Mohaideen abdul kadhar - Academia.edu (original) (raw)
Papers by Mohaideen abdul kadhar
Results in engineering, Jun 1, 2024
International Transactions on Electrical Energy Systems
The design of finite impulse response (FIR) filters involves the estimation of effective filter c... more The design of finite impulse response (FIR) filters involves the estimation of effective filter coefficients, making the designed filter exhibit infinite stopband attenuation and have a flat-shaped passband. The few conventional filter design methods such as impulse response truncation (IRT) and windowing technique exhibit undesirable characteristics owing to the Gibbs phenomenon, thus making them unsuitable for various practical complexities. This research work employs the fuzzy logic-based diversity-controlled self-adaptive differential evolution algorithm (FLDCSaDE) for the design of FIR band stop (BS) and high pass (HP) filters. In order to validate the results of the proposed technique, various population-based evolutionary computing techniques such as the covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), self-adaptive differential evolution (SaDE), and Jaya algorithm have also been applied for determining the effective filter coefficients. T...
Water Practice and Technology
The precise evaluation of the Muskingum model (MM) parameters is quite critical for routing flood... more The precise evaluation of the Muskingum model (MM) parameters is quite critical for routing flood waves for achieving flood control in open channels. The MM is one of the popular techniques adopted for flood routing. Estimation of the MM parameters so as to provide the best fit for the observed and computed flow values is a global optimization problem. Several optimization techniques have been adopted in the past to serve this purpose, but efficient optimization algorithms are needed to overcome the local optima issues and improvement of accuracy. In this paper, the efficiency of three optimization algorithms, namely Jaya, Covariance Matrix Adaption-Evolution Strategy (CMAES) and self-adaptive differential evolution (SaDE), has been assessed in the evaluation of the Muskingum parameters. The sum of the square deviation of the observed outflow and computed outflow (SSQ) is considered an objective in this MM optimization problem. Also, a constraint is proposed in this paper to help th...
2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 2018
Wireless sensor network (WSN) is a group of sensor nodes that provides effective long haul data t... more Wireless sensor network (WSN) is a group of sensor nodes that provides effective long haul data transmission. It can be used for various large scale applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring etc. This work focuses on a comparative analysis for improving the lifetime of Wireless Sensor Network (WSN) and creating an energy efficient communication in WSN. As clustering techniques provides efficient way to reduce energy consumption, this analysis gives various clustering algorithms like to maximize the lifetime of WSN. The energy and life time of Low-Energy Adaptive clustering Hierarchy (LEACH), K-means, Fuzzy C-means algorithms are analyzed based on their characteristics for different scenarios. The simulation results shows that Fuzzy C-Means algorithm has competing performance than LEACH.
Construction and Building Materials, 2010
ABSTRACT The falling weight deflectometer (FWD) is the foremost and widely accepted tool for char... more ABSTRACT The falling weight deflectometer (FWD) is the foremost and widely accepted tool for characterizing the deflection basins of pavements in a non-destructive manner. The FWD pavement deflection data are used to determine the in situ mechanical properties (elastic moduli) of the pavement layers through inverse analysis, a process commonly referred to as backcalculation (B/C). Several B/C methodologies have been proposed over the years, each with individual strengths and weaknesses. Hybrid methods (combining two methods or more) are recently proposed for overcoming problems posed by stand-alone methods, while extracting and compounding the benefits that are individually offered. This paper proposes a novel hybrid strategy that integrates co-variance matrix Adaptation (CMA) evolution strategy, Finite element (FE) modeling with neural networks (NN) non-linear mapping for backcalculation of non-linear, stress dependent pavement layer moduli. The resulting strategy, referred as CMANIA (CMA with neural networks for inverse analysis) is applied for asphalt pavement moduli backcalculation and is compared with a conventional B/C approach. Results demonstrate the superiority of this method in terms of higher accuracy, achieving nearer to global solutions, better computational speed, and robustness in predicting the pavement layer moduli over the conventional methods.
Data Science with Raspberry Pi, 2021
The most important step in data science is to prepare the data. Data preparation is the process o... more The most important step in data science is to prepare the data. Data preparation is the process of cleaning, processing, and transforming the raw data for analysis. From this stage, the errors in the data can be effectively handled by cleaning, identifying the missing values, handling outliers, etc. Hence, this chapter discusses the methodologies used to prepare the data using the Pandas package in Python.
Data Science with Raspberry Pi, 2021
The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invent... more The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invented by the Raspberry Pi Foundation in the United Kingdom to promote basic computer science and electronics among students around the world. Students and tech enthusiasts use the Raspberry Pi to learn programming concepts, build hardware projects and robots, and make artificial intelligence projects. It is also used in industrial applications.
Data Science with Raspberry Pi, 2021
Data Science with Raspberry Pi, 2021
Exploratory data analysis (EDA) is the process of understanding the data by summarizing its chara... more Exploratory data analysis (EDA) is the process of understanding the data by summarizing its characteristics. This step is important before modeling the data for machine learning. From this analysis, the user can extract the information, identify the root cause of any issues in the data, and figure out the steps to initiate any policies for development. In simple terms, this type of analysis explores the data to understand and identify the patterns and trends in it. There is no common method for doing EDA; it depends on the data we are working with. For simplicity in this chapter, we will use common methods and plots for doing EDA.
Data Science with Raspberry Pi, 2021
In the previous chapter, we discussed a number of steps involved in preparing the data for analys... more In the previous chapter, we discussed a number of steps involved in preparing the data for analysis. Before analyzing the data, it is imperative to get to know the nature of data we are dealing with. Visualizing the data may give us some useful insights about the nature of data. These insights, such as patterns in the data, distribution of the data, outliers present in the data, etc., can prove to be handy in determining the methodology to be used for analyzing the data. In addition, visualization can be used at the end of analysis to communicate the findings to the party concerned as conveying the results of analysis through visualization techniques can be more effective than writing pages of textual content explaining the findings. In this chapter, we will learn about some of the basic visualization plots provided by the Matplotlib package of Python and how those plots can be customized to convey the characteristics of different data.
Data Science with Raspberry Pi, 2021
Data is a collection of information in the form of words, numbers, and descriptions about the sub... more Data is a collection of information in the form of words, numbers, and descriptions about the subject. Consider the following statement: “The dog has four legs, is 1.5m high, and has brown hair.” This statement has three different kinds of information (i.e., data) about the dog. The data “four” and “1.5m” is numerical data, and “brown hair” is descriptive. It is good to know the various kinds of data types to understand the data, perform effective analysis, and better extract knowledge from the data. Basically, data can be categorized into two types.
Data Science with Raspberry Pi, 2021
The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invent... more The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invented by the Raspberry Pi Foundation in the United Kingdom to promote basic computer science and electronics among students around the world. Students and tech enthusiasts use the Raspberry Pi to learn programming concepts, build hardware projects and robots, and make artificial intelligence projects. It is also used in industrial applications.
Data Science with Raspberry Pi, 2021
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol ... more Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
Data Science with Raspberry Pi, 2021
Data Science with Raspberry Pi, 2021
2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020
Microstrip patch antenna has immense benefit in mobile satellite communication, WLAN's, GPS s... more Microstrip patch antenna has immense benefit in mobile satellite communication, WLAN's, GPS system, missiles, biomedical telemetry etc., due to its key features like less weight, ease of construction and fabrication, low cost etc., In this paper, a Compact Rectangular Microstrip Patch Antenna is designed with inset feed and the size parameters are optimized using Particle Swarm Optimization (PSO) to achieve minimum return loss with better size miniaturization. The proposed antenna possesses minimum area consumption of 435 mm2 with return loss −30 dB and gain of 10 dB. Radiator is laid over low cost FR4 substrate material with mathrmC!!!!scriptstyle=mathrmr\mathrm{C}\!\!\!\!{\scriptstyle{{}^=}}_{\mathrm{r}}mathrmC!!!!scriptstyle=mathrmr of 4.4 with thickness, t of 1.57 mm. The antenna parameters such as return loss, current distribution, impedance, radiation pattern and directivity of the proposed microstrip patch antenna are also discussed. Results demonstrates that the proposed antenna possess minimum return loss of 57.8% and better size miniaturization of 58% by resonating at 2.45 GHz frequency (i.e. ISM band). Thereby, it is suitable for biomedical applications.
International Journal of Fuzzy Systems, 2017
This paper presents an application of evolutionary algorithm techniques for optimal nonlinear con... more This paper presents an application of evolutionary algorithm techniques for optimal nonlinear controller design in drum-type boiler-turbine system. Designing of controller for third-order boiler-turbine system is always a complicated task due to the presence of highly interactive nonlinearities. The present work is the first one which attempts to design and implement recently developed finite time convergent controller in third-order boiler-turbine dynamics, and it is described as multi-inputmulti-output (MIMO) nonlinear system. The present work explores the possibility of application of single-objective and multi-objective evolutionary algorithm techniques toward optimal tuning of finite time convergent controller to achieve the desired performance for third-order boilerturbine system. The single-objective evolutionary algorithm techniques such as real-coded genetic algorithm (RGA), modified particle swarm optimization (MPSO), differential evolution (DE), and self-adaptive differential evolution (SADE) are implemented with the minimization of integral square error (ISE) as an objective to obtain optimal tuning parameters. Also, the present paper explores the possibility of simultaneous minimization of conflicting objectives such as ISE and computational cost of the proposed controller using multi-objective evolutionary algorithms such as non-dominated sorting genetic algorithm (NSGA) and modified non-dominated sorting genetic algorithm-II (MNSGA-II). The performance of the proposed optimal finite time convergent controller is validated by simulating different kinds of set point changes, and the obtained results are presented as various case studies. The adaptability of the proposed controller during parameter variations is also examined. The performance of the singleobjective and multi-objective evolutionary algorithms has been statistically analyzed, and the results are reported. The results reveal that among the four single-objective EA techniques, SADE offers better performance due to its inherit self-adaptive capability. Also, during multi-objective optimization, MNSGA-II has provided better solution due the presence of dynamic crowding distance (DCD) and control elitism (CE) strategies. Keywords Boiler-turbine system Á Finite time convergent controller Á Single-objective and multi-objective evolutionary algorithms Á Optimal tuning Int. J. Fuzzy Syst.
IETE Journal of Research, 2017
ABSTRACT This paper investigates an optimized design of newly developed nonlinear controller call... more ABSTRACT This paper investigates an optimized design of newly developed nonlinear controller called finite-time convergent controller to a third-order boiler–turbine dynamics. The third-order boiler–turbine dynamics only includes highly nonlinear and critical parameters of thermal plant like drum pressure, electric power and drum level; the design of controller is always a complicated task. The present work explores the possibility of application of newly developed finite-time convergent controller to a third-order boiler–turbine dynamics. The novelty of the newly developed finite-time convergent controller is complete utilization of system nonlinearities instead of cancelling or linearizing them. Also, the finite-time convergent controller ensures robustness and fast convergence. To achieve optimal performance, the tuning parameters involved in finite-time convergent controller have been optimized using evolutionary algorithm techniques. To validate the control performance of an optimized nonlinear controller design, simulations have been conducted using various evolutionary algorithm techniques and the results are reported as various case studies. To compare the performance of proposed optimized finite-time convergent controller, the fuzzy logic controller has also been designed using ANFIS for boiler–turbine system and the results are reported.
Soft Computing, 2016
This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary... more This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms (MOEA/Ds) to reduce the unnecessary usage of computational resource. In MOEA/D, a multi-objective problem is decomposed into a number of single-objective subproblems using a Tchebycheff decomposition approach. Then, optimal Pareto front (PF) is obtained by optimizing the Tchebycheff objective of all the subproblems. The proposed stopping criterion monitors the variations of Tchebycheff objective at every generation using maximum Tchebycheff objective error (MTOE) of all the subproblems and stops the algorithm, when there is no significant improvement in MTOE. χ 2 test is used for statistically verifying the significant changes of MTOE for every γ generations. The proposed stopping criterion is implemented in a recently constrained MOEA/D variant, namely CMOEA/D-CDP, and a simulation study is conducted with the constrained test instances for choosing a suitable tolerance value for the MTOE stopping criterion. A comparison with the recent stopping methods demonstrates that the proposed MTOE stopping criterion is simple and has minimum computational complexity. Moreover, the MTOE stopping criterion is tested on real-world Communicated by V. Loia.
Results in engineering, Jun 1, 2024
International Transactions on Electrical Energy Systems
The design of finite impulse response (FIR) filters involves the estimation of effective filter c... more The design of finite impulse response (FIR) filters involves the estimation of effective filter coefficients, making the designed filter exhibit infinite stopband attenuation and have a flat-shaped passband. The few conventional filter design methods such as impulse response truncation (IRT) and windowing technique exhibit undesirable characteristics owing to the Gibbs phenomenon, thus making them unsuitable for various practical complexities. This research work employs the fuzzy logic-based diversity-controlled self-adaptive differential evolution algorithm (FLDCSaDE) for the design of FIR band stop (BS) and high pass (HP) filters. In order to validate the results of the proposed technique, various population-based evolutionary computing techniques such as the covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), self-adaptive differential evolution (SaDE), and Jaya algorithm have also been applied for determining the effective filter coefficients. T...
Water Practice and Technology
The precise evaluation of the Muskingum model (MM) parameters is quite critical for routing flood... more The precise evaluation of the Muskingum model (MM) parameters is quite critical for routing flood waves for achieving flood control in open channels. The MM is one of the popular techniques adopted for flood routing. Estimation of the MM parameters so as to provide the best fit for the observed and computed flow values is a global optimization problem. Several optimization techniques have been adopted in the past to serve this purpose, but efficient optimization algorithms are needed to overcome the local optima issues and improvement of accuracy. In this paper, the efficiency of three optimization algorithms, namely Jaya, Covariance Matrix Adaption-Evolution Strategy (CMAES) and self-adaptive differential evolution (SaDE), has been assessed in the evaluation of the Muskingum parameters. The sum of the square deviation of the observed outflow and computed outflow (SSQ) is considered an objective in this MM optimization problem. Also, a constraint is proposed in this paper to help th...
2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 2018
Wireless sensor network (WSN) is a group of sensor nodes that provides effective long haul data t... more Wireless sensor network (WSN) is a group of sensor nodes that provides effective long haul data transmission. It can be used for various large scale applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring etc. This work focuses on a comparative analysis for improving the lifetime of Wireless Sensor Network (WSN) and creating an energy efficient communication in WSN. As clustering techniques provides efficient way to reduce energy consumption, this analysis gives various clustering algorithms like to maximize the lifetime of WSN. The energy and life time of Low-Energy Adaptive clustering Hierarchy (LEACH), K-means, Fuzzy C-means algorithms are analyzed based on their characteristics for different scenarios. The simulation results shows that Fuzzy C-Means algorithm has competing performance than LEACH.
Construction and Building Materials, 2010
ABSTRACT The falling weight deflectometer (FWD) is the foremost and widely accepted tool for char... more ABSTRACT The falling weight deflectometer (FWD) is the foremost and widely accepted tool for characterizing the deflection basins of pavements in a non-destructive manner. The FWD pavement deflection data are used to determine the in situ mechanical properties (elastic moduli) of the pavement layers through inverse analysis, a process commonly referred to as backcalculation (B/C). Several B/C methodologies have been proposed over the years, each with individual strengths and weaknesses. Hybrid methods (combining two methods or more) are recently proposed for overcoming problems posed by stand-alone methods, while extracting and compounding the benefits that are individually offered. This paper proposes a novel hybrid strategy that integrates co-variance matrix Adaptation (CMA) evolution strategy, Finite element (FE) modeling with neural networks (NN) non-linear mapping for backcalculation of non-linear, stress dependent pavement layer moduli. The resulting strategy, referred as CMANIA (CMA with neural networks for inverse analysis) is applied for asphalt pavement moduli backcalculation and is compared with a conventional B/C approach. Results demonstrate the superiority of this method in terms of higher accuracy, achieving nearer to global solutions, better computational speed, and robustness in predicting the pavement layer moduli over the conventional methods.
Data Science with Raspberry Pi, 2021
The most important step in data science is to prepare the data. Data preparation is the process o... more The most important step in data science is to prepare the data. Data preparation is the process of cleaning, processing, and transforming the raw data for analysis. From this stage, the errors in the data can be effectively handled by cleaning, identifying the missing values, handling outliers, etc. Hence, this chapter discusses the methodologies used to prepare the data using the Pandas package in Python.
Data Science with Raspberry Pi, 2021
The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invent... more The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invented by the Raspberry Pi Foundation in the United Kingdom to promote basic computer science and electronics among students around the world. Students and tech enthusiasts use the Raspberry Pi to learn programming concepts, build hardware projects and robots, and make artificial intelligence projects. It is also used in industrial applications.
Data Science with Raspberry Pi, 2021
Data Science with Raspberry Pi, 2021
Exploratory data analysis (EDA) is the process of understanding the data by summarizing its chara... more Exploratory data analysis (EDA) is the process of understanding the data by summarizing its characteristics. This step is important before modeling the data for machine learning. From this analysis, the user can extract the information, identify the root cause of any issues in the data, and figure out the steps to initiate any policies for development. In simple terms, this type of analysis explores the data to understand and identify the patterns and trends in it. There is no common method for doing EDA; it depends on the data we are working with. For simplicity in this chapter, we will use common methods and plots for doing EDA.
Data Science with Raspberry Pi, 2021
In the previous chapter, we discussed a number of steps involved in preparing the data for analys... more In the previous chapter, we discussed a number of steps involved in preparing the data for analysis. Before analyzing the data, it is imperative to get to know the nature of data we are dealing with. Visualizing the data may give us some useful insights about the nature of data. These insights, such as patterns in the data, distribution of the data, outliers present in the data, etc., can prove to be handy in determining the methodology to be used for analyzing the data. In addition, visualization can be used at the end of analysis to communicate the findings to the party concerned as conveying the results of analysis through visualization techniques can be more effective than writing pages of textual content explaining the findings. In this chapter, we will learn about some of the basic visualization plots provided by the Matplotlib package of Python and how those plots can be customized to convey the characteristics of different data.
Data Science with Raspberry Pi, 2021
Data is a collection of information in the form of words, numbers, and descriptions about the sub... more Data is a collection of information in the form of words, numbers, and descriptions about the subject. Consider the following statement: “The dog has four legs, is 1.5m high, and has brown hair.” This statement has three different kinds of information (i.e., data) about the dog. The data “four” and “1.5m” is numerical data, and “brown hair” is descriptive. It is good to know the various kinds of data types to understand the data, perform effective analysis, and better extract knowledge from the data. Basically, data can be categorized into two types.
Data Science with Raspberry Pi, 2021
The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invent... more The Raspberry Pi, or simply the Pi, is a series of small, low-cost, single-board computers invented by the Raspberry Pi Foundation in the United Kingdom to promote basic computer science and electronics among students around the world. Students and tech enthusiasts use the Raspberry Pi to learn programming concepts, build hardware projects and robots, and make artificial intelligence projects. It is also used in industrial applications.
Data Science with Raspberry Pi, 2021
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol ... more Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
Data Science with Raspberry Pi, 2021
Data Science with Raspberry Pi, 2021
2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020
Microstrip patch antenna has immense benefit in mobile satellite communication, WLAN's, GPS s... more Microstrip patch antenna has immense benefit in mobile satellite communication, WLAN's, GPS system, missiles, biomedical telemetry etc., due to its key features like less weight, ease of construction and fabrication, low cost etc., In this paper, a Compact Rectangular Microstrip Patch Antenna is designed with inset feed and the size parameters are optimized using Particle Swarm Optimization (PSO) to achieve minimum return loss with better size miniaturization. The proposed antenna possesses minimum area consumption of 435 mm2 with return loss −30 dB and gain of 10 dB. Radiator is laid over low cost FR4 substrate material with mathrmC!!!!scriptstyle=mathrmr\mathrm{C}\!\!\!\!{\scriptstyle{{}^=}}_{\mathrm{r}}mathrmC!!!!scriptstyle=mathrmr of 4.4 with thickness, t of 1.57 mm. The antenna parameters such as return loss, current distribution, impedance, radiation pattern and directivity of the proposed microstrip patch antenna are also discussed. Results demonstrates that the proposed antenna possess minimum return loss of 57.8% and better size miniaturization of 58% by resonating at 2.45 GHz frequency (i.e. ISM band). Thereby, it is suitable for biomedical applications.
International Journal of Fuzzy Systems, 2017
This paper presents an application of evolutionary algorithm techniques for optimal nonlinear con... more This paper presents an application of evolutionary algorithm techniques for optimal nonlinear controller design in drum-type boiler-turbine system. Designing of controller for third-order boiler-turbine system is always a complicated task due to the presence of highly interactive nonlinearities. The present work is the first one which attempts to design and implement recently developed finite time convergent controller in third-order boiler-turbine dynamics, and it is described as multi-inputmulti-output (MIMO) nonlinear system. The present work explores the possibility of application of single-objective and multi-objective evolutionary algorithm techniques toward optimal tuning of finite time convergent controller to achieve the desired performance for third-order boilerturbine system. The single-objective evolutionary algorithm techniques such as real-coded genetic algorithm (RGA), modified particle swarm optimization (MPSO), differential evolution (DE), and self-adaptive differential evolution (SADE) are implemented with the minimization of integral square error (ISE) as an objective to obtain optimal tuning parameters. Also, the present paper explores the possibility of simultaneous minimization of conflicting objectives such as ISE and computational cost of the proposed controller using multi-objective evolutionary algorithms such as non-dominated sorting genetic algorithm (NSGA) and modified non-dominated sorting genetic algorithm-II (MNSGA-II). The performance of the proposed optimal finite time convergent controller is validated by simulating different kinds of set point changes, and the obtained results are presented as various case studies. The adaptability of the proposed controller during parameter variations is also examined. The performance of the singleobjective and multi-objective evolutionary algorithms has been statistically analyzed, and the results are reported. The results reveal that among the four single-objective EA techniques, SADE offers better performance due to its inherit self-adaptive capability. Also, during multi-objective optimization, MNSGA-II has provided better solution due the presence of dynamic crowding distance (DCD) and control elitism (CE) strategies. Keywords Boiler-turbine system Á Finite time convergent controller Á Single-objective and multi-objective evolutionary algorithms Á Optimal tuning Int. J. Fuzzy Syst.
IETE Journal of Research, 2017
ABSTRACT This paper investigates an optimized design of newly developed nonlinear controller call... more ABSTRACT This paper investigates an optimized design of newly developed nonlinear controller called finite-time convergent controller to a third-order boiler–turbine dynamics. The third-order boiler–turbine dynamics only includes highly nonlinear and critical parameters of thermal plant like drum pressure, electric power and drum level; the design of controller is always a complicated task. The present work explores the possibility of application of newly developed finite-time convergent controller to a third-order boiler–turbine dynamics. The novelty of the newly developed finite-time convergent controller is complete utilization of system nonlinearities instead of cancelling or linearizing them. Also, the finite-time convergent controller ensures robustness and fast convergence. To achieve optimal performance, the tuning parameters involved in finite-time convergent controller have been optimized using evolutionary algorithm techniques. To validate the control performance of an optimized nonlinear controller design, simulations have been conducted using various evolutionary algorithm techniques and the results are reported as various case studies. To compare the performance of proposed optimized finite-time convergent controller, the fuzzy logic controller has also been designed using ANFIS for boiler–turbine system and the results are reported.
Soft Computing, 2016
This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary... more This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms (MOEA/Ds) to reduce the unnecessary usage of computational resource. In MOEA/D, a multi-objective problem is decomposed into a number of single-objective subproblems using a Tchebycheff decomposition approach. Then, optimal Pareto front (PF) is obtained by optimizing the Tchebycheff objective of all the subproblems. The proposed stopping criterion monitors the variations of Tchebycheff objective at every generation using maximum Tchebycheff objective error (MTOE) of all the subproblems and stops the algorithm, when there is no significant improvement in MTOE. χ 2 test is used for statistically verifying the significant changes of MTOE for every γ generations. The proposed stopping criterion is implemented in a recently constrained MOEA/D variant, namely CMOEA/D-CDP, and a simulation study is conducted with the constrained test instances for choosing a suitable tolerance value for the MTOE stopping criterion. A comparison with the recent stopping methods demonstrates that the proposed MTOE stopping criterion is simple and has minimum computational complexity. Moreover, the MTOE stopping criterion is tested on real-world Communicated by V. Loia.