Robiah Ahmad | Universiti Teknologi Malaysia - UTM - Academia.edu

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Robiah Ahmad

Universiti Teknologi Malaysia - UTM, Razak School of Engineering & Advanced Technology, Faculty Member

Universiti Teknologi Malaysia - UTM, Razak School of Engineering & Advanced Technology, Faculty Member |

Precision Agriculture

Address: UTM Razak School of Engineering and Advanced Technology, University Teknologi Malaysia Kuala Lumpur

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81 Followers | 14 Following | 12 Co-authors | —Total Views

Papers

Cryptography and image steganography using dynamic encryption on LSB and color image based data hiding

Cryptography and steganography are two conventional techniques used to cipher or hide information... more Cryptography and steganography are two conventional techniques used to cipher or hide informationor data in existing communication. Cryptography is the art of saving information by encrypting it into anobscure format. On the other hand, steganography is the art and science of secret communication to sendmessages in a way which hides even the existence of the communication. In cryptography method, the messagesends in an incomprehensible form in order to only the recipient can remove the disguise to extract and readthe encrypted text. Conversely, in steganographic systems, the data embedded into a harmless digital mediafile like image, video, or audio and the file transmitted. Although cryptography and steganography provide anacceptable level of security when used separately via communicating in the unreliable medium like the Internet,advancesin steganalysis make it a constantly evolving field. This paper aims to improve a new scheme of hiding a secret message in an image, by exploiti...

© Universiti Teknologi Malaysia RADIAL BASIS FUNCTION (RBF) FOR NON-LINEAR DYNAMIC SYSTEM IDENTIFICATION

Abstract. One of the key problem in system identification is finding a suitable model structure. ... more Abstract. One of the key problem in system identification is finding a suitable model structure. In this paper, radial basis function (RBF) network using various basis functions are trained to represent discrete-time nonlinear dynamic systems and the results are compared. The orthogonal least square algorithm is employed to select parsimonious RBF models. To demonstrate the identification proce-dure, two examples of modelling nonlinear system were included.

Extreme learning machine based sub-key generation for cryptography system

The key generation process is the substantial step in any cryptosystem. Incorporating Artificial ... more The key generation process is the substantial step in any cryptosystem. Incorporating Artificial Neural Network (ANN) in the algorithmic work of cryptography achieves good performance in realizing high accuracy and security. In this paper, ANN based sub-key generation algorithm is presented. Extreme learning Machine (ELM) type is adopted for one hidden layer neural network. Initial key includes all needed information about ANN topology, activation function, and seeds for Pseudo-Random Number Generation (PRNG) in each round to initialize input-hidden layer weights and data. Sub-key in each round is generated from output layer weights. Evaluation measures have proved complete sensitivity and inevitability of this approach. In addition, it contributes in reducing the risks of breaking the symmetric key algorithms due to the generated independent sub-key in each round. Thus, it can be integrated in any cryptosystem for subkey generation.

Morphological and Physiological Responses of Brassica chinensis on Different Far-Red (FR) Light Treatments Using Internet-of-Things (IoT) Technology

Advanced technology in agriculture has enabled the manipulation of the artificial light spectrum ... more Advanced technology in agriculture has enabled the manipulation of the artificial light spectrum in plant development such as improving yield and plant growth. Light manipulation using light-emitting diodes or LEDs can inhibit, delay, or even promote flowering. Some studies have shown that far-red (FR) light can stop flowering, but studies have not fully explored the best method involving intensity and duration to induce plant growth. This paper presents results on LED light manipulation techniques, particularly FR light, on plant flowering control and plant elongation. The light manipulation technique on the combination of colors, photoperiods, and intensities proved that it can stop flowering, and stimulate and control the growth of plants during cultivation. The system was monitored using an Internet-of-Things (IoT) remote monitoring system, and it performed data mining. The results showed that plants that were grown under artificial sunlight (T5) and normal light (T1) treatments...

Advanced encryption standard algorithm versus extreme learning machine based weight: a comparative study

ARPN journal of engineering and applied sciences, 2017

Advanced Encryption Standard (AES) is beingwidely used ciphering/deciphering system has emerged a... more Advanced Encryption Standard (AES) is beingwidely used ciphering/deciphering system has emerged asa standard benchmark. Due to rapid advancement in the hardware specifications, thearchitecture security of AES became a major concern. Furthermore, the newly developed machine learning dependent encryption architecture called Extreme Learning Machine Based Weight (ELMWi) appears more suitable for sundry cryptographic implementations. This article compares the performance of ELMWi with AESvia statistical evaluation, where the parameters such as sensitivity, visual imperceptibility metrics, and key space are determined. Results reveal their similar performances. It is further argued that ELMWi outperforms the AES in perspective of architecture implementation.

A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem

Applied Sciences, 2018

Firefly algorithm is among the nature-inspired optimization algorithms. The standard firefly algo... more Firefly algorithm is among the nature-inspired optimization algorithms. The standard firefly algorithm has been successfully applied to many engineering problems. However, this algorithm might be stuck in stagnation (the solutions do not enhance anymore) or possibly fall in premature convergence (fall in to the local optimum) in searching space. It seems that both issues could be connected to the exploitation and exploration. Excessive exploitation leads to premature convergence, while excessive exploration slows down the convergence. In this study, the classical firefly algorithm is modified such that make a balance between exploitation and exploration. The purposed modified algorithm ranks and sorts the initial solutions. Next, the operators named insertion, swap and reversion are utilized to search the neighbourhood of solutions in the second group, in which all these operators are chosen randomly. After that, the acquired solutions combined with the first group and the firefly a...

Improved Internet of Things (IoT) monitoring system for growth optimization of Brassica chinensis

Computers and Electronics in Agriculture, 2019

Abstract The Internet of Things (IoT) has been integrated in various applications such as smart h... more Abstract The Internet of Things (IoT) has been integrated in various applications such as smart homes and smart cities. IoT in agriculture such as in monitoring indoor climatic conditions can help to improve the plant growth. This paper proposes a new approach in utilizing IoT technology as a remote monitoring system to control the indoor climatic conditions via light emitting diode (LED) parameters which include spectrums, photoperiod and intensity in order to increase yields as well as to reduce the turnaround time. This study showed that growth of Brassica chinensis under different wavelengths of light source has influenced plant growth performance and phytochemicals characteristics. Four different light treatments were experimented using pulse treatment (1 h light and 1 h dark), continuous light (CL), high intensity and artificial light control of 12 h light and 12 h dark. Data such as the leaves count, height, dry weight and chlorophyll a & b of plants were analyzed. The results showed high mean value of plants’ fresh weight (FW) of 410.77 g under pulse treatment compared to other light treatments. The percentage of moisture content (MC) was recorded higher on average under normal light (99.15%), value of leaf area (LA) was recorded higher under artificial sunlight with an average value of 976.84 cm2. Even though the results of LA were better under artificial sunlight, CL with low intensity gave higher stem height (SH) and number of leaves (NOL). In order to capture real-time data and monitor the environmental parameters of the plant experiment, an intelligent embedded system was developed to automate the LED control and manipulation. The results showed that the system is stable and has significant referential in the area of plant factory or indoor farming.

NARMAX model identification of a palm oil biodiesel engine using multi-objective optimization differential evolution

AIP Conference Proceedings, 2017

Optimizing a Just-In-Time logistics network problem under fuzzy supply and demand: two parameter-tuned metaheuristics algorithms

Neural Computing and Applications, 2017

Extreme learning machine based optimal embedding location finder for image steganography

PLOS ONE, 2017

A novel extreme learning machine-based cryptography system

Security and Communication Networks, 2016

A literature review on green supply chain modelling for optimising CO<SUB align="right">2 emission

International Journal of Operational Research, 2016

Carbon-capped Distribution Planning: A JIT Perspective

Computers & Industrial Engineering, 2016

We use a hybrid NSGA-II to obtain the near Pareto front solutions in a Just-In-Time distribution ... more We use a hybrid NSGA-II to obtain the near Pareto front solutions in a Just-In-Time distribution network.We investigate different carbon constraints namely periodic, cumulative and global.We present the complexity analysis for the proposed model.We analyze the solution approach as well as identify some managerial and policy insights. Products distribution and transportation is one of the largest sources of CO2 emission in supply chains. To date, a number of researchers have argued that intensive transportation activities through popular distribution strategies such as Just-In-Time (JIT) could significantly increase carbon emissions within logistics chains. However, a systematic understanding of how JIT distribution affects carbon emissions is still lacking in current literature. In this study, we develop a bi-objective optimization model for a carbon-capped JIT distribution of multiple products in a multi-period and multi-echelon distribution network. The aims are to jointly minimize total logistics cost and to minimize the maximum carbon quota allowed per period (carbon cap). The considered problem is investigated under three different carbon emission constraints namely periodic, cumulative and global. Since the studied problem is NP-Hard, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed and its parameters are tuned by Taguchi method. For further quality improvement of the developed solution approach, a novel local search approach called modified firefly algorithm incorporates NSGA-II. Different sizes of the problem are considered to compare the performances of the proposed hybrid NSGA-II and the classical one. Finally, the results are presented along with some policy and managerial insights. For policy makers, the findings show the impact of varying the carbon emission cap on total cost and total emissions under JIT distribution concept. From managerial perspectives, we analyze the relationships between average inventory holding and backlog level per period which can assist mangers to identify critical decisions for JIT distribution of products in carbon-capped environment.

A tuned NSGA-II to optimize the total cost and service level for a just-in-time distribution network

Neural Computing and Applications, 2016

Distribution network planning has attracted the attention of many studies during last decades. Ju... more Distribution network planning has attracted the attention of many studies during last decades. Just-in-time (JIT) distribution has a key role in efficient delivery of products within distribution networks. In modeling of JIT distribution networks, the most frequently applied objectives are related to cost and service level. However, evaluating the impact of simultaneously minimizing total costs and balance between distribution network entities in different echelons still rarely complies with the current literature. To remedy this shortcoming and model reality more accurately, this paper develops a multi-objective mixed-integer nonlinear optimization model for a JIT distribution in three-echelon distribution network. The aims are minimization of total logistics cost along with maximization of capacity utilization balance for distribution centers and manufacturing plants. A non-dominated sorting genetic algorithm-II (NSGA-II) with three different mutation operators namely swap, reversion and insertion is employed to provide a set of near-optimal Pareto solutions. Then, the provided solutions are verified with non-dominated ranked genetic algorithm (NRGA) as well. The Taguchi method in design of experiments tunes the parameters of both algorithms, and their performances are then compared in terms of some multi-objective performance measures. In addition, a genetic algorithm is used to assess Pareto optimal solutions of NSGA-II. Different problems with different sizes are considered to compare the performance of the suggested algorithms. The results show that the proposed solution approach performs efficiently. Finally, the conclusion and some directions for future research are proposed.

Identification and modeling of environment climates inside naturally ventilated tropical greenhouse

Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification

Development of hand-arm model rig for tremor excitation

2008 International Symposium on Information Technology, 2008

Production Planning and Inventory Control in Automotive Supply Chain Networks

Lecture Notes in Computer Science, 2014

ABSTRACT his paper addresses a non-linear optimization model by integrating production planning a... more ABSTRACT his paper addresses a non-linear optimization model by integrating production planning and inventory control in the automotive industry at the strategic and operational level. In order to provide an effective modeling, we developed a framework to integrate manufacturing system and suppliers within an automotive supply chain network. The numerical experiments demonstrate the efficiency of the proposed model on minimization of total delivery cost and due date delivery.

Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 2007

Multiobjective Evolutionary Algorithm Approach in Modeling Discrete-Time Multivariable Dynamics Systems

2010 Second International Conference on Computational Intelligence, Modelling and Simulation, 2010

ABSTRACT Multiobjective evolutionary algorithms are robust tool in solving many optimization prob... more ABSTRACT Multiobjective evolutionary algorithms are robust tool in solving many optimization problems. Model structure selection is a procedure in system identification procedures. This procedure counters two contradicting objective functions which are minimizing mean square error and complexity of the selected model. This paper investigates the effectiveness and the performance of multiobjective evolutionary algorithm using elitist nondominated sorting genetic algorithm (NSGA-II) in identifying the model structure for discrete-time multivariable dynamic systems. Two simulated multivariable systems and a real multivariable system, which is a jacketed continuous stirred tank reactor, were used to investigate the effectiveness of NSGA-II. The identified model is validated using one-step-ahead prediction. The results indicate that NSGA-II is able to optimize the model structure of the multivariable systems with good predictive accuracy and adequate model structure.

Cryptography and image steganography using dynamic encryption on LSB and color image based data hiding

Cryptography and steganography are two conventional techniques used to cipher or hide information... more Cryptography and steganography are two conventional techniques used to cipher or hide informationor data in existing communication. Cryptography is the art of saving information by encrypting it into anobscure format. On the other hand, steganography is the art and science of secret communication to sendmessages in a way which hides even the existence of the communication. In cryptography method, the messagesends in an incomprehensible form in order to only the recipient can remove the disguise to extract and readthe encrypted text. Conversely, in steganographic systems, the data embedded into a harmless digital mediafile like image, video, or audio and the file transmitted. Although cryptography and steganography provide anacceptable level of security when used separately via communicating in the unreliable medium like the Internet,advancesin steganalysis make it a constantly evolving field. This paper aims to improve a new scheme of hiding a secret message in an image, by exploiti...

© Universiti Teknologi Malaysia RADIAL BASIS FUNCTION (RBF) FOR NON-LINEAR DYNAMIC SYSTEM IDENTIFICATION

Abstract. One of the key problem in system identification is finding a suitable model structure. ... more Abstract. One of the key problem in system identification is finding a suitable model structure. In this paper, radial basis function (RBF) network using various basis functions are trained to represent discrete-time nonlinear dynamic systems and the results are compared. The orthogonal least square algorithm is employed to select parsimonious RBF models. To demonstrate the identification proce-dure, two examples of modelling nonlinear system were included.

Extreme learning machine based sub-key generation for cryptography system

The key generation process is the substantial step in any cryptosystem. Incorporating Artificial ... more The key generation process is the substantial step in any cryptosystem. Incorporating Artificial Neural Network (ANN) in the algorithmic work of cryptography achieves good performance in realizing high accuracy and security. In this paper, ANN based sub-key generation algorithm is presented. Extreme learning Machine (ELM) type is adopted for one hidden layer neural network. Initial key includes all needed information about ANN topology, activation function, and seeds for Pseudo-Random Number Generation (PRNG) in each round to initialize input-hidden layer weights and data. Sub-key in each round is generated from output layer weights. Evaluation measures have proved complete sensitivity and inevitability of this approach. In addition, it contributes in reducing the risks of breaking the symmetric key algorithms due to the generated independent sub-key in each round. Thus, it can be integrated in any cryptosystem for subkey generation.

Morphological and Physiological Responses of Brassica chinensis on Different Far-Red (FR) Light Treatments Using Internet-of-Things (IoT) Technology

Advanced technology in agriculture has enabled the manipulation of the artificial light spectrum ... more Advanced technology in agriculture has enabled the manipulation of the artificial light spectrum in plant development such as improving yield and plant growth. Light manipulation using light-emitting diodes or LEDs can inhibit, delay, or even promote flowering. Some studies have shown that far-red (FR) light can stop flowering, but studies have not fully explored the best method involving intensity and duration to induce plant growth. This paper presents results on LED light manipulation techniques, particularly FR light, on plant flowering control and plant elongation. The light manipulation technique on the combination of colors, photoperiods, and intensities proved that it can stop flowering, and stimulate and control the growth of plants during cultivation. The system was monitored using an Internet-of-Things (IoT) remote monitoring system, and it performed data mining. The results showed that plants that were grown under artificial sunlight (T5) and normal light (T1) treatments...

Advanced encryption standard algorithm versus extreme learning machine based weight: a comparative study

ARPN journal of engineering and applied sciences, 2017

Advanced Encryption Standard (AES) is beingwidely used ciphering/deciphering system has emerged a... more Advanced Encryption Standard (AES) is beingwidely used ciphering/deciphering system has emerged asa standard benchmark. Due to rapid advancement in the hardware specifications, thearchitecture security of AES became a major concern. Furthermore, the newly developed machine learning dependent encryption architecture called Extreme Learning Machine Based Weight (ELMWi) appears more suitable for sundry cryptographic implementations. This article compares the performance of ELMWi with AESvia statistical evaluation, where the parameters such as sensitivity, visual imperceptibility metrics, and key space are determined. Results reveal their similar performances. It is further argued that ELMWi outperforms the AES in perspective of architecture implementation.

A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem

Applied Sciences, 2018

Firefly algorithm is among the nature-inspired optimization algorithms. The standard firefly algo... more Firefly algorithm is among the nature-inspired optimization algorithms. The standard firefly algorithm has been successfully applied to many engineering problems. However, this algorithm might be stuck in stagnation (the solutions do not enhance anymore) or possibly fall in premature convergence (fall in to the local optimum) in searching space. It seems that both issues could be connected to the exploitation and exploration. Excessive exploitation leads to premature convergence, while excessive exploration slows down the convergence. In this study, the classical firefly algorithm is modified such that make a balance between exploitation and exploration. The purposed modified algorithm ranks and sorts the initial solutions. Next, the operators named insertion, swap and reversion are utilized to search the neighbourhood of solutions in the second group, in which all these operators are chosen randomly. After that, the acquired solutions combined with the first group and the firefly a...

Improved Internet of Things (IoT) monitoring system for growth optimization of Brassica chinensis

Computers and Electronics in Agriculture, 2019

Abstract The Internet of Things (IoT) has been integrated in various applications such as smart h... more Abstract The Internet of Things (IoT) has been integrated in various applications such as smart homes and smart cities. IoT in agriculture such as in monitoring indoor climatic conditions can help to improve the plant growth. This paper proposes a new approach in utilizing IoT technology as a remote monitoring system to control the indoor climatic conditions via light emitting diode (LED) parameters which include spectrums, photoperiod and intensity in order to increase yields as well as to reduce the turnaround time. This study showed that growth of Brassica chinensis under different wavelengths of light source has influenced plant growth performance and phytochemicals characteristics. Four different light treatments were experimented using pulse treatment (1 h light and 1 h dark), continuous light (CL), high intensity and artificial light control of 12 h light and 12 h dark. Data such as the leaves count, height, dry weight and chlorophyll a & b of plants were analyzed. The results showed high mean value of plants’ fresh weight (FW) of 410.77 g under pulse treatment compared to other light treatments. The percentage of moisture content (MC) was recorded higher on average under normal light (99.15%), value of leaf area (LA) was recorded higher under artificial sunlight with an average value of 976.84 cm2. Even though the results of LA were better under artificial sunlight, CL with low intensity gave higher stem height (SH) and number of leaves (NOL). In order to capture real-time data and monitor the environmental parameters of the plant experiment, an intelligent embedded system was developed to automate the LED control and manipulation. The results showed that the system is stable and has significant referential in the area of plant factory or indoor farming.

NARMAX model identification of a palm oil biodiesel engine using multi-objective optimization differential evolution

AIP Conference Proceedings, 2017

Optimizing a Just-In-Time logistics network problem under fuzzy supply and demand: two parameter-tuned metaheuristics algorithms

Neural Computing and Applications, 2017

Extreme learning machine based optimal embedding location finder for image steganography

PLOS ONE, 2017

A novel extreme learning machine-based cryptography system

Security and Communication Networks, 2016

A literature review on green supply chain modelling for optimising CO<SUB align="right">2 emission

International Journal of Operational Research, 2016

Carbon-capped Distribution Planning: A JIT Perspective

Computers & Industrial Engineering, 2016

We use a hybrid NSGA-II to obtain the near Pareto front solutions in a Just-In-Time distribution ... more We use a hybrid NSGA-II to obtain the near Pareto front solutions in a Just-In-Time distribution network.We investigate different carbon constraints namely periodic, cumulative and global.We present the complexity analysis for the proposed model.We analyze the solution approach as well as identify some managerial and policy insights. Products distribution and transportation is one of the largest sources of CO2 emission in supply chains. To date, a number of researchers have argued that intensive transportation activities through popular distribution strategies such as Just-In-Time (JIT) could significantly increase carbon emissions within logistics chains. However, a systematic understanding of how JIT distribution affects carbon emissions is still lacking in current literature. In this study, we develop a bi-objective optimization model for a carbon-capped JIT distribution of multiple products in a multi-period and multi-echelon distribution network. The aims are to jointly minimize total logistics cost and to minimize the maximum carbon quota allowed per period (carbon cap). The considered problem is investigated under three different carbon emission constraints namely periodic, cumulative and global. Since the studied problem is NP-Hard, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed and its parameters are tuned by Taguchi method. For further quality improvement of the developed solution approach, a novel local search approach called modified firefly algorithm incorporates NSGA-II. Different sizes of the problem are considered to compare the performances of the proposed hybrid NSGA-II and the classical one. Finally, the results are presented along with some policy and managerial insights. For policy makers, the findings show the impact of varying the carbon emission cap on total cost and total emissions under JIT distribution concept. From managerial perspectives, we analyze the relationships between average inventory holding and backlog level per period which can assist mangers to identify critical decisions for JIT distribution of products in carbon-capped environment.

A tuned NSGA-II to optimize the total cost and service level for a just-in-time distribution network

Neural Computing and Applications, 2016

Distribution network planning has attracted the attention of many studies during last decades. Ju... more Distribution network planning has attracted the attention of many studies during last decades. Just-in-time (JIT) distribution has a key role in efficient delivery of products within distribution networks. In modeling of JIT distribution networks, the most frequently applied objectives are related to cost and service level. However, evaluating the impact of simultaneously minimizing total costs and balance between distribution network entities in different echelons still rarely complies with the current literature. To remedy this shortcoming and model reality more accurately, this paper develops a multi-objective mixed-integer nonlinear optimization model for a JIT distribution in three-echelon distribution network. The aims are minimization of total logistics cost along with maximization of capacity utilization balance for distribution centers and manufacturing plants. A non-dominated sorting genetic algorithm-II (NSGA-II) with three different mutation operators namely swap, reversion and insertion is employed to provide a set of near-optimal Pareto solutions. Then, the provided solutions are verified with non-dominated ranked genetic algorithm (NRGA) as well. The Taguchi method in design of experiments tunes the parameters of both algorithms, and their performances are then compared in terms of some multi-objective performance measures. In addition, a genetic algorithm is used to assess Pareto optimal solutions of NSGA-II. Different problems with different sizes are considered to compare the performance of the suggested algorithms. The results show that the proposed solution approach performs efficiently. Finally, the conclusion and some directions for future research are proposed.

Identification and modeling of environment climates inside naturally ventilated tropical greenhouse

Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification

Development of hand-arm model rig for tremor excitation

2008 International Symposium on Information Technology, 2008

Production Planning and Inventory Control in Automotive Supply Chain Networks

Lecture Notes in Computer Science, 2014

ABSTRACT his paper addresses a non-linear optimization model by integrating production planning a... more ABSTRACT his paper addresses a non-linear optimization model by integrating production planning and inventory control in the automotive industry at the strategic and operational level. In order to provide an effective modeling, we developed a framework to integrate manufacturing system and suppliers within an automotive supply chain network. The numerical experiments demonstrate the efficiency of the proposed model on minimization of total delivery cost and due date delivery.

Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 2007

Multiobjective Evolutionary Algorithm Approach in Modeling Discrete-Time Multivariable Dynamics Systems

2010 Second International Conference on Computational Intelligence, Modelling and Simulation, 2010

ABSTRACT Multiobjective evolutionary algorithms are robust tool in solving many optimization prob... more ABSTRACT Multiobjective evolutionary algorithms are robust tool in solving many optimization problems. Model structure selection is a procedure in system identification procedures. This procedure counters two contradicting objective functions which are minimizing mean square error and complexity of the selected model. This paper investigates the effectiveness and the performance of multiobjective evolutionary algorithm using elitist nondominated sorting genetic algorithm (NSGA-II) in identifying the model structure for discrete-time multivariable dynamic systems. Two simulated multivariable systems and a real multivariable system, which is a jacketed continuous stirred tank reactor, were used to investigate the effectiveness of NSGA-II. The identified model is validated using one-step-ahead prediction. The results indicate that NSGA-II is able to optimize the model structure of the multivariable systems with good predictive accuracy and adequate model structure.