Non-laminated glass fiber-reinforced epoxy composites (GFREC) have shown promising applications in various engineering fields. In this study, an experimental investigation followed by artificial intelligent modeling i...
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Non-laminated glass fiber-reinforced epoxy composites (GFREC) have shown promising applications in various engineering fields. In this study, an experimental investigation followed by artificial intelligent modeling is carried out on the drilling of GFREC under two cooling conditions, namely internal and external. The damage factor of the drilled holes and the temperature of the drill tip were considered as main process responses. The ex-periments were conducted under different combinations of feed, spindle speed and coolant pressure. All holes were drilled using fresh tungsten carbide twist drills coated by TiN/TiAlN layer. A fine-tuned random vector functional link network (RVFL) model incor-porated with a new optimizer called parasitism-predation algorithm (PPA) was developed to model the drilling process. PPA acts as an advanced metaheuristic optimizer to obtain the best model variables of RVFL. The developed model was compared with the stand-alone RVFL as well as the fined-tuned RVFL using particle swarm optimizer (PSO). RVFL-PPA outperformed other models in terms of coefficient of determination, root mean square error and other measures. The coefficient of determination of RVFL-PPA for different responses and cooling conditions ranges between 0.995 and 1. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
The process of constructing a reliable mathematical model of solid oxide fuel cell (SOFC) is a challenge due to its complex nature. This paper proposes a new methodology incorporated a recent meta-heuristic algorithm ...
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The process of constructing a reliable mathematical model of solid oxide fuel cell (SOFC) is a challenge due to its complex nature. This paper proposes a new methodology incorporated a recent meta-heuristic algorithm named parasitism-predation algorithm (PPA) to estimate the optimal parameters of SOFC equivalent circuit. Two experiments are conducted in this work;the first one comprises four measured datasets for a commercial enhanced cylindrical SOFC manufactured by Siemen Energy. While the second series consists of five measured datasets for a theoretical 5oKWTHORN dynamic SOFC stack with 96 connected cells. The collected datasets are measured at different operating conditions. An excessive comparative study is presented with other optimizers of comprehensive learning particle swarm optimization (CLPSO), improved PSO with difference mean with perturbation (DMP_PSO), heterogeneous CLPSO (HCLPSO), locally informed PSO (LIPS), modified CSO with tri-competitive mechanism (MCSO), opposition-based learning competitive PSO (OBLCPSO), ranking-based biased learning swarm optimizer (RBLSO), competitive swarm optimizer (CSO), hybrid Jaya with DE (JayaDE), and social learning PSO (SLPSO). Furthermore, statistical analyses of the ranking tests, multiple sign tests, Friedman tests, and ANOVA are performed. The obtained results confirmed the proposed PPA's competence in constructing a reliable model of SOFC as it provides the least mean square error (MSE) between the measured and estimated characteristics of 2.164e(-6) in the first series of experiments at 1073 K, in contrast, the most peer (CLPSO) provides 5.57e-6. Similarly, in the second series of experiments, PPA achieves lease MSE of 7.17e-2 at 973 K;meanwhile, the most peer (CLPSO) attains 5.44e(-1).
In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhanc...
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In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn't guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent meta heuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm. (c) 2021 Elsevier Ltd. All rights reserved.
Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. "Classification ...
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Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. "Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy." This work presents a new meta-heuristic optimization approach, called parasitism-predation algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow-cuckoo-cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options. (C) 2019 The Authors. Published by Elsevier B.V.
the electromechanical industries rely heavily on AC induction or asynchronous motors. However, choosing a suitable engine for specific drive applications is an uncompromising task. The use of an equivalent T-circuit f...
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ISBN:
(纸本)9781665408738
the electromechanical industries rely heavily on AC induction or asynchronous motors. However, choosing a suitable engine for specific drive applications is an uncompromising task. The use of an equivalent T-circuit for this purpose is one of the most universally accepted and effective methods. The estimation of the equivalent circuit parameter must be done relatively quickly and accurately. This paper used a parasitism-predation algorithm (PPA) as a new approach techniques method for calculating the equivalent circuit parameters of two induction motors. The variance between computed data and manufacturer data (objective functions) is reduced to a minimum which has been considered in the torque (starting torque, maximum torque and full load torque) and full load power factor. The results referred to that the PPA method is superior because it has a less deviation than the results obtained with other recent techniques.
Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. In the present paper, a novel hybrid algorithm based on Arithmetic optimizati...
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Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. In the present paper, a novel hybrid algorithm based on Arithmetic optimization algorithm (AOA) and Artificial Gorilla troop's optimization (GTO), (HAOAGTO) is compared with different four algorithms Arithmetic optimization algorithm (AOA), Artificial Gorilla troop's optimization (GTO), Seagull optimization algorithm (SOA), parasitism-predation algorithm (PPA). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model. It is observed that the PPA technique achieves the highest values of best fitness value for simulation results among other control approaches, while the HAOAGTO approach reduces the best fitness function compared to other optimization techniques used. We verified that the obtained results by application of the proposed hybrid algorithm-based AOA and GTO (HAOAGTO) is better than those obtained by Arithmetic optimization algorithm (AOA), Artificial Gorilla troop's optimization (GTO), Seagull optimization algorithm (SOA), parasitism-predation algorithm (PPA). it is implemented to obtain the optimal parameters of the PID for reduction the ISTES.
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