moment-resisting frame (SMRF) under an artificial, white-noise excitation. The objective functions include 1) the maximum roof lateral displacement, 2) the maximum drift, 3) the root mean square of drifts and 4) the cumulative hysteretic energy of the SMRF. The results indicate that the SMRF equipped with a TMD optimized using the cumulative hysteretic energy of the SMRF as the objective function exhibits the best seismic response under the artificial earthquake. Further examining the response of the TMD-equipped SMRF under four historic earthquake records shows that equipping a structure with a TMD optimized using an artificial earthquake will not warrant that the structure will exhibit a better seismic performance in all measures compared with when no TMD is used. Put other way, while the minimization of cumulative hysteretic energy could be the best objective function for a case subjected to an artificial earthquake, under real earthquakes, none of the objective functions consistently results in a better seismic performance. This behavior is attributed to detuning effects arising from major structural damages and significant period shifts that occur during strong earthquakes.

关键词： Earthquake protective systems Viscoelastic damper Dynamic vibration absorbers metaheuristic optimization algorithms

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Performance of Turbulent Flow of Water optimization on Economic Load Dispatch Problem

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IEEE ACCESS 2021年 9卷 77882-77893页

作者： Deb, Sanchari Houssein, Essam H. Said, Mokhtar Abdelminaam, Diaa Salama VTT Tech Res Ctr Espoo 02100 Finland Minia Univ Fac Comp & Informat Al Minya 61519 Egypt Fayoum Univ Elect Engn Dept Fac Engn Faiyum 63514 Egypt Benha Univ Fac Comp & Artifcial Intelligence Banha 12311 Egypt Misr Int Univ Fac Comp Sci Cairo 611310 Egypt

The economic load dispatch (ELD) problems considering nonlinear characteristics where an optimal combination of power generating units is selected in order to minimize the total cost by economic allocation of power produced and the emission cost. As a consequence, optimal allocation is performed by considering both fuel cost and emission leading to Combined Economic and Emission Dispatch (CEED). This study presents a new Meta-heuristic algorithms (MHs) called the Turbulent Flow of Water optimization (TFWO), which is based on the behaviour of whirlpools created in turbulent water flow, for solving different variants of ELD and CEED. To verify the robustness of the TFWO, various test network of CEED with effect of valve, and ELD with losses of transmission are incorporated. In comparison with seven well-known MHs such as Cuckoo Search Algorithm (CSA), Grey Wolf Algorithm (GW), Sine Cosine Algorithm (SCA), Earth Worm optimization Algorithm (EWA), Tunicate Swarm Algorithm (TSA), Moth Search Algorithm (MSA) and Teaching Learning Based optimization (TLBO), the TFWO provides the minimum fuel cost and significantly robust solutions of ELD problem over all tested networks. The results confirm the potential and effectiveness of the GWO to be a promising technique to solve various ELD problems.

关键词： optimization Economics Fuels Valves Heuristic algorithms Search problems Generators Turbulent flow of water optimization (TFWO) economic load dispatch (ELD) combined economic and emission dispatch (CEED) metaheuristic optimization algorithms

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An efficient equilibrium optimizer with support vector regression for stock market prediction

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NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 3165-3200页

作者： Houssein, Essam H. Dirar, Mahmoud Abualigah, Laith Mohamed, Waleed M. Minia Univ Fac Comp & Informat Al Minya 61519 Egypt Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Pulau Pinang Malaysia

A hybridized method that relies on using the support vector regression (SVR) method with equilibrium optimizer (EO) is proposed to foresee the closing prices of Egyptian Exchange (EGX). Three indices are modeled and employed: EGX 30, EGX 30 capped, and EGX 50 EWI. The efficiency of using the technical indicators and statistical measures in the forecasting process is evaluated. The proposed EO-SVR-based forecasting model is adopted and evaluated using mean absolute percentage error, average, standard deviation, best fit, worst fit, and CPU time. Also, it is compared with recently developed metaheuristic optimization algorithms published in the literature such as whale optimization algorithm, salp swarm algorithm, Harris Hawks optimization, gray wolf optimizer, Henry gas solubility optimization, Barnacles mating optimizer, Manta ray foraging optimization, and slime mold algorithm. The proposed EO-SVR model got better results than other the counterparts, and EO-SVR is considered the optimal model according to its superior outcomes. Moreover, there is no need to use technical indicators and statistical measures as their effect is not noticeable.

关键词： Equilibrium optimizer (EO) Support vector regression (SVR) Stock price prediction metaheuristic optimization algorithms Technical indicators

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Swarm-based hybridizations of neural network for predicting the concrete strength

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SMART STRUCTURES AND SYSTEMS 2020年第2期26卷 241-251页

作者： Ma, Xinyan Foong, Loke Kok Morasaei, Armin Ghabussi, Aria Lyu, Zongjie China Airport Planning & Design Inst Co Ltd Beijing 100101 Peoples R China Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam KN Toosi Univ Technol Dept Civil Engn Tehran Iran Islamic Azad Univ Dept Civil Engn Cent Tehran Branch Tehran Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn Da Nang 550000 Vietnam

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

关键词： concrete compressive strength neural computing metaheuristic optimization algorithms

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Multispectral Satellite Image Denoising via Adaptive Cuckoo Search-Based Wiener Filter

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第8期56卷 4334-4345页

作者： Suresh, Shilpa Lal, Shyam Chen, Chen Celik, Turgay Natl Inst Technol Karnataka Dept Elect & Commun Engn Mangalore 575025 India Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Witwatersrand Sch Comp Sci & Appl Math ZA-2000 Johannesburg South Africa Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China

Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well.

关键词： 2-D finite-impulse response (FIR) Wiener filter adaptive cuckoo search (ACS) algorithm metaheuristic optimization algorithms satellite image denoising

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Optimal sizing and techno-economic analysis of energy- and cost-efficient standalone multi-carrier microgrid

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ENERGY 2019年 178卷 751-764页

作者： Lorestani, Alireza Gharehpetian, G. B. Nazari, Mohammad Hassan Amirkabir Univ Technol Dept Elect Engn Ctr Excellence Power Syst Tehran Iran McMaster Univ Dept Mech Engn Integrated Community Energy & Harvesting Res Lab Hamilton ON Canada

The objective of this study is optimal sizing and techno-economic analysis of an energy- and costefficient standalone multi-carrier microgrid (SMCMG). This SMCMG is with the integration of photovoltaic-thermal (PVT), wind turbine (WT), micro-turbine (MT), electrical energy storage (EES), thermal energy storage (TES), and backup natural gas boiler (GB) units. For this aim, a long-term planning approach is put forward, taking into account battery degradation and reliability considerations. Total annual cost (TAC) is served as the objective function of the optimization problem, which covers investment, replacement, fuel, and operation and maintenance costs. A rule-based energy management system (EMS), which prioritizes the application of renewable energy (RE) resources, is introduced to administrate the SMCMG operation and maximize RE utilization. To solve the optimization problem, a robust metaheuristic optimization algorithm, called evolutionary particle swarm optimization (E-PSO), is employed, and the results are validated. The simulations are conducted based on real data from Rafsanjan, Iran. The superiority of the E-PSO algorithm is demonstrated. Various techno-economic analyses are conducted, and different configurations for SMCMG are studied. The results signify the competence of the proposed configuration for SMCMG from economic, energy efficiency, reliability, and environmental points of view. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Multi-carrier microgrid CHP system Renewable energy metaheuristic optimization algorithms

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A new integrated intelligent computing paradigm for predicting joints shear strength

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Geoscience Frontiers 2024年第6期15卷 176-193页

作者： Shijie Xie Zheyuan Jiang Hang Lin Tianxing Ma Kang Peng Hongwei Liu Baohua Liu School of Resources and Safety Engineering Central South UniversityChangsha 410083HunanChina Jiangsu Key Laboratory of Urban Underground Engineering and Environmental Safety Institute of Geotechnical EngineeringSoutheast UniversityNanjing 210096JiangsuChina Ocean College Zhejiang UniversityZhoushan 316021China

Joints shear strength is a critical parameter during the design and construction of geotechnical engineering *** prevailing models mostly adopt the form of empirical functions,employing mathematical regression techniques to represent experimental *** an alternative approach,this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear *** metaheuristic optimization algorithms,including the chameleon swarm algorithm(CSA),slime mold algorithm,transient search optimization algorithm,equilibrium optimizer and social network search algorithm,were employed to enhance the performance of the multilayered perception(MLP)*** comparisons were conducted between the proposed CSA-MLP model and twelve classical models,employing statistical indicators such as root mean square error(RMSE),correlation coefficient(R2),mean absolute error(MAE),and variance accounted for(VAF)to evaluate the performance of each *** sensitivity analysis of parameters that impact joints shear strength was ***,the feasibility and limitations of this study were *** results revealed that,in comparison to other models,the CSA-MLP model exhibited the most appropriate performance in terms of R2(0.88),RMSE(0.19),MAE(0.15),and VAF(90.32%)*** result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear *** paper presented an efficacious attempt toward swift prediction of joints shear strength,thus avoiding the need for costly in-site and laboratory tests.

关键词： Rock discontinuities Joints shear strength metaheuristic optimization algorithms Machine learning

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