148 results on '"Neshat, Mehdi"'
Search Results
102. FAIPSO: fuzzy adaptive informed particle swarm optimization
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Neshat, Mehdi, primary
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- 2012
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103. Harmony-based feature selection to improve the nearest neighbor classification
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Adeli, Ali, primary, Sinaee, Mehrnoosh, additional, Zomorodian, Javad, additional, and Neshat, Mehdi, additional
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- 2012
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104. Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization
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Neshat, Mehdi, primary, Sargolzaei, Mehdi, additional, Nadjaran Toosi, Adel, additional, and Masoumi, Azra, additional
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- 2012
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105. Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications
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Neshat, Mehdi, primary, Sepidnam, Ghodrat, additional, Sargolzaei, Mehdi, additional, and Toosi, Adel Najaran, additional
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- 2012
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106. Swallow swarm optimization algorithm: a new method to optimization
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Neshat, Mehdi, primary, Sepidnam, Ghodrat, additional, and Sargolzaei, Mehdi, additional
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- 2012
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107. A New Kind of Pso: Predator Particle Swarm Optimization
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Neshat, Mehdi, primary, Sargolzaei, Mehdi, additional, Masoumi, Azra, additional, and Najaran, Adel, additional
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- 2012
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108. A Review of Artificial Fish Swarm Optimization Methods and Applications
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Neshat, Mehdi, primary, Adeli, Ali, additional, Sepidnam, Ghodrat, additional, Sargolzaei, Mehdi, additional, and Toosi, Adel Najaran, additional
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- 2012
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109. Designing a fuzzy expert system to predict the concrete mix design
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Neshat, Mehdi, primary and Adeli, Ali, additional
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- 2011
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110. A Hybrid Method in Informed Search: Fuzzy Simplified Memory-Bounded A* Approach
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Neshat, Mehdi, primary
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- 2010
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111. AIPSO: Adaptive Informed Particle Swarm Optimization
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Neshat, Mehdi, primary and Rezaei, Masoud, additional
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- 2010
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112. Hopfield neural network and fuzzy Hopfield neural network for diagnosis of liver disorders
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Neshat, Mehdi, primary and Zadeh, Abas E., additional
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- 2010
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113. A New Frequency Dependent Resistor for modeling skin effect of wire and echo cancellation by PSO
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Rezaei, Masoud, primary, Neshat, Mehdi, additional, and yazdi, Hadi Sadoghi, additional
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- 2010
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114. FESHDD: Fuzzy expert system for hepatitis B diseases diagnosis
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Neshat, Mehdi, primary and Yaghobi, Mehdi, additional
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- 2009
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115. Designing an expert system of liver disorders by using neural network and comparing it with parametric and nonparametric system
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Neshat, Mehdi, primary, Yaghobi, Mehdi, additional, and Naghibi, Mohammad, additional
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- 2008
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116. Offshore Wind Farm Layouts Designer Software's
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Nezhad, Meysam Majidi, Neshat, Mehdi, Azaza, Maher, Avelin, Anders, Piras, Giuseppe, and Garcia, Davide Astiaso
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•Highlight the offshore wind farms analysis, installation and development sites;•Offshore wind farms layouts analysis designed and developed based on site key specifications;•Wind farms accurate assessment to identify site locations and potential installation;•Offshore wind farm layouts designer software's;
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- 2023
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117. An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation
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Neshat, Mehdi, Lee, Soohyun, Momin, Md Moksedul, Truong, Buu, van der Werf, Julius HJ, and Lee, S Hong
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harmonised matrix ,hyper-parameters ,scale factor ,genomic prediction ,single-step genetic evaluation - Abstract
Refereed/Peer-reviewed The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter
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- 2023
118. A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm
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Ehsan Abbasnejad, Markus Wagner, Meysam Majidi Nezhad, Davide Astiaso Garcia, Mehdi Neshat, Seyedali Mirjalili, Lina Bertling Tjernberg, Bradley Alexander, Neshat, Mehdi, Nezhad, Meysam Majidi, Abbasnejad, Ehsan, Mirjalili, Seyedali, Tjernberg, Lina Bertling, Astiaso Garcia, Davide, Alexande, Bradley, and Wagner, Markus
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Computer science ,020209 energy ,Evolutionary algorithm ,Energy Engineering and Power Technology ,generalised normal distribution optimisation ,02 engineering and technology ,Turbine ,Wind speed ,wind speed prediction ,020401 chemical engineering ,short-term forecasting ,0202 electrical engineering, electronic engineering, information engineering ,0204 chemical engineering ,evolutionary algorithms ,Physics::Atmospheric and Oceanic Physics ,Wind power ,Artificial neural network ,Renewable Energy, Sustainability and the Environment ,business.industry ,Deep learning ,deep learning models ,hybrid evolutionary deep learning method ,Term (time) ,Offshore wind power ,Fuel Technology ,Nuclear Energy and Engineering ,Artificial intelligence ,business ,Marine engineering - Abstract
Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria. Refereed/Peer-reviewed
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- 2021
119. A Parametric Study of Wave Energy Converter Layouts in Real Wave Models
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Erfan Amini, Mehdi Neshat, Davide Astiaso Garcia, Fereidoun Amini, Danial Golbaz, Meysam Majidi Nezhad, Amini, Erfan, Golbaz, Danial, Amini, Fereidoun, Nezhad, Meysam Majidi, Neshat, Mehdi, and Astiaso Garcia, Davide
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Wave energy converter ,Control and Optimization ,Computer science ,020209 energy ,Acoustics ,layout assessment ,wave energy conversion ,Energy Engineering and Power Technology ,02 engineering and technology ,lcsh:Technology ,real wave model ,Physics::Geophysics ,Wind wave ,renewable energy ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Engineering (miscellaneous) ,Physics::Atmospheric and Oceanic Physics ,Parametric statistics ,Buoy ,lcsh:T ,Renewable Energy, Sustainability and the Environment ,business.industry ,Power (physics) ,Renewable energy ,020201 artificial intelligence & image processing ,business ,Rotation (mathematics) ,Energy (signal processing) ,Energy (miscellaneous) - Abstract
Ocean wave energy is a broadly accessible renewable energy source; however, it is not fully developed. Further studies on wave energy converter (WEC) technologies are required in order to achieve more commercial developments. In this study, four CETO6 spherical WEC arrangements have been investigated, in which a fully submerged spherical converter is modelled. The numerical model is applied using linear potential theory, frequency-domain analysis, and irregular wave scenario. We investigate a parametric study of the distance influence between WECs and the effect of rotation regarding significant wave direction in each arrangement compared to the pre-defined layout. Moreover, we perform a numerical landscape analysis using a grid search technique to validate the best-found power output of the layout in real wave models of four locations on the southern Australian coast. The results specify the prominent role of the distance between WECs, along with the relative angle of the layout to dominant wave direction, in harnessing more power from the waves. Furthermore, it is observed that a rise in the number of WECs contributed to an increase in the optimum distance between converters. Consequently, the maximum exploited power from each buoy array has been found, indicating the optimum values of the distance between buoys in different real wave scenarios and the relative angle of the designed layout with respect to the dominant in-site wave direction. Refereed/Peer-reviewed
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- 2020
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120. Design optimisation of a multi-mode wave energy converter
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ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2020 Online 3-7 August 2020, Sergiienko, Nataliia Y, Neshat, Mehdi, Da Silva, Leandro Souza Pinheiro, Alexander, Bradley, and Wagner, Markus
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A wave energy converter (WEC) similar to the CETO system developed by Carnegie Clean Energy is considered for design optimisation. This WEC is able to absorb power from heave, surge and pitch motion modes, making the optimisation problem nontrivial. The WEC dynamics is simulated using the spectraldomain model taking into account hydrodynamic forces, viscous drag, and power take-off forces. The design parameters for optimisation include the buoy radius, buoy height, tether inclination angles, and control variables (damping and stiffness). The WEC design is optimised for the wave climate at Albany test site in Western Australia considering unidirectional irregular waves. Two objective functions are considered: (i) maximisation of the annual average power output, and (ii) minimisation of the levelised cost of energy (LCoE) for a given sea site. The LCoE calculation is approximated as a ratio of the produced energy to the significant mass of the system that includes the mass of the buoy and anchor system. Six different heuristic optimisation methods are applied in order to evaluate and compare the performance of the best known evolutionary algorithms, a swarm intelligence technique and a numerical optimisation approach. The results demonstrate that if we are interested in maximising energy production without taking into account the cost of manufacturing such a system, the buoy should be built as large as possible (20 m radius and 30 m height). However, if we want the system that produces cheap energy, then the radius of the buoy should be approximately 11-14 m while the height should be as low as possible. These results coincide with the overall design that Carnegie Clean Energy has selected for its CETO 6 multimoored unit. However, it should be noted that this study is not informed by them, so this can be seen as an independent validation of the design choices.
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- 2020
121. GTOPX Space Mission Benchmarks
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Martin Schlueter, Markus Wagner, Mohamed Wahib, Masaharu Munetomo, Mehdi Neshat, Schlueter, Martin, Neshat, Mehdi, Wahib, Mohamed, Munetomo, Masaharu, and Wagner, Markus
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Optimization ,FOS: Computer and information sciences ,landscape analysis ,Theoretical computer science ,Source code ,Computer science ,Fitness landscape ,media_common.quotation_subject ,Subroutine ,Benchmark ,QA76.75-76.765 ,benchmark ,Computer software ,Neural and Evolutionary Computing (cs.NE) ,MATLAB ,space mission trajectory ,computer.programming_language ,media_common ,Heuristic ,Landscape analysis ,Computer Science - Neural and Evolutionary Computing ,Python (programming language) ,Computer Science Applications ,Term (time) ,Space mission trajectory ,Benchmark (computing) ,optimization ,computer ,Software - Abstract
This contribution introduces the GTOPX space mission benchmark collection, which is an extension of the GTOP database published by the European Space Agency (ESA). The term GTOPX stands for Global Trajectory Optimization Problems with eXtension. GTOPX consists of ten individual benchmark instances representing real-world interplanetary space trajectory design problems. In regard to the original GTOP collection, GTOPX includes three new problem instances featuring mixed-integer and multi-objective properties. GTOPX enables a simplified user-handling, unified benchmark function call and some minor bug corrections to the original GTOP implementation. Furthermore, GTOPX is linked from original C++ source code to Python and Matlab based on dynamic link libraries, assuring computationally fast and accurate reproduction of the benchmark results in all programming languages. We performed a comprehensive landscape analysis to characterize the properties of the fitness landscape of GTOPX benchmarks. Space mission trajectory design problems as those represented in GTOPX are known to be highly non-linear and difficult to solve. The GTOPX collection therefore aims in particular at researchers wishing to put advanced (meta)heuristic and hybrid optimization algorithms to the test. Furthermore, GTOPX is linked from original C++ source code to Python and Matlab based on dynamic link libraries, assuring computationally fast and accurate reproduction of the benchmark results in all programming languages. We performed a comprehensive landscape analysis to characterize the properties of the fitness landscape of GTOPX benchmarks. Space mission trajectory design problems as those represented in GTOPX are known to be highly non-linear and difficult to solve. The GTOPX collection therefore aims in particular at researchers wishing to put advanced (meta)heuristic and hybrid optimization algorithms to the test. Refereed/Peer-reviewed
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- 2020
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122. New insights into position optimisation of wave energy converters using hybrid local search
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Mehdi Neshat, Nataliia Y. Sergiienko, Bradley Alexander, Markus Wagner, Neshat, Mehdi, Alexander, Bradley, Sergiienko, fNataliia Y., and Wagner, Markus
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Mathematical optimization ,hybrid local search ,position optimisation ,General Computer Science ,Buoy ,Heuristic (computer science) ,business.industry ,Computer science ,General Mathematics ,05 social sciences ,050301 education ,02 engineering and technology ,renewable energy ,Renewable energy ,Set (abstract data type) ,Wave model ,Position (vector) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Local search (optimization) ,wave energy converters ,business ,0503 education ,Energy (signal processing) - Abstract
Renewable energy will play a pivotal role in meeting future global energy demand. Of current renewable sources, wave energy offers enormous potential for growth. This research investigates the optimisation of the placement of oscillating buoy-type wave energy converters (WECs). This work explores the design of a wave farm consisting of an array of fully submerged three-tether buoys. In a wave farm, buoy positions strongly determine the farm's output. Optimising the buoy positions is a challenging research problem due to complex and extensive interactions (constructive and destructive) between buoys. This research focuses on maximising the power output of the farm through the placement of buoys in a size-constrained environment, and we propose a new hybrid approach mixing local search, using a surrogate power model, and numerical optimisation methods. The proposed hybrid method is compared with other state-of-the-art search methods in five different wave scenarios - one simplified irregular wave model and four real wave regimes. The new hybrid methods outperform well-known previous heuristic methods in terms of both quality of achieved solutions and the convergence-rate of search in all tested wave regimes. The best performing method in real-wave scenarios uses the active set non-linear optimisation method to tune final placements. The effectiveness of this method seems to stem for its capacity to search over a larger area than other compared tuning methods. Refereed/Peer-reviewed
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- 2020
123. Optimisation of large wave farms using a multi-strategy evolutionary framework
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Markus Wagner, Mehdi Neshat, Nataliia Y. Sergiienko, Bradley Alexander, Neshat, Mehdi, Alexander, Bradley, Sergiienko, Nataliia Y, Wagner, Markus, and 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 Cancun, Mexico 8-12 July 2020
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Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Wave energy converter ,Mathematical optimization ,Computer science ,optimisation ,Population ,Evolutionary algorithm ,0102 computer and information sciences ,02 engineering and technology ,01 natural sciences ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,Local search (optimization) ,Neural and Evolutionary Computing (cs.NE) ,wave energy converters ,Electrical Engineering and Systems Science - Signal Processing ,evolutionary algorithms ,education ,education.field_of_study ,business.industry ,hybrid multi-strategy evolutionary method ,Computer Science - Neural and Evolutionary Computing ,Renewable energy ,discrete local search ,010201 computation theory & mathematics ,020201 artificial intelligence & image processing ,business ,large wave farm ,Energy (signal processing) - Abstract
Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs). Energy maximisation for large farms is a challenging search problem due to the costly calculations of the hydrodynamic interactions between WECs in a large wave farm and the high dimensionality of the search space. To address this problem, we propose a new hybrid multi-strategy evolutionary framework combining smart initialisation, binary population-based evolutionary algorithm, discrete local search and continuous global optimisation. For assessing the performance of the proposed hybrid method, we compare it with a wide variety of state-of-the-art optimisation approaches, including six continuous evolutionary algorithms, four discrete search techniques and three hybrid optimisation methods. The results show that the proposed method performs considerably better in terms of convergence speed and farm output. Refereed/Peer-reviewed
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- 2020
124. A new bi-level optimisation framework for optimising a multi-modewave energy converter design. A case study for the marettimo island, mediterranean sea
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Erfan Amini, Meysam Majidi Nezhad, Davide Astiaso Garcia, Mehdi Neshat, Nataliia Y. Sergiienko, Bradley Alexander, Markus Wagner, Neshat, Mehdi, Sergiienko, Nataliia Y, Amini, Erfan, Nezhad, Meysam Majidi, Astiaso Garcia, Davide, Alexander, Bradley, and Wagner, Markus
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Wave energy converter ,Control and Optimization ,Computer science ,bi-level optimisation method ,020209 energy ,Energy Engineering and Power Technology ,020101 civil engineering ,02 engineering and technology ,evolutionary algorithms ,renewable energy ,wave energy converter ,geometric parameters ,power take-off ,levelised cost of energy ,lcsh:Technology ,0201 civil engineering ,Mediterranean sea ,Wind wave ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Rogue wave ,Power take-off ,Cost of electricity by source ,Engineering (miscellaneous) ,CETO ,Renewable Energy, Sustainability and the Environment ,business.industry ,lcsh:T ,Renewable energy ,Power (physics) ,business ,Energy harvesting ,Energy (signal processing) ,Energy (miscellaneous) ,Marine engineering - Abstract
To advance commercialisation of ocean wave energy and for the technology to become competitive with other sources of renewable energy, the cost of wave energy harvesting should be significantly reduced. The Mediterranean Sea is a region with a relatively low wave energy potential, but due to the absence of extreme waves, can be considered at the initial stage of the prototype development as a proof of concept. In this study, we focus on the optimisation of a multi-mode wave energy converter inspired by the CETO system to be tested in the west of Sicily, Italy. We develop a computationally efficient spectral-domain model that fully captures the nonlinear dynamics of a wave energy converter (WEC). We consider two different objective functions for the purpose of optimising a WEC: (1) maximise the annual average power output (with no concern for WEC cost), and (2) minimise the levelised cost of energy (LCoE). We develop a new bi-level optimisation framework to simultaneously optimise the WEC geometry, tether angles and power take-off (PTO) parameters. In the upper-level of this bi-level process, all WEC parameters are optimised using a state-of-the-art self-adaptive differential evolution method as a global optimisation technique. At the lower-level, we apply a local downhill search method to optimise the geometry and tether angles settings in two independent steps. We evaluate and compare the performance of the new bi-level optimisation framework with seven well-known evolutionary and swarm optimisation methods using the same computational budget. The simulation results demonstrate that the bi-level method converges faster than other methods to a better configuration in terms of both absorbed power and the levelised cost of energy. The optimisation results confirm that if we focus on minimising the produced energy cost at the given location, the best-found WEC dimension is that of a small WEC with a radius of 5 m and height of 2 m. Refereed/Peer-reviewed
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- 2020
125. A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
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Markus Wagner, Mehdi Neshat, Bradley Alexander, Neshat, Mehdi, Alexander, Bradley, and Wagner, Markus
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FOS: Computer and information sciences ,Information Systems and Management ,position optimisation ,Computer science ,Evolutionary algorithm ,adaptive gray wolf optimiser ,power take off system ,Scale (descriptive set theory) ,02 engineering and technology ,Theoretical Computer Science ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,Local search (optimization) ,Neural and Evolutionary Computing (cs.NE) ,wave energy converters ,Power take-off ,business.industry ,Backtracking ,05 social sciences ,cooperative co-Evolution algorithms ,050301 education ,Computer Science - Neural and Evolutionary Computing ,renewable energy ,Computer Science Applications ,Renewable energy ,Control and Systems Engineering ,020201 artificial intelligence & image processing ,business ,0503 education ,Algorithm ,Software ,Energy (signal processing) - Abstract
Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this work, we explore the optimisation of WEC arrays consisting of a three-tether buoy model called CETO. Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys in farms and also the power-take-off (PTO) parameters for each buoy. The search space for these parameters is complex and multi-modal. Moreover, the evaluation of each parameter setting is computationally expensive -- limiting the number of full model evaluations that can be made. To handle this problem, we propose a new hybrid cooperative co-evolution algorithm (HCCA). HCCA consists of a symmetric local search plus Nelder-Mead and a cooperative co-evolution algorithm (CC) with a backtracking strategy for optimising the positions and PTO settings of WECs, respectively. Moreover, a new adaptive scenario is proposed for tuning grey wolf optimiser (AGWO) hyper-parameter. AGWO participates notably with other applied optimisers in HCCA. For assessing the effectiveness of the proposed approach five popular Evolutionary Algorithms (EAs), four alternating optimisation methods and two modern hybrid ideas (LS-NM and SLS-NM-B) are carefully compared in four real wave situations (Adelaide, Tasmania, Sydney and Perth) with two wave farm sizes (4 and 16). According to the experimental outcomes, the hybrid cooperative framework exhibits better performance in terms of both runtime and quality of obtained solutions., Information Sciences (2020)
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- 2019
126. A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
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Mehdi Neshat, Nataliia Y. Sergiienko, Markus Wagner, Bradley Alexander, Neshat, Mehdi, Alexander, Bradley, Sergiienko, Nataliia Y, Wagner, Markus, and 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 Prague, Czech Republic 13-17 July 2019
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FOS: Computer and information sciences ,Mathematical optimization ,Computer science ,Evolutionary algorithm ,0102 computer and information sciences ,02 engineering and technology ,01 natural sciences ,Power Take Off system ,Wind wave ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,Local search (optimization) ,Neural and Evolutionary Computing (cs.NE) ,evolutionary algorithms ,Wave Energy Converters ,Power take-off ,business.industry ,Heuristic ,Computer Science - Neural and Evolutionary Computing ,position optimization ,renewable energy ,Renewable energy ,010201 computation theory & mathematics ,020201 artificial intelligence & image processing ,business ,Energy (signal processing) - Abstract
Ocean wave energy is a source of renewable energy that has gained much attention for its potential to contribute significantly to meeting the global energy demand. In this research, we investigate the problem of maximising the energy delivered by farms of wave energy converters (WEC's). We consider state-of-the-art fully submerged three-tether converters deployed in arrays. The goal of this work is to use heuristic search to optimise the power output of arrays in a size-constrained environment by configuring WEC locations and the power-take-off (PTO) settings for each WEC. Modelling the complex hydrodynamic interactions in wave farms is expensive, which constrains search to only a few thousand model evaluations. We explore a variety of heuristic approaches including cooperative and hybrid methods. The effectiveness of these approaches is assessed in two real wave scenarios (Sydney and Perth) with farms of two different scales. We find that a combination of symmetric local search with Nelder-Mead Simplex direct search combined with a back-tracking optimization strategy is able to outperform previously defined search techniques by up to 3%. Refereed/Peer-reviewed
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- 2019
127. Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation
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Ehsan Abbasnejad, Qinfeng Shi, Markus Wagner, Mehdi Neshat, Bradley Alexander, Neshat, Mehdi, Abbasnejad, Ehsan, Shi, Qinfeng, Alexander, Bradley, Wagner, Markus, and 26th International Conference on Neural Information Processing, ICONIP 2019 Sydney, Australia 12-15 December 2019
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Gray Wolf Optimiser ,Mathematical optimization ,Wind power ,Computer science ,business.industry ,Backtracking ,local search ,020209 energy ,Evolutionary algorithm ,02 engineering and technology ,renewable energy ,Renewable energy ,Recurrent neural network ,surrogate-based optimisation ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,020201 artificial intelligence & image processing ,Local search (optimization) ,Evolutionary Algorithms ,sequential deep learning ,Wave Energy Converters ,business ,Energy (signal processing) - Abstract
Installed renewable energy capacity has expanded massively in recent years. Wave energy, with its high capacity factors, has great potential to complement established sources of solar and wind energy. This study explores the problem of optimising the layout of advanced, three-tether wave energy converters in a size-constrained farm in a numerically modelled ocean environment. Simulating and computing the complicated hydrodynamic interactions in wave farms can be computationally costly, which limits optimisation methods to using just a few thousand evaluations. For dealing with this expensive optimisation problem, an adaptive neuro-surrogate optimisation (ANSO) method is proposed that consists of a surrogate Recurrent Neural Network (RNN) model trained with a very limited number of observations. This model is coupled with a fast meta-heuristic optimiser for adjusting the model’s hyper-parameters. The trained model is applied using a greedy local search with a backtracking optimisation strategy. For evaluating the performance of the proposed approach, some of the more popular and successful Evolutionary Algorithms (EAs) are compared in four real wave scenarios (Sydney, Perth, Adelaide and Tasmania). Experimental results show that the adaptive neuro model is competitive with other optimisation methods in terms of total harnessed power output and faster in terms of total computational costs. Refereed/Peer-reviewed
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- 2019
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128. A detailed comparison of meta-heuristic methods for optimising wave energy converter placements
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Markus Wagner, Mehdi Neshat, Bradley Alexander, Yuanzhong Xia, Neshat, Mehdi, Alexander, Bradley, Wagner, Markus, Xia, Yuanzhong, and 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 Kyoto, Japan 15-19 July 2018
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Power station ,Computer science ,business.industry ,020209 energy ,Evolutionary algorithm ,position optimization ,02 engineering and technology ,Industrial engineering ,renewable energy ,Renewable energy ,Work (electrical) ,Wave Energy Converter ,Wind wave ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Local search (optimization) ,evolutionary algorithms ,Heuristics ,business ,Energy (signal processing) - Abstract
In order to address environmental concerns and meet growing energy demand the development of green energy technology has expanded tremendously. One of the most promising types of renewable energy is ocean wave energy. While there has been strong research in the development of this technology to date there remain a number of technical hurdles to overcome. This research explores a type of wave energy converter (WEC) called a buoy. This work models a power station as an array of fully submerged three-tether buoys. The target problem of this work is to place buoys in a size-constrained environment to maximise power output. This article improves prior work by using a more detailed model and exploring the search space using a wide variety of search heuristics. We show that a hybrid method of stochastic local search combined with Nelder-Mead Simplex direct search performs better than previous search techniques. Refereed/Peer-reviewed
- Published
- 2018
129. FAIPSO: fuzzy adaptive informed particle swarm optimization
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Mehdi Neshat and Neshat, Mehdi
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Mathematical optimization ,Adaptive neuro fuzzy inference system ,particle swarm optimization ,swarm intelligence ,media_common.quotation_subject ,Particle swarm optimization ,adaptation ,Inertia ,Swarm intelligence ,Acceleration ,Range (mathematics) ,Artificial Intelligence ,Multi-swarm optimization ,optimization ,Software ,fuzzy inference system ,Mathematics ,media_common ,Premature convergence - Abstract
Conventional particle swarm optimization (PSO) is an appropriate optimization method, yet it suffers from some drawbacks. Trapping in local minimums or premature convergence of particles leads to unsatisfactory levels of optimization. In this paper, a new method for improving PSO is provided. In the proposed method (FAIPSO), the acceleration coefficients c 1 and c 2 are adaptively adjusted for each particle in each iteration. For the adaptive controlling of the acceleration coefficients, a fuzzy inference system is used. This fuzzy inference system comprises six inputs, two outputs, and ten rules. In order to reduce inertia weight (ω), a parabolic model is used. In addition to this, a range of vision (Mu) is defined for each of the particles and every one of the particles searches within this range. This range of vision changes adaptively. In order to adaptively control the range of vision, a fuzzy inference system is employed. This system has two inputs, one output, and 14 rules. To test the proposed method, 16 benchmarks, each inheriting special characteristics, are used. The performance of the proposed method was compared with that of ten types of PSOs (each of which are among the reputable works of the PSO subject). According to the results, the proposed method shows a good performance and is more appropriate than other methods.
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- 2012
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130. Swallow swarm optimization algorithm: a new method to optimization
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Mehdi Neshat, Ghodrat Sepidnam, Mehdi Sargolzaei, Neshat, Mehdi, Sepidnam, Ghodrat, and Sargolzaei, Mehdi
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Mathematical optimization ,Meta-optimization ,particle swarm optimization ,Computer science ,ComputingMethodologies_MISCELLANEOUS ,Swarm behaviour ,Particle swarm optimization ,Computational intelligence ,Swarm intelligence ,fish swarm optimization ,Artificial Intelligence ,computational intelligence ,Derivative-free optimization ,Multi-swarm optimization ,benchmark function ,swallow swarm optimization (SSO) ,Metaheuristic ,Software - Abstract
This paper presents an exposition of a new method of swarm intelligence-based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO. Refereed/Peer-reviewed
- Published
- 2012
- Full Text
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131. Using LibQUAL Model for Improving the Level of Students’ Satisfaction from Quality of Services in Academic Libraries: A Case Study in North Khorasan Province, Iran
- Author
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Mohammad Hasani, Mehdi Neshat, Ali Akbar Pourahmad, Pourahmad, Ali Akbar, Neshat, Mehdi, and Hasani, Mohammad Reza
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Estimation ,Data collection ,measurement of library services ,Computer Networks and Communications ,Computer science ,media_common.quotation_subject ,Library services ,05 social sciences ,Library and Information Sciences ,LibQUAL model ,academic libraries ,050905 science studies ,Computer Science Applications ,Survey methodology ,Terms of service ,services quality ,Anticipation (artificial intelligence) ,Quality (business) ,Quality level ,0509 other social sciences ,Marketing ,050904 information & library sciences ,media_common - Abstract
The most important aim of the research is to evaluate and analyse the quality level of services of four different university libraries which are located in the North Khorasan province in Iran. The statistical populations included various students from different branches and they were chosen as samples. For collecting data, the survey method was applied; meanwhile, data collection tool, specific questionnaire were used since that each of the four components for quality estimation of services was calculated using LibQUAL tool. The mean total services for university libraries of North Khorasan were negative in terms of service fitness gap, which means that libraries were not capable of satisfying the minimum anticipation of their users. Interestingly, for all library services, gap was negative too. In other words, libraries are far from rewarding the expectations of students associated with the most desirable (maximum) level of services. Refereed/Peer-reviewed
- Published
- 2016
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132. A hybrid fuzzy knowledge-based system for forest fire risk forecasting
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Masoud Tabatabi, Ebrahim Zahmati, Mehdi Neshat, Mohhammad Shirdel, Neshat, Mehdi, Tabatabi, Masoud, Zahmati, Ebrahim, and Shirdel, Mohhammad
- Subjects
Fuzzy inference ,General Computer Science ,Fuzzy expert system ,Computer science ,fire intensity ,General Engineering ,computer.software_genre ,Fuzzy logic ,Fire risk ,modelling ,Knowledge-based systems ,Hybrid system ,hybrid system ,Forest ecology ,risk estimation ,Social consequence ,Data mining ,computer ,forest fire ,fuzzy inference system - Abstract
Fire is one of the most important factors destroying forest ecosystems which can result in negative economic and social consequences. Quick detection can be an effective factor in controlling this destructive phenomenon. This research was aimed at designing a hybrid fuzzy expert system in order to predict the size of forest fires effectively and accurately. The data were taken from the authentic dataset named forest fire in University of California (UCI). In fact, the proposed system is a hybrid of six fuzzy inference systems with acceptable performances according to their results. The accuracy of predicting the size of fire was 81.2%. Refereed/Peer-reviewed
- Published
- 2016
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133. Recognising the kind of cloud using a new fuzzy knowledge-based system
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Mohhammad Ahmadi, Mehdi Neshat, Neshat, Mehdi, and Ahmadi, Mohhammad
- Subjects
General Computer Science ,Computer science ,Human life ,knowledge extraction ,Cloud computing ,computer.software_genre ,Fuzzy logic ,Knowledge-based systems ,knowledge-based systems ,Knowledge extraction ,cloud datasets ,fuzzy inference systems ,meteorology ,fuzzy KBS ,FIS ,business.industry ,General Engineering ,Data science ,Expert system ,Variety (cybernetics) ,fuzzy expert systems ,Satellite ,fuzzy logic ,business ,computer ,satellite images - Abstract
Nowadays, expert systems play a major role in better doing of complex tasks and giving advice to the experts because expertism is a specialised knowledge. Overall, expert systems are used to solve the problems for which there is not an accurate knowledge and a particular algorithm. Understanding the atmospheric phenomena and their role in human life are the most important and affecting issues in human societies. In meteorology, it is important to identify the type of clouds. By monitoring from the Earth's surface (seeing bottom view of the cloud) and using satellites (seeing top view of the cloud), we can identify the variety of clouds. A fuzzy inference system with the specialists' knowledge of meteorology is designed in this paper and its aims are detection of the cloud type through extracting knowledge from satellite images of the cloud upper portions. The used data are extracted from the reputable website of UCI called cloud dataset. This dataset is gathered by Philip Collard in two ranges of IR and VISIBLE. Using the experts' knowledge, this system determines the type of cloud with an accuracy level of 88.25% ± 0.5 and according to experts' opinion; the results are suitable and acceptable Refereed/Peer-reviewed
- Published
- 2016
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134. A REVIEW OF ARTIFICIAL FISH SWARM OPTIMIZATION METHODS AND APPLICATIONS
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Adel Najaran Toosi, Mehdi Sargolzaei, Mehdi Neshat, Ghodrat Sepidnam, Ali Adeli, Neshat, Mehdi, Adeli, Ali, Sepidnam, Ghodrat, Sargolzaei, Mehdi, and Toosi, Adel Najaran
- Subjects
Optimization problem ,swarm optimization ,business.industry ,Natural computing ,Computer science ,lcsh:T ,Intelligent decision support system ,natural computing ,Swarm behaviour ,Natural Computing ,Swarm intelligence ,lcsh:Technology ,Control and Systems Engineering ,Swarm Optimization ,lcsh:Technology (General) ,Optimization methods ,artificial fish swarm optimization ,Social animal ,lcsh:T1-995 ,En masse Movement ,Artificial intelligence ,Electrical and Electronic Engineering ,Artificial Fish Swarm Optimization ,business - Abstract
The Swarm Intelligence is a new and modern method employed in optimization problems. The Swarm Intelligence method is based on the en masse movement of living animals like birds, fishes, ants and other social animals. Migration, seeking for food and fighting with enemies are social behaviors of animals. Optimization principle is seen in these animals. The Artificial Fish Swarm Optimization (AFSA) method is one of the Swarm Intelligence approaches that works based on the population and stochastic search. Fishes show very intelligently social behaviors. This algorithm is one of the best approaches of the Swarm Intelligence method with considerable advantages like high convergence speed, flexibility, error tolerance and high accuracy. this paper review the AFSA algorithm, its evolution stages from the start point up to now, improvements and applications in various fields like optimization, control, image processing, data mining, improving neural networks, networks, scheduling, and signal processing and so on. Also, various methods combining the AFSA with other optimization methods like PSO, Fuzzy Logic, Cellular Learning Automata or intelligent search methods like Tabu search, Simulated Annealing, Chaos Search and etc.
- Published
- 2012
135. Harmony-based feature selection to improve the nearest neighbor classification
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Javad Zomorodian, Ali Adeli, Mehrnoosh Sinaee, Mehdi Neshat, Adeli, Ali, Sinaee, Mehrnoosh, Zomorodian, Javad, Neshat, Mehdi, and 2nd International Conference on Computational Science, Engineering and Information, CCSEIT 2012 Coimbatore 26-28 October 2012
- Subjects
Fitness function ,AUC ,Receiver operating characteristic ,business.industry ,Feature vector ,Feature selection ,Pattern recognition ,computer.software_genre ,k-nearest neighbors algorithm ,feature selection ,noisy feature elimination ,harmony search ,Harmony search ,Artificial intelligence ,Data mining ,business ,Classifier (UML) ,computer ,Mathematics ,K-NN - Abstract
A new approach for feature selection is presented in this paper. The proposed approach uses the Harmony Search with a novel fitness function to eliminate noisy and irrelevant features. Harmony vectors contain real weights which refer to feature space. The best and significant features are selected according to a threshold. Fitness function of Harmony Search is based on the Area Under the receiver operating characteristics Curve (AUC). All of the selected features are employed to improve the classification of the k Nearest Neighbor (k-NN) classifier. Experimental results claim that the proposed method is able to improve the classification performance of k-NN algorithm in comparison with the other important methods in realm of feature selection such as BAHSIC, FSS, BSS and MFS. Refereed/Peer-reviewed
- Published
- 2012
136. A new kind of PSO: predator particle swarm optimization
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Mehdi Sargolzaei, Mehdi Neshat, Adel Najaran, Azra Masoumi, Neshat, Mehdi, Sargolzaei, Mehdi, Masoumi, Azra, and Najaran, Adel
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Mathematical optimization ,particle swarm optimization ,predator ,Computer science ,lcsh:T ,premature convergence ,Particle swarm optimization ,local optimum ,Swarm intelligence ,lcsh:Technology ,ComputingMethodologies_ARTIFICIALINTELLIGENCE ,Control and Systems Engineering ,lcsh:Technology (General) ,lcsh:T1-995 ,Electrical and Electronic Engineering ,Multi-swarm optimization ,Predator ,Metaheuristic - Abstract
Today, swarm intelligence is widely used in optimization problems. PSO is one the best swarm intelligence methods. In the method, each particle moves toward the direction in which the best individual and group experience has happened. The most important disadvantage of this method is that it falls in local optima. To fix the problem, a metaheuristic method is proposed in this paper. There has always been a competition between prey and predator in the nature. Little birds often fly in a colony form to run away from birds of prey. Being inspired by the phenomenon, a new particle is added to PSO algorithm known as predator, also a new behavior called “Take flight from predator” is defined. This particle is responsible for attacking the colony of particles so as to prevent the premature convergence. With the predator attack to the colony, particles run away and again the chance rises for a Global optimum to be gained. The attack just caused particles dispersion and no particle dies. It can be repeated for m times and the optimal point is saved each time. To test the method, 12 benchmark functions were employed and the results were compared to OPSO, VPSO, LPSO, and GPSO methods. Regarding the results, the proposed method had a better performance.
- Published
- 2012
137. The new method of adaptive CPU scheduling using Fonseca and Fleming's Genetic Algorithm
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Neshat, Mehdi, Sargolzaei, Mehdi, Najaran, Adel, and Adeli, Ali
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FFGA ,CPU Scheduling ,multiobjective optimization ,response time ,waiting time ,turnaround time - Abstract
The CPU scheduling is one of the most important tasks of the operating system. Many algorithms are designed and used in this regard each having advantages and disadvantages. In this paper a new algorithm for the CPU scheduling is presented using FFGA (Fonseca and Fleming’s Genetic Algorithm) multiobjective optimization. Contrary to the classical algorithms in use, it uses the three parameters of CPU burst time; I/O devices service time, and priority of process instead of using one parameter of CPU burst time. The important point is the adaptation of the algorithm which selects a special process depending on the system situation. The performance of this algorithm was compared with seven classical schedulingalgorithms (FCFS, RR (equal, prioritized), SJF (preemptive, non-preemptive, Priority (preemptive, nonpreemptive)), and the results showed that the performance of the proposed method is more optimized than other methods. The proposed algorithm optimizes the average waiting time and response time for the processes. Refereed/Peer-reviewed
- Published
- 2012
138. Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems
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Ghodrat Sepidnam, Mehdi Neshat, Mehdi Sargolzaei, Ali Adeli, Neshat, Mehdi, Adeli, Ali, Sepidnam, Ghodrat, and Sargolzaei, Mehdi
- Subjects
Engineering ,Adaptive neuro fuzzy inference system ,Aggregate (composite) ,Fineness modulus ,business.industry ,Mechanical Engineering ,Inference ,Structural engineering ,Fuzzy logic ,Industrial and Manufacturing Engineering ,Field (computer science) ,Computer Science Applications ,Slump ,Control and Systems Engineering ,Layer (object-oriented design) ,business ,Algorithm ,concrete mix design ,ANFIS ,fuzzy expert system ,fuzzy logic ,water ,cement ,slump ,fineness modulus (FM) ,CA ,F.A ,Software - Abstract
The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (D max), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of D max and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of D max and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system's results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.
- Published
- 2012
139. Improving nearest neighbor classification using Particle Swarm Optimization with novel fitness function
- Author
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Ali Adeli, Saeed Mozaffari, M. Javad Zomorodian, Mehdi Neshat, Ahmad Ghorbani-Rad, Adeli, Ali, Ghorbani-Rad, Ahmad, Zomorodian, M Javad, Neshat, Mehdi, Mozaffari, Saeed, and 4th International Conference ICCCI: International Conference on Computational Collective Intelligence Ho Chi Minh City, Vietnam 28-30 November 2012
- Subjects
Fitness function ,AUC ,Computer science ,business.industry ,particle swarm intelligence ,Particle swarm optimization ,k-NN ,Pattern recognition ,Feature selection ,feature weighting ,Tabu search ,Weighting ,k-nearest neighbors algorithm ,noisy feature elimination ,Feature (computer vision) ,Genetic algorithm ,Artificial intelligence ,business - Abstract
A new method of feature selection is presented in this paper. The proposed idea uses Particle Swarm Optimization (PSO) with fitness function in order to assign higher weights to informative features while noisy irrelevant features are given low weights. The measure of Area Under the receiver operating characteristics Curve (AUC) is used as the fitness function of the particles. Experimental results claim that the PSO-based feature weighting can improve the classification performance of the k-NN algorithm in comparison with the other important method in realm of feature weighting such as Mutual Information, Genetic Algorithm, Tabu Search and chi-squared (χ2). Additionally, on synthetic data sets, this method is able to allocate very low weight to the noisy irrelevant features which may be considered as the eliminated features from the data set.
- Published
- 2012
140. Designing a fuzzy expert system to predict the concrete mix design
- Author
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Mehdi Neshat, Ali Adeli, Neshat, Mehdi, Adeli, Ali, and 2011 9th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA Ottawa, ON, Canada 19-21 September 2011
- Subjects
Mathematical optimization ,Aggregate (composite) ,Computer science ,Fineness modulus ,Fuzzy control system ,prediction ,computer.software_genre ,Fuzzy logic ,Expert system ,Slump ,Compressive strength ,concert mix design ,sort ,fuzzy logic ,computer ,expert system ,fuzzy inference system - Abstract
The aim of this study is to design a Fuzzy Expert System to determine the concrete mix design. In the civil engineering, the determination of concrete mix design is so difficult and usually results in imprecision. Fuzzy logic is a way to represent a sort of uncertainty which is understandable for human. So, we can use the fuzzy logic to easily determine the concrete mix designs in a descriptive form. The input fields of system are Slump, Maximum Size of Aggregate (D max ), Concrete Compressive Strength (CCS) and Fineness Modulus (FM). The output fields are quantities of water, Cement, Fine Aggregate (F.A) and Course Aggregate (C.A). The experimental results show that the average error of predicted compressive strength for FIS is 6.43%, the minimum error of which is 4.73%. Refereed/Peer-reviewed
- Published
- 2011
141. A Hybrid Method in Informed Search: Fuzzy Simplified Memory-Bounded A* Approach
- Author
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Mehdi Neshat, Neshat, Mehdi, and 2010 International Conference on Computational Intelligence and Communication Networks Bhopal, India 26-28 November 2010
- Subjects
fuzzy ,Computer science ,business.industry ,Mobile robot ,Fuzzy control system ,SMA ,Fuzzy logic ,Memory management ,Path (graph theory) ,Robot ,hybrid method ,Artificial intelligence ,Motion planning ,business ,informed search - Abstract
In this paper, two methods are explained for robot's navigation and planning. Applying fuzzy logic and optimized searching SMA*, this compound method causes adaptive behavior in navigation. Also in big environments, it provides coming back with economizing on memory and memorizing the path - from starting point to target. Using fuzzy logic leads to a flexible behavior with least mistake of the navigator robot (target tracker). These two methods are compared and the method FSMA*has proved better results. Refereed/Peer-reviewed
- Published
- 2010
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142. AIPSO: Adaptive Informed Particle Swarm Optimization
- Author
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Mehdi Neshat, Masoud Rezaei, Neshat, Mehdi, Rezaei, Masoud, and 2010 IEEE International Conference on Intelligent Systems, IS 2010 London, UK 7-9 July 2010
- Subjects
Novel technique ,Mathematical optimization ,Computer Science::Neural and Evolutionary Computation ,ComputingMethodologies_MISCELLANEOUS ,MathematicsofComputing_NUMERICALANALYSIS ,adaptive ,PSO ,Particle swarm optimization ,informed ,Radius ,swarm intelligent ,Variable (computer science) ,Nonlinear system ,Point (geometry) ,Multi-swarm optimization ,Metaheuristic ,Mathematics - Abstract
A novel technique is proposed in this paper to optimize the Particle Swarm Optimization (PSO) algorithm. It is named Informed Particle Swarm Optimization (IPSO). A new treatment is added to the conventional PSO which eliminates blind searching in the conventional PSO. In the proposed algorithm, each particle will search it's around by a variable radius before following the gbest and pbest. It makes the proposed algorithm faster in searching the search space and better in finding the optimum point. The radius which each particle can will be decreases look around during the optimization by a nonlinear function. Because of the non blinding search, in the proposed algorithm, probability of falling in the best local is significantly decreased. The proposed algorithm is applied on some benchmarks and simulation results show advantages of the proposed IPSO. Refereed/Peer-reviewed
- Published
- 2010
143. A Fuzzy Expert System for heart disease diagnosis
- Author
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International MultiConference of Engineers and Computer Scientists, IMECS 2010 Kowloon, Hong Kong 17-19 March 2010, Adeli, Ali, and Neshat, Mehdi
- Subjects
medical distinguish ,fuzzy logic ,heart disease ,F=fuzzy expert system - Abstract
The aim of this study is to design a Fuzzy Expert System for heart disease diagnosis. The designed system based on the V.A. Medical Center, Long Beach and Cleveland Clinic Foundation data base. The system has 13 input fields and one output field. Input fields are chest pain type, blood pressure, cholesterol, resting blood sugar, maximum heart rate, resting electrocardiography (ECG), exercise, old peak (ST depression induced by exercise relative to rest), thallium scan, sex and age. The output field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4 (distinguish presence (values 1, 2, 3, 4)). This system uses Mamdani inference method. The results obtained from designed system are compared with the data in upon database and observed results of designed system are correct in 94%. The system designed in Matlab software. The system can be viewed as an alternative for existing methods to distinguish of heart disease presence. Refereed/Peer-reviewed
- Published
- 2010
144. Hopfield neural network and fuzzy Hopfield neural network for diagnosis of liver disorders
- Author
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Mehdi Neshat, Abas E. Zadeh, Neshat, Mehdi, Zadeh, Abas E, and 2010 IEEE International Conference on Intelligent Systems, IS 2010 London, UK 7-9 July 2010
- Subjects
Artificial neural network ,fuzzy ,Computer science ,business.industry ,neural network ,Intelligent decision support system ,Liver disorder diagnosis ,Machine learning ,computer.software_genre ,liver disorders ,Fuzzy logic ,Patient diagnosis ,Artificial intelligence ,State (computer science) ,Medical diagnosis ,business ,Fuzzy neural nets ,Hopfield ,computer - Abstract
Nowadays, artificial intelligence has a wide usage especially for designing intelligent systems in medicine. Diagnosing and determining different kinds of diseases are a part of this system's duties. In this research tried to diagnose liver disorders more accurate by using Hopfield neural network and fuzzy Hopfield beside fuzzy C-Means. Requiring data including 345 records and 6 fields is chosen from valid data bank (UCI) there are 6 inputs and the rate of network liver disorders risk is the output. In comparison with traditional diagnoses this system is faster, more economical, more reliable and more accurate. In the best state of training, Hopfield neural network and fuzzy Hopfield neural network diagnose liver disorders with the accuracy of 88.2% and 92% respectively. These results have been examined and proved experimentally under observation of specialists. Regarding diverse neural networks which been applied in diagnosing liver disorders, results have been an agreeable improvement. Refereed/Peer-reviewed
- Published
- 2010
145. FESHDD: Fuzzy expert system for hepatitis B diseases diagnosis
- Author
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Mehdi Neshat, Mehdi Yaghobi, Neshat, Mehdi, Yaghobi, Mehdi, and 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW 2009 Famagusta, Cyprus 2-4 September 2009
- Subjects
Medical knowledge ,medicine.diagnostic_test ,Fuzzy expert system ,diagnosis ,Computer science ,business.industry ,Fuzzy control system ,Hepatitis B ,medicine.disease ,computer.software_genre ,Expert system ,Knowledge-based systems ,Liver biopsy ,fuzzy expert systems ,medicine ,hepatitis B ,Artificial intelligence ,business ,Rule of inference ,computer ,Biomedical engineering - Abstract
Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. In this paper a fuzzy expert system has been designed for diagnosing the hepatitis B intensity rate. The main problem in determining the disease intensity is not having information about the data variation rate and its resulting effect on the system. A Hepatitis B data bank has been collected in accordance with the recent medical findings about this disease and the endorsement of a liver specialist. This bank has 300 records and each record has 7 fields. This bank has been assembled from patients presenting at the liver biopsy department of Imam Reza hospital Mashad, Iran. Using specialist research and experience strong inference rules have been attained. Thus, the accuracy oft the system in diagnosing the hepatitis B intensity is 94.4± 0.2%. This system is a great improvement in comparison with currently existing system. Refereed/Peer-reviewed
- Published
- 2009
- Full Text
- View/download PDF
146. Designing a fuzzy expert system of diagnosing the hepatitis B intensity rate and comparing it with adaptive neural network fuzzy system
- Author
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WCECS 2009: World Congress on Engineering and Computer Science San Francisco 20/10/2009-22/10/2009, Neshat, Mehdi, and Yaghobi, Mehdi
- Subjects
fuzzy ,neural network ,hepatitis B ,adaptive neural fuzzy system ,expert system - Abstract
In this paper an adaptive neural fuzzy system has been designed for diagnosing the hepatitis B intensity rate. The main problem in determining the disease intensity is not having information about the data variation rate and its resulting effect on the system. Designing a fuzzy expert system and using a neural network for training then testing the system adaptively has resulted in a very good optimization. A Hepatitis B databank has been collected in accordance with the recent medical findings about this disease and the endorsement of a liver specialist. This bank has 300 records and each record has 7fields. This bank has been assembled from patients presenting at the liver biopsy department of Imam Reza hospital Mashad, Iran. Using specialist research and experience strong inference rules have been attained. Thus, the accuracy oft the system in diagnosing the hepatitis B intensity is 96.4± 0.2%. Refereed/Peer-reviewed
- Published
- 2009
147. Designing an expert system of liver disorders by using neural network and comparing it with parametric and nonparametric system
- Author
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M. Yaghobi, Mehdi Neshat, M. Naghibi, Neshat, Mehdi, Yaghobi, Mehdi, Naghibi, Mohammad, and 5th International Multi-Conference on Systems, Signals and Devices, SSD'08 Amman, Jordan 20-22 July 2008
- Subjects
Artificial neural network ,neural network ,parametric method ,business.industry ,Computer science ,medical expert system ,Decision theory ,Bayesian probability ,Nonparametric statistics ,Liver disorder diagnosis ,computer.software_genre ,Machine learning ,Expert system ,Liver disorder ,liver disorder ,Bayesian theory ,Artificial intelligence ,Data mining ,business ,computer ,Parametric statistics - Abstract
In this essay, we are going to design a medical expert system by using neural network. In order to test the system, we used the data in the intended bank e.g. BUPA liver disorders. We compare the operation of the system with parametric methods -like Bayesian decision making theory- and non parametric methods. By analysis of the data, we first discovered an undesirable field in the bank which causes to decrease learning rate of the system. When we omitted this field, we concluded very good results. By comparing the three above systems, we concluded that the neural network has the best operation and effect in liver disorder diagnosis. This result has also been improved so far. Refereed/Peer-reviewed
- Published
- 2008
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148. A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO)
- Author
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Ghodrat Sepidname, Mehdi Neshat, Neshat, Mehdi, and Sepidname, Ghodrat
- Subjects
Mathematical optimization ,Optimization problem ,swarm intelligence ,Computer science ,adaptive ,Swarm intelligence ,Swarm behaviour ,QA75.5-76.95 ,Management Science and Operations Research ,Adaptive ,Fuzzy logic ,Computer Science Applications ,Swallow swarm optimization ,Acceleration ,Electronic computers. Computer science ,Convergence (routing) ,Benchmark (computing) ,fuzzy logic ,Multi-swarm optimization ,Swallow Swarm Optimization ,Information Systems - Abstract
In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO) method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO) method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained. Refereed/Peer-reviewed
- Full Text
- View/download PDF
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