Fitness Function Research Articles

Association between physical fitness and executive functions in cognitively healthy female older adults: a cross-sectional study.

Cardiovascular endurance and muscular fitness seem to impact specific cognitive components in older females. However, it remains uncertain whether these relate to executive functions or if these correlations are limited to specific physical fitness indicators. This study aimed to determine the association between specific physical fitness components and executive functions in community-dwelling older females. Thirty-five cognitively healthy community-dwelling older females (71.5 ± 5.7years) underwent a series of physical fitness tests. These included the handgrip strength test (HGT), the 6-min walk test (6MWT), the 8-foot up-and-go test (8FUGT), and the chair stand test (CST). Participants also completed trail A and trail B of the cognitive trail making test. Results showed that trail B reaction time had a negative association with both HGT (r = - 0.502; p = 0.002) and 6MWT (r = - 0.543; p < 0.001). Together, the HGT and 6MWT results explained 39% of the variation in trail B reaction times: HGT accounted for 18% and 6MWT for 21%. Better scores on the 6MWT and HGT-but not on the 8FUGT and CST-correlated with enhanced executive function in cognitively healthy community-dwelling older females. The results of this study underscore the importance of specific physical assessments, like the 6MWT and HGT, as potential indicators of executive function, offering targeted strategies for maintaining cognitive health in aging females.

Parametric Optimization of System Modes for Nozzle Turbine Vane by Means of Costimulated Artificial Immune System

One requirement posed by customers is to achieve adequate durability levels as described in technical requirement documents. Modal analysis is one of the design assessments aimed at identifying the risks of high cycle fatigue (HCF). This article presents a novel application of an artificial immune system (AIS) in the optimization of a nozzle guide vane’s modal characteristics. The aim is to optimize the system’s natural frequencies in the vibration vane and adjacent hardware (turbine casing). The geometrical characteristics accounted for in the optimization process include the shell thicknesses on the turbine casing side and the nozzle outer band features (hook thickness, leaning and position). The optimization process is based on a representative model established from FEM analysis results. The framework is robust because of the applied metamodel and does not require time-consuming FEM analysis in order to evaluate the fitness function. The aim is to minimize the model area (a derivative of the system weight) with constraints imposed on the frequency (a penalty function). The optimum design is given as the solution with an increased shell thickness in the turbine casing and leaning nozzle outer band hooks to obtain the maximum stiffness of the system. The results obtained by means of the artificial immune system (AIS) and a novel variant based on an additional costimulation procedure (CAIS) are compared with the solution obtained by means of a genetic algorithm implemented in the commercial CAE software (Ansys version 19.2).

Acoustic heterostructures designed by topology optimization for interface states

Interface states can be induced through the design of heterostructures comprised of structures or materials with different physical properties. Compared with the traditional method of the manual experience adjustment, the topology optimization method is more systematic and scientific in designing heterostructures to achieve the desired performance of interface states. We combine the multiple population genetic algorithm (MPGA) with the finite element method, and consider the transmission spectrum characteristic of interface states as a fitness function forming a topology optimization method to find optimal designs for acoustic heterostructures. The frequency of the interface state induced by acoustic heterostructures after topology optimization is highly consistent with the objective frequency. The characteristic analyses of the sound field and transmission spectrum show that interface states possess high transmission and localization. Such a topology optimization method opens up design solutions in various engineering fields, which include wavefront manipulation, sound detection, and acoustic topological materials.

Oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization for imbalanced classification

In response to the need to generate synthetic minority class samples to extend minority classes, the SMOTE-based oversampling methods have been favored for class-imbalanced classification. They usually generate unnecessary noise when training data are not well separated. Although filtering-based oversampling methods are recognized as effective solutions for addressing noise generation through employing specific noise filters based on instance selection methods to remove suspicious noise, they suffer from the following issues: a) noise filters heavily rely on strong assumptions, causing low robustness to different datasets; b) noise filters are specially designed for a specific oversampling method, and are not easily extended to others; and c) noise filters have a relatively high time consumption. To address noise generation while overcoming the above issues a)-c), an oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization (OF-SSO-ABPSO) is proposed. OF-SSO-ABPSO is a wrapping framework compatible with almost all the oversampling methods. First, in the framework, a SMOTE-based method is used to generate synthetic minority class samples. Second, a novel accelerated binary particle swarm optimization (ABPSO) algorithm with a new search space reduction strategy, a new particle update mechanism, and a new fitness function is proposed. Third, a novel ABPSO-based sample subspace optimization (SSO-ABPSO) method is proposed and used as a noise filter to remove suspicious noise from the training set and synthetic minority class samples. Experiments prove that, a) OF-SSO-ABPSO can improve 6 representative SMOTE variations by addressing noise generation, and b) OF-SSO-ABPSO outperforms 7 state-of-the-art filtering-based oversampling methods.

Form generative approach for front face design of electric vehicle under female aesthetic preferences

Vehicles are the most representative product of both transportation and industry. Fueled by the growing popularity of energy-saving and environmentally friendly ideas and policies, new energy technologies and their supporting industries are experiencing constant development and refinement. As a result, electric vehicles (EVs) are steadily displacing traditional fuel-powered vehicles as the dominant force in the vehicle market. The experience economy is driving a significant shift in the direction of EV development, which is transitioning from improving functionality to optimizing the emotional connection consumers have with their vehicles. This means the form factor of the vehicle itself is becoming a key element in attracting consumers and influencing purchasing decisions. Over half of China's EV consumer market is comprised of females. However, many companies continue to develop products solely from a male perspective, missing out on a vast market opportunity. Therefore, incorporating design elements that cater to female aesthetic preferences into EVs can undoubtedly play a crucial role in unlocking this significant segment of the market. Based on this, the paper proposes a form generative design approach that specifically caters to the preferences of female consumers within the theoretical framework of Kansei engineering.The research process begins by obtaining Kansei imagery through the collection and analysis of big data. Subsequently, eye-tracking experiments are conducted to define the key design elements of the automobiles. Concurrently, appropriate aesthetic measurement indicator (AMI) system is established. Next, AMI values are computed for the sample set. This data is then leveraged to create a mapping relationship with the Kansei imagery, enabling the objective reflection of subjective aesthetic evaluations. Finally, an integrated Kansei evaluation equation serves as the fitness function within a generative approach that utilizes genetic algorithms and visual AI to produce form design solutions for electric vehicles. The evaluation confirms that the proposed design approach successfully generates a substantial number of high-quality creative solutions that align with female aesthetic preferences, while maintaining low cost and high efficiency.

Exploring double-diffusive nanofluid flow in divergent/convergent nondarcy porous space: A machine learning numerical approach with zero mass flux

In this study, authors address the problem of analyzing the Jeffrey–Hamel nanofluid flow within a nonparallel channel under the influence of a thermally balanced non-Darcy permeable medium. Our objective is to explore the behavior of the nanofluid, employing the Buongiorno nanofluid model, and considering factors such as zero mass flux conditions, variable rheological fluid properties, and the presence of temperature jump phenomena through ANN-PSO-NNA. To tackle this intricate issue, propose a novel approach using Artificial Neural Networks (ANN) integrated with particle swarm optimization (PSO) hybrid with neural network algorithm (NNA). This combined technique is referred to as ANN-PSO-NNA. The governing flow equations based on the Jeffrey–Hamel analysis are first transformed into dimensionless forms. To establish the efficacy of our approach compare it with the widely used NDSolve method. Furthermore, the study presents the absolute error analysis for four distinct cases offering a quantitative assessment of the accuracy of ANN-PSO-NNA. The absolute error between the NDsolve and the proposed ANN-PSO-NNA is given for four different cases, ranging from 1.95E-06 to 1.18E-08. The biases and weights obtained through proposed method range from −10 to 10. This enables us to gauge the performance of the ANN-PSO-NNA minimum values of the fitness function 2.37E-08, 2.30E-08, 3.92E-08, and 1.22E-08 from multiple independent runs, which are showcased to be convergent, underscoring the robustness of the ANN-PSO-NNA approach. Employing graphical representations, explores the influence of various parameters on the velocity and temperature profiles. Our integrated approach offers a comprehensive understanding of the underlying dynamics, paving the way for optimized designs and enhanced efficiency across these diverse engineering applications.

Design of Intelligent Political Test Paper Generation Method Based on Improved Intelligent Optimization Algorithm

With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of political ideology teaching evaluation. Aiming at the problem that the current political ideology examination paper-grouping strategy method easily falls into the local optimum, a kind of intelligent paper-grouping method for political ideology examination based on the improved stock market trading optimisation algorithm is proposed. Firstly, by analyzing the traditional steps of political thought grouping, according to the index genus of the grouping problem and the condition constraints, we construct the grouping model of political thought test questions; then, combining the segmented real number coding method and the fitness function, we use the securities market trading optimization algorithm based on the Circle chaotic mapping initialization strategy and adaptive t-distribution variability strategy to solve the grouping problem of the political thought test. The experimental results show that the method can effectively find the optimal strategy of political thought exam grouping, and the test questions have higher knowledge point coverage, moderate difficulty, and more stable performance.

Research and verification on parameter solution of mixed shock model for common cause failure based on particle swarm algorithm

AbstractMixed shock model is an explicit construction method of failure probability model based on component independent failure, system nonfatal shock, and fatal shock failure, which considers common cause failure (CCF) in redundant system. For aerospace systems, a modified mixed shock model is proposed, which considers several components may fail independently and simultaneously in operation. In order to solve the issue that the parameters of the mixed shock model cannot be solved directly based on the failure probability data, a parameter solving method based on particle swarm optimization (PSO) algorithm is proposed. Additionally, the relationship between the failure probability and the gradient of the parameter change is deduced, and the reduced‐order (RO) solution based on the gradient of the parameter change is proposed to improve the efficiency of the solution. A fitness function construction method based on the relative error of the solution probability and the true probability is proposed to improve the probability solution accuracy of multicomponent failure. The nonlinear inertia factor optimization method combined with fitness change is studied to improve the particle swarm dynamics. The accuracy of the results of different parameters solving sequence and different PSO methods are compared, and the effectiveness of the RO solution is verified. The results of the mixed shock model before and after modification are compared with the different CCF data, which verifies the effectiveness and wide applicability of the modified mixed shock model. The results show that the modified mixed shock model for CCF and its parameter solution method can significantly improve the probability solution accuracy of all components failure, and also provide a new theoretical basis and solution method for the quantitative analysis of multiredundant system failure.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

Close-range maneuver planning for uncontrolled rendezvous with multiple elliptical orbit targets based on genetic algorithm

On-orbit debris removal is an important and challenging problem in space engineering. A low-cost method for this problem is to use uncontrolled rendezvous with a sub-spacecraft released by a service spacecraft. The sub-spacecraft do not carry any control system and are designed to rendezvous with the target debris to remove it. This paper presents a comprehensive study on the mission planning. First, we analyze and dynamically model the uncontrolled rendezvous process for elliptical orbit targets, and study various aspects such as maneuver, sub-spacecraft release parameter solution, and rendezvous accuracy estimation. Then, we use NSGA-II, a multi-objective optimization genetic algorithm, to establish the mission planning model, focusing on the design of decision variables, constraints and fitness functions. Finally, we apply our proposed method to two specific cases of Molniya orbit, and verify the effectiveness of the planning model. We also conduct a deep analysis of the planning results, and summarize the commonalities and differences of various effective strategies.

Foraging ants affect community composition and diversity of phyllosphere fungi on a myrmecophilous plants, Mallotus japonicus.

Many microorganisms inhabit the aboveground parts of plants (i.e. the phyllosphere), which mainly comprise leaves. Understanding the structure of phyllosphere microbial communities and their drivers is important because they influence host plant fitness and ecosystem functions. Despite the high prevalence of ant-plant associations, few studies have used quantitative community data to investigate the effects of ants on phyllosphere microbial communities. In the present study, we investigated the effects of ants on the phyllosphere fungal communities of Mallotus japonicus using high-throughput sequencing. Mallotus japonicus is a myrmecophilous plants that bears extrafloral nectaries, attracting several ant species, but does not provide specific ant species with nest sites like myrmecophytes do. We experimentally excluded ants with sticky resins from the target plants and collected leaf discs to extract fungal DNA. The ribosomal DNA internal transcribed spacer 1 (ITS1) regions of the phyllosphere fungi were amplified and sequenced to obtain fungal community data. Our results showed that the exclusion of ants changed the phyllosphere fungal community composition; however, the effect of ants on OTU richness was not clear. These results indicate that ants can change the community of phyllosphere fungi, even if the plant is not a myrmecophyte.

Research on civil aircraft fault diagnosis method based on selective integration

Based on the research of the existing methods, this paper applies the performance parameters of the base learner and proposes the overall performance metrics of the base learner subset based on the diversity measurement method, the diagnostic accuracy of the base learner, and the running time. The performance metric as the fitness function of the genetic algorithm establishes the selection method of the subset of the base learner; the deep neural network is used to learn the complex nonlinear relationship between the base learners and a selective integration framework based on the stacking method is proposed to improve the overall diagnostic performance of integration model. Then, the data from engine operation to failure are used to verify the method performance proposed in this paper. The comparison results find that the method has a higher fault diagnosis accuracy and robustness. Finally, the method is streamlined to make it more applicable and suitable for engineering problems.

Optimization of off-grid renewable energy systems using a hybrid version of golden search algorithm

Currently, the development of HRESs (hybrid renewable energy systems) in remote areas is of great importance and popularity. However, measuring and optimizing the capacity of these systems faces a difficult challenge. Multiple works had been reported in the literature to optimize such systems, all of which aim to achieve an optimal configuration with minimum annual net cost. Therefore, the significance of providing off-grid electrification to remote areas through HRESs can be highlighted as a crucial case for sustainable growth. Accordingly, the study proposes a modified metaheuristic approach, known as the Hybrid Golden Search Algorithm (HGSA), for long-term application planning and optimization of the off-grid HRES. The aim of this algorithm is to minimize the amount of net cost which is used annually; to reduce the probability of power supply interruption. In order to assess the effectiveness of the proposed algorithm, a simulation study over a long period on a remote area was conducted. From the results, increasing the reliability level from 1 % to 3 % causes a decrease in the total net annual cost by around 7.3 % under the proposed HGSA and also results in a reduction in component size (around 6 % and 21 % reductions for the wind turbine area and storage tanks, respectively). Further, the HGSA technique obtains the lowest value of fitness function for the hybrid system at a reliability level of 3 %, which is 31,539,810$. This result demonstrates that the efficiency of HGSA outperforms Fuzzy Logic and Optimization, Artificial Bee Colony (ABC), and GSA techniques. Based on this, the proposed HGSA could lead to more promising results than the other comparative algorithms. Hence, the proposed HGSA can be a valuable tool for long-term application planning and optimization of off-grid HRES, which can contribute significantly to achieving sustainable development goals.

Effects of 12 Weeks of Moderate-intensity Continuous Exercise and High-intensity Interval Exercise on Cognitive Function in Elderly Subjects

OBJECTIVES This study analyzed the possibility of brain function activation after exercise training of high-intensity interval exercise and moderate-intensity continuous exercise in elderly people.METHODS The subjects of the study were elderly female people aged 65 years or older and were divided into three groups : control group, moderate-intensity continuous training (MICT), and high-intensity interval training (HIIT). Changes of body composition, physical fitness, cognitive function, and cognitive function-related blood variables were compared between before and after 12 weeks of exercise training.RESULTS Body weight, BMI (body mass index), and WHR (waist hip ratio) of body composition in HIIT group decreased significantly (p&lt;0.05) after exercise training, and there was no significant difference in MICT group and the control group. HIIT group significantly (p&lt;0.05) improved in grip strength, muscular endurance, flexibility, balance, cardiorespiratory endurance, and walking ability after exercise training, while MICT group showed significant (p&lt;0.05) improvements only in flexibility, balance, and cardiorespiratory endurance. Regarding blood variables, HIIT group showed significant (p&lt;0.05) differences in BDNF (brain-derived neurotrophic factor), irisin, and β-amyloid after exercise training, MICT group showed significant (p&lt;0.05) differences only in β-amyloid. Cognitive function in HIIT group and MICT group significantly (p&lt;0.05) improved in terms of temporal and spatial orientation, memory, and total scores after exercise training. During the recovery phase after one session of high-intensity interval exercise, blood lactic acid concentration increased significantly.CONCLUSIONS This study was confirmed the possibility of giving that high-intensity interval exercise for the elderly not only produces positive changes in body composition and improves physical fitness and cognitive function, but also frequently stimulates lactate production, helping to activate brain function through changes in BDNF and irisin in the blood.

AHA-3WKM: The optimization of K-means with three-way clustering and artificial hummingbird algorithm

Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K-means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K-means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results.

The relationship between cardiorespiratory fitness and inhibitory control following acute stress: An ERP study.

Although the relationships among acute stress, cardiorespiratory fitness (CRF), and cognitive function have been examined, whether CRF is related to behavioral and neuroelectric indices of inhibitory control following acute stress remains unknown. The purpose of the current study was to investigate the combined influence of acute stress and CRF on inhibitory control. Participants, aged 20-30 years, were stratified into the Higher-Fit (n = 31) and the Lower-Fit (n = 32) groups, and completed a Stroop task following the modified Maastricht Acute Stress Test (MAST) in the stress condition and the sham-MAST in the non-stress condition, during which electroencephalography was recorded. Behavioral (i.e., response time and accuracy) and neuroelectric (N2 and P3b components of the event-related potential) outcomes of inhibitory control were obtained. While the Higher-Fit group demonstrated shorter response times and higher accuracy than the Lower-Fit group following both the MAST and the sham-MAST, they also exhibited selective benefits of acute stress on inhibitory control performance (i.e., decreased response times and diminished interference scores). CRF-dependent alterations in neuroelectric indices were also observed, with the Higher-Fit group displaying smaller N2 and greater P3b amplitudes than the Lower-Fit group following the sham-MAST, and increased N2 and attenuated P3b amplitudes following the MAST. Collectively, these findings not only confirm the positive relationship between CRF and inhibitory control but also provide novel insights into the potential influence of CRF on inhibitory control and associated neuroelectric activity following acute stress.

An optimized microstrip antenna to generate intense localized heating at target sites for maximum effect

AbstractNowadays, the use of electromagnetic waves in medical applications has become common, and hyperthermia is one of the popular areas. Nonetheless, designing effective antennas for electromagnetic hyperthermia poses a key challenge. In designing of hyperthermia antennas for medical applications, factors such as appropriate resonant frequencies and appropriate antenna sizes are important. Another critical aspect in the design of useful and usable hyperthermia antenna is the heat on the target body area, since a proper depth setting for heating is normally neglected. In this paper, using the Particle Swarm Optimization (PSO) algorithm, we focus on the heat on the target area when designing a hyperthermia antenna that operates at the frequency of 432 MHz. The antenna is analyzed using the finite difference time domain method, while the PSO fitness function is selected in such a way as to maintain the optimal frequency characteristics of the antenna, along with optimization of its heating performance. A series of simulation studies in MATLAB and the associated laboratory results confirm accuracy of the designed antenna. With minimal influence on healthy tissues, the temperature of approximately 42 degrees Celsius is achieved steadily after about 12 min from the start of heating in the target area, where the tumor is located. We observe minor differences between simulation and laboratory results, owing to not being able to use living tissue in the laboratory and lack of precision in the construction of antenna with optimized parameters.

Dynamic path planning for mobile robots based on artificial potential field enhanced improved multiobjective snake optimization (APF‐IMOSO)

AbstractWith the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta‐heuristic algorithms have gained popularity owing to their efficiency and problem‐specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF‐IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF‐IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time‐savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta‐heuristics, APF‐IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF‐IMOSO can generate multiple viable collision‐free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.

Crushing Response and Optimization of a Modified 3D Re-Entrant Honeycomb.

A modified 3D re-entrant honeycomb is designed and fabricated utilizing Laser Cladding Deposition (LCD) technology, the mechanical properties of which are systematically investigated by experimental and finite element (FE) methods. Firstly, the influences of honeycomb angle on localized deformation and the response of force are studied by an experiment. Experimental results reveal that the honeycomb angles have a significant effect on deformation and force. Secondly, a series of numerical studies are conducted to analyze stress characteristics and energy absorption under different angles (α) and velocities (v). It is evident that two variables play an important role in stress and energy. Thirdly, response surface methodology (RSM) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented with high precision to solve multi-objective optimization. Finally, the final compromise solution is determined based on the fitness function, with an angle of 49.23° and an impact velocity of 16.40 m/s. Through simulation verification, the errors of energy absorption (EA) and peak crush stress (PCS) are 9.26% and 0.4%, respectively. The findings of this study offer valuable design guidance for selecting the optimal design parameters under the same mass conditions to effectively enhance the performance of the honeycomb.

Application of Evolutionary Computation to the Optimization of Biodiesel Mixtures Using a Nature-Inspired Adaptive Genetic Algorithm

The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of the ingredients in the optimal mixtures. The innovative approach’s effectiveness lies in problem simulation with improvements in the evaluation of the specific function and the way to define and tune the genetic algorithm. Environmental imperatives in the era of climate change currently impose the optimized production of alternative environmentally friendly biofuels to replace fossil fuels. Biodiesel in particular, appears to be more attractive in recent years, as it originates from renewable bio-derived resources. The main ingredients of the specific biofuel mixture investigated in this research are diesel and biodiesel (100% from bioresources). The assessment of the new biodiesel examined was performed using a fitness function that estimated both the density and cost of the fuel. Beyond the evaluation criterion of cost, density also influences the suitability of this biofuel for commercial use and market sale. The outcomes from the modeling process can be beneficial in saving cost and time for new biodiesel production by using this novel decision-making tool in comparison with randomized laboratory experimentations.