Fitness Function Of Genetic Algorithm Research Articles

A New Hybrid Optimization Algorithm to Solve the Clustering Problem

: This paper presents a novel hybrid technique, merging the K-means algorithm with Genetic Algorithm (GA), aiming to enhance clustering performance. This approach leverages the strengths of both algorithms, enabling improved cluster generation by overcoming individual algorithmic limitations. The GA-KM algorithm is introduced to aid K-means in avoiding local optima, with GA exhibiting proficiency in determining optimal cluster initialization and parameter optimization. The focus is on developing a GA-based algorithm for generating high-quality clusters efficiently. Notably, the research explores the application of this hybrid approach to address issues in the educational domain, specifically for out-of-school children. The fitness function in GA is tailored to the problem area, emphasizing the need for an appropriate system to study and address school children's problems. The research proposes a hybrid algorithm (KM-GA-NM-PSO) that amalgamates the best features of existing algorithms, thereby overcoming individual limitations and promising superior results. This hybridization is expected to yield high-quality clusters with minimal function evaluations, outperforming other methods by producing clusters with small standard deviations on selected datasets. The proposed approach, combining KM, GA, NM, and PSO algorithms, demonstrates improved data clustering quality and algorithmic efficiency.

ENERGY EFFICIENCY IMPROVEMENT AND ENTROPY GENERATION MINIMIZATION THROUGH STRUCTURAL OPTIMIZATION OF A DOUBLE-LAYER LIQUID-COOLED PLATE WITH CIRCULAR ARC-SHAPED FLOW CHANNELS

Power batteries for new energy vehicles and other high-power electrical devices benefit greatly from liquid-cooled plates for thermal control. In the present work, a liquid-cooled plate with a double-layer arc-channel structure is developed to achieve a uniform temperature distribution on the surface of lithium-ion powered batteries and to reduce operating temperatures. Numerical simulations are employed to examine the flow properties and heat transfer capabilities of the plate. Subsequently, the model is validated experimentally. The structure of the liquid-cooled plate is optimized using a genetic algorithm. In the research, two methods for optimizing the structure of liquid-cooled plates have been proposed based on defining the fitness function of genetic algorithms. The first method uses a dimensionless number to represent the amount of pump power needed to allow the working fluid to absorb one joule of heat energy. The other method uses the entropy generation of the liquid-cooled plate as the fitness function of genetic algorithms. Genetic algorithms may be used to find the minimum dimensionless number and the minimal amount of entropy. The structural characteristics of the liquid-cooled plate may be obtained with the best energy efficiency and the least amount of entropy production using the dimensionless number minimization (DNM) and entropy generation (EGM) optimization techniques, respectively. The performance of the two optimization techniques is contrasted. The maximum temperature of the plate is reduced by 2.58 K and 0.14 K, and the standard deviation of the temperature is reduced by 0.685 K and 0.408 K after the optimization using the creatively established dimensionless number and the entropy generation minimization methods, respectively. The pump work required by the working fluid to absorb one joule of heat energy from the plate is reduced by 70.5% and 12.1%. At two distinct boundary conditions, the proposed liquid-cooled plate outperforms the plates with serpentine and parallel channels in terms of cooling performance or energy efficiency.

Design Methodology of Wind Turbine Rotor Models Based on Aerodynamic Thrust and Torque Equivalence

Limited by scaling effects, the physical model tests of FOWTs (floating offshore wind turbines) cannot simulate the aerodynamic loads on rotors correctly. To solve this problem, the real-time hybrid model tests in wind tunnels were developed and provided a feasible solution for the aerodynamic simulation. To perform the wind tunnel tests, the design of aerodynamic equivalent rotor models is most critical. In this study, an innovative methodology of aerodynamic equivalent design for the wind turbine rotors is developed based on GA (genetic algorithm). The NREL (National Renewable Energy Laboratory) 5 MW and DTU (Technical University of Denmark) 10 MW rotors are employed for the case studies to validate the proposed methodology. According to the results, the model-scale aerodynamic thrust performance can be accurately matched with the prototype in the entire region between cut-in and cut-out wind speeds, which allows the rotor model to provide correct thrust at different wind speeds. The variance of the aerodynamic torque with the wind speeds for the developed model is also in good agreement with the prototype, which could be beneficial for the design of the model-scale active pitch control strategy. Moreover, the applicability of the fitness functions of GA is discussed.

Nested genetic algorithm-based classifier selection and placement in multi-level ensemble framework for effective disease diagnosis

Effective disease diagnosis is a critical unmet need on a global scale. The intricacies of the numerous disease mechanisms and underlying symptoms make developing a model for early diagnosis and effective treatment extremely difficult. Machine learning (ML) can help to solve some of these issues. Recently, various ensemble-based ML models have benefited clinicians in early diagnosis. However, one of the most difficult challenges in multi-level ensemble approaches is the classifier selection and their placement in the ensemble framework as it improves the overall performance. Let m classifiers have to select from n classifiers there are ( n m ) ways. Again, these ( n m ) possibilities can be arranged in m ! ways. Finding the best m classifiers and their positions from total ( n m ) m ! ways is a challenging and hard problem. To address this challenge, a dynamic three-level ensemble framework is proposed. A nested Genetic Algorithm (GA) and ensemble-based fitness function are employed to optimize the classifier selection and their placement in a three-level ensemble framework. Our approach used eleven classifiers and chose seven classifiers by maximizing the fitness function. The proposed model experiments on 12 disease datasets. The proposed model outperformed in terms of accuracy, F1, and G-measure on the Chronic Kidney Disease (CKD) dataset is 0.987, 0.988, and 0.989, respectively. In terms of AUC on the Heart disease dataset (HDD) is 0.998 and in terms of recall on the Hypothyroid disease dataset (HyDD) is 0.988. In addition, the proposed model superiority is statically evaluated by Wilcoxon-Signed-Rank (WSR) test compared with other ensemble models, such as random forest (RF), bagging classifier (BC), XGBoost (XGB), and gradient boost classifier (GBC) with probability value p < 0.05 results shows all the traditional ensemble model differs with proposed model and also effective size evaluated with using the matched-pairs rank biserial correlation coefficient wc and statistical results shows effective size is large with RF and BC and effective size is medium with XGB and GBC. Proposed model has outperformed comparing with State-Of-The-Art (SOTA) ensemble and non-ensemble models. Further, the proposed model outperformed in terms of the ROC curve in the majority of the disease datasets. The results suggest the usage of the proposed model for disease diagnosis applications.

Dynamic small-series fashion order allocation and supplier selection: a ga-topsis-based model

The fashion industry is currently confronted with significant economic and environmental challenges, necessitating the exploration of novel business models. Among the promising approaches is small series production on demand, though this poses considerable complexities in the highly competitive sector. Traditional supplier selection and production planning processes, known for their lengthy and intricate nature, must be replaced with more dynamic and effective decision-making procedures. To tackle this problem, GA-TOPSIS hybrid model is proposed as the methodology. The model integrates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) evaluation into the fitness function of Genetic Algorithm (GA) to comprehensively consider both qualitative and quantitative criteria for supplier selection. Simultaneously, GA efficiently optimizes the order sequence for production planning. The model's efficacy is demonstrated through implementation on real orders, showcasing its ability to handle diverse evaluation criteria and support supplier selection in different scenarios. Moreover, the proposed model is employed to compute the Pareto front, which provides optimal sets of solutions for the given objective criteria. This allows for an effective demand-driven strategy, particularly relevant for fashion retailers to select supplier and order planning optimization decisions in dynamic and multi-criteria context. Overall, GA-TOPSIS hybrid model offers an innovative and efficient decision support system for fashion retailers to adapt to changing demands and achieve effective supplier selection and production planning optimization. The model's incorporation of both qualitative and quantitative criteria in a dynamic environment contributes to its originality and potential for addressing the complexities of the fashion industry's supply chain challenges

A Residual Resource Fitness-Based Genetic Algorithm for a Fog-Level Virtual Machine Placement for Green Smart City Services

Energy efficient information and communication technology (ICT) infrastructure at all levels of a city’s edifice constitutes a core requirement within the sustainable development goals. The ICT infrastructure of smart cities can be considered in three levels, namely the cloud layer infrastructure, devices/sensing layer infrastructure, and fog layer infrastructure at the edge of the network. Efficiency of a data-centre’s energy infrastructure is significantly affected by the placement of virtual machines (VMs) within the data-centre facility. This research establishes the virtual machine (VM) placement problem as an optimisation problem, and due to its adaptability for such complicated search issues, this paper applies the genetic algorithm (GA) towards the VM placement problem solution. When allocating or reallocating a VM, there is a large quantity of unused resources that might be used, however these resources are inefficiently spread over several different active physical machines (PMs). This study aims to increase the data-centre’s efficiency in terms of both energy usage and time spent on maintenance, and introduces a novel fitness function to streamline the process of computing the fitness function in GAs, which is the most computationally intensive component in a GA. A standard GA and first fit decreasing GA (FFD-GA) are applied on benchmark datasets to compare their relative performances. Experimental results obtained using data from Google data-centres demonstrate that the proposed FFD-GA saves around 8% more energy than a standard GA while reducing the computational overhead by approximately 66%.

Experimentation and Mathematical Modelling of Process Parameters for Prevention of Infectious Disease Caused by Staphylococcus aureus Bacteria in Indoor Environment.

Performance optimization using process parameters of an indoor air filtration system is a requirement that has to be established through experimental and analytical means for increasing machine efficacy. A closed casing containing a motor-driven blower is placed in a glass-encapsulated control volume. Air flows axially through an inlet filter and is thrown radially by the blower. In the radial path, air is treated with free radicals from the UVC-irradiated nano-TiO2 coated in the inner wall of casing. A known quantity of Staphylococcus aureus bacteria is populated (Courtesy: EFRAC Laboratories) in the glass-encapsulated control volume. The bacterial colony count is measured at different time intervals after the machine is switched on. Machine learning approaches are applied to develop a hypothesis space and the hypothesis based on best R2 score is used as a fitness function in genetic algorithm to find the optimal values of input parameters. The present research aims to determine the optimum time for which the setup is operated, the optimum air flow velocity in the chamber, the optimum setup-chamber-turning-radius affecting the air flow chaos, and the optimum UVC tube wattage, which when maintained yields the maximum reduction in bacterial colony count. The optimal values of the process parameters were obtained from genetic algorithm using the hypothesis obtained from multivariate polynomial regression. A reduction of 91.41% in bacterial colony count was observed in the confirmation run upon running the air filter in the optimal condition.

Design and optimization of piezoelectric actuators for microflap-embedded micropumps

In this paper, we propose a new method to use cost-effective multi-sheet off-the-shelf piezoelectric material (e.g. PZT) as an actuator for micropumps. Instead of one customized single PZT sheet that is typically expensive, multiple commercially available PZT sheets are utilized to decrease the cost of fabrication. For this purpose, we have derived analytic equations for expressing the natural frequency and mode shape of the actuator. The FEM simulations are utilized to verify the analytic equations. Thanks to their high accuracy, we can utilize the derived analytic equations as fitness functions of genetic algorithm (GA) for the optimization purpose of PZT physical aspects. Our experimental measurement results show that the GA is capable of optimizing multiple physical parameters of the piezoelectric actuator. Moreover, one-way compliant microflaps are presented for the first time to act as one-way valves for a PZT micropump aided by our proposed multi-sheet PZT actuator. The flow rate of this configuration is compared with a single-sheet PZT actuator in order to demonstrate the effect of the optimized PZT actuators in the practical applications of micropumps.

ANOVA as Fitness Function for Genetic Algorithm in Group Composition

Establishing suitable groups of students is one of the factors considered as a key to success in group collaboration. In addition, searching for the optimal solution of the problem can be more complicated and becomes an exhaustive search, while taking into consideration the equality of the group homogeneity. However, a few approaches focus on forming groups of students based on the analysis of variance (ANOVA) to ensure that all generated groups have been drawn from a similar population. Hence, the main purpose of this research is to propose a heuristic search algorithm based on genetic algorithm (GA) referred as to ‘Genetic Algorithm with ANOVA’ (GANOVA) to search for best possible groupings of students in terms of educational learning styles. Furthermore, the empirical case studies demonstrate that the proposed algorithm successfully searches for forming the optimal groups of students, where the F-test value of equality of variances is near zero.

Genetic algorithm-aided ensemble model for sensor fault detection and diagnosis of air-cooled chiller system

Due to the change of environment and operation conditions, sensor data in chiller system show the coexistence of gaussianity, non-gaussianity, non-linearity, and dynamic characteristic. Most of the existing methods can only be used for fault diagnosis based on a specific assumption, such as linearity or gaussianity. Therefore, this paper proposes a genetic algorithm (GA)-aided ensemble model without any characteristic assumption to detect and diagnose sensor fault in air-cooled chiller. Different from conventional techniques, the proposed method improves the detection and diagnosis performances by considering the Gaussian, non-Gaussian, non-linear, and dynamic characteristics, simultaneously. Firstly, several statistical models are analyzed and selected as the sub-models in our ensemble framework. The underlying assumptions for these models are different so that all the above characteristics can be considered. Then, an ensemble detection framework and an ensemble diagnosis framework based on GA are proposed to adapt to the complex data characteristics. To maximize the fault detection rate and diagnosis accuracy, two fitness functions of GA are proposed for detection and diagnosis, respectively. Finally, an integrated statistic and an integrated contribution plot are proposed based on the GA-aided ensemble framework. The detection and diagnosis performances of the ensemble statistic and the ensemble contribution plot are validated by using the data collected from a real chiller system. Results show that the proposed method delivers superior performance compared with other methods.

Genetic algorithm based probabilistic model for agile project success in global software development

Software development organizations implement agile methods in global software development (GSD) to leverage benefits in terms of low development cost, continuous project delivery, and high-quality product. Despite the benefits, using the agile process in the GSD organizations is not a straightforward task due to inhibit additional risk factors that could lead agile projects to failure. GSD organizations are continuously improving their process management activities to improve the rate of agile project success. Project success could be improved if the project manager or organization management has preliminary information about the derailment of the project features. This work aims to investigate the most influential agile project features in determining the project outcomes and develop a cost-effective, and effort-based prediction model to improve the probability of successful completion in the globally distributed environment. To do so, a nature-inspired optimization algorithm i.e., genetic algorithm (GA), has been employed to achieve these goals. In the proposed model, GA considers the probability of success with respect to cost to determine probable project outcomes. An efficacy measure is formulated as a fitness function in GA which maximizes the success of agile project outcomes relative to cost. The optimization model has been tested with two different prediction models i.e., Naive Bayes classifier (NBC) and logistic regression (LR). We performed the experiment on data gathered through the survey administered from the globally distributed agile projects. The results demonstrate that prediction models calculate the efficacy for best solutions as 0.531 and 0.5850 for NBC and LR, respectively. Moreover, the ranking of each project feature based on their relative cost identified using NBC and LR have more similarities. The t test results are significant, i.e., t = 6.068, p = 0.001 < 0.005, which indicates that no significant differences have been observed between the ranking assigned by two different methods ( NBC and LR). The results reveal that the developed prediction model based on identified eight agile project features that the GSD organization management and agile team need to focus more on to facilitate cost-effective successful implementation of agile projects.

Optimization of Structural Parameters of PCF Polarization Filter by a Genetic Algorithm

The material and geometry of photonic crystal fiber (PCF) are significant adjustable characteristics, and affect its optical properties. Finding the global optimal fiber is formulated as an optimization problem, and intelligent algorithms are used for the complex objective optimization. This paper presents the optimal structure of PCF for polarization filter determined by the Genetic Algorithm (GA) method, and the fitness function of GA is defined relatedly to the polarization filter optical index crosstalk (CT) and bandwidth. The designed PCF polarization filter has the following characteristics: the CT reaches 1845.3113 dB at 1.55 μm. When the fiber length is limited to 1000 nm, the bandwidth value exceeds 800 nm. Numerical results demonstrate that the performance of the designed PCF filter is better than that of the original and also have proved the GA method is beneficial to the design and fabrication of optical fibers.

Multi-Modal Haptic Rendering Based on Genetic Algorithm

Multi-modal haptic rendering is an important research direction to improve realism in haptic rendering. It can produce various mechanical stimuli that render multiple perceptions, such as hardness and roughness. This paper proposes a multi-modal haptic rendering method based on a genetic algorithm (GA), which generates force and vibration stimuli of haptic actuators according to the user’s target hardness and roughness. The work utilizes a back propagation (BP) neural network to implement the perception model f that establishes the mapping (I=f(G)) from objective stimuli features G to perception intensities I. We use the perception model to design the fitness function of GA and set physically achievable constraints in fitness calculation. The perception model is transformed into the force/vibration control model by GA. Finally, we conducted realism evaluation experiments between real and virtual samples under single or multi-mode haptic rendering, where subjects scored 0-100. The average score was 70.86 for multi-modal haptic rendering compared with 57.81 for hardness rendering and 50.23 for roughness rendering, which proved that the multi-modal haptic rendering is more realistic than the single mode. Based on the work, our method can be applied to render objects in more perceptual dimensions, not only limited to hardness and roughness. It has significant implications for multi-modal haptic rendering.

Region of convergence by parameter sensitivity constrained genetic algorithm-based optimization for coordinated load frequency control in multi-source distributed hybrid power system

The imbalances created by generation and load can indeed result in frequency variations demanding a load frequency control (LFC). The LFC becomes even more complicated with the multi-source distributed hybrid power system (HPS), where designing optimal controller parameters by identifying the region of convergence (RoC) is critical as it guarantees the HPS's stability. Hence, this paper aims to address the frequency regulation (FR) issue in a state-of-the-art modeled multi-source distributed HPS with a coordinated control approach. The modeled HPS includes a reheat thermal power system (RTPS), wind turbine generator (WTG), fuel cell (FC) stack, battery energy storage system (BESS), diesel engine generator (DEG), and various controllers.A novel method called parameter sensitivity algorithm (PSA) is proposed to obtain the RoC of the controller gains and is further optimized using a constrained genetic algorithm (GA). To demonstrate coordinated control effectiveness, a hybrid objective function for optimization is formulated as constrained GA's fitness function using the integral time absolute error (ITAE) and integral absolute error (IAE). Furthermore, a comparative assessment for PI, PID with filter (PIDN), cascaded PI-PD with filter (PIPDN) controller topologies is carried out using error index-based performance metrics to ascertain the preeminence of the hybrid objective function over existing objective functions. The investigation results validate that the suggested optimized controllers provide enhanced FR and are robust to uncertainties. The practical feasibility of the proposed methodology is reinforced using dSPACE hardware in loop testing.

Hyperspectral image classification using meta-heuristics and artificial neural network

Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of correlation and possess unique information that can be used for precise image classification. The precise selection of useful features from these high dimensional band information is very important to reduce the challenge of hyper spectral image classification approaches. Nowadays, metaheuristic algorithms are immensely utilized as a promising tool for hyperspectral image classification. In the present research work, hyperspectral images are classified with the various combinations of meta-heuristic approaches and the neural network including the mostly used Cuckoo Search (CS) optimization algorithm to resolve the global optimization search problems considering the improvement needed in image classification. Further, the strength of CS is improved using the integration of the Genetic Algorithm (GA) fitness function within the CS. The feature selection is performed by the hybrid CS and GA algorithm and the optimized features are then fed to ANN for training and classification. The paper has shown a comparative analysis of various meta heuristics techniques with ANN on parameters like kappa coefficient, Class accuracy and overall Accuracy and the designed algorithms are tested on the Indian Pines dataset. The proposed CS and GA with ANN outperformed the two already existing works with an overall average accuracy of 97.30% and a kappa coefficient of 0.9760.

A novel adaptive predictive control strategy of hybrid radiant-air cooling systems operating in desert climates

Hydronic radiant-air cooling systems have proved to be highly energy-efficient compared to all-air systems. However, such systems require advanced control strategies to overcome their slow thermal response and potential water vapor condensation on the radiant surfaces. Yet, there is a clear lack of proposed adaptive controllers in the literature to collaboratively manage both radiant and air sides of the system. Such adaptive controllers are necessary for reliable and stable operation with minimal energy consumption. In this study, a novel offline adaptive predictive control strategy is proposed for the advantages of maximizing the energy savings of hybrid radiant-air systems, located in desert climate zones, while maintaining a higher quality indoor environment. A transient simulation model is developed in TRNSYS v.18 based on a typical medium-office space, located in Cairo, Egypt, with two air-cooled chiller loops serving a thermally activated building system and a dedicated outdoor air system. A multi-input multi-output multi-layered perceptron artificial neural network model has been developed in MATLAB based on batch simulations, performed in TRNSYS, to predict the electricity consumption and Predicted Percentage Dissatisfied (PPD) for different setpoints. The trained model was used as a fitness function of a multi-objective genetic algorithm, coupled with a decision-making criterion, to determine chillers’ setpoint temperatures that minimize the electricity consumption and thermal discomfort on a half-hourly simulation basis. The proposed controller was compared to a baseline three-step feedback controller, which turns the water circulation pumps on or off depending on the difference between the setpoint temperature and the indoor air temperature, as well as the initial state of the pumps (on or off). The results show that the proposed strategy substantially improves the indoor thermal environment, where the percentage of comfortable occupied hours is increased by 25.1%. The proposed controller also reduced the peak electricity consumption by 18.8%. However, the total energy consumption of the cooling season was lower by only 2.7% due to the longer operating hours of the chillers and the circulation pumps with this controller. Different strategies for further reduction of total energy consumption with a minimal tuning of the proposed controller are recommended for future studies.

Flexible Zoom Telescopic Optical System Design Based on Genetic Algorithm

The performance of current liquid zoom systems is severely limited by their initial structure’s construction and solution. In this study, an automatic search method based on genetic algorithm (GA) was proposed for obtaining the optimal initial structure of a double liquid lens zoom optical system. This method was used to design a zoom telescopic objective with a fast response characteristic. The zoom equation of the zoom system was derived based on the Gaussian bracket method, and an initial structure evaluation function that integrated the aberration, the system length, and the smoothness of the focal power change in the liquid lenses was designed. This evaluation function was used as the fitness function in GA to automatically retrieve the optimal initial structure of the zoom system. Finally, an optical design software was used to optimize the design of the zoom system to obtain the final structure of the zoom system. Image quality analysis and tolerance analysis of the zoom system revealed that the system exhibited excellent imaging capability and could be manufactured easily. In addition, the analysis of the zoom curve revealed that the optical system exhibited a smooth continuous zooming capability.

BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets.

The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of machine-learning (ML) and deep-learning (DL) models. Here, we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 35 out of 42 performance comparisons between BiGAMi and other feature selection methods evaluated here (sequential forward selection, SelectKBest, and GARS), BiGAMi achieved its results by selecting 6–93% fewer features. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition.

Hybrid Neuro-Genetic Machine Learning Models for the Engineering of Ring-spun Cotton Yarns

ABSTRACT Yarn engineering is a long-standing problem for the cotton spinning industry as the functional relationship between fiber and yarn properties is quite complex. The objective of this research is to develop a hybrid machine learning-based prescriptive yarn engineering system that can foretell the properties of cotton fiber for achieving desired yarn properties. Artificial neural network (ANN) and genetic algorithm (GA) were used to develop the predictive model for cotton yarn properties and optimization of cotton fiber properties, respectively. Two separate ANN models were developed for predicting yarn tenacity and yarn unevenness. The functional relationships approximated by the ANN models were used to formulate the fitness function for GA. The validation of the ANN-GA system demonstrated good accuracy as cotton fiber strength, length and length uniformity were predicted with very good accuracy (mean error < 5%). The developed machine learning system can supplant the intuition-based decision making in textile spinning industry and pave the way for yarn engineering.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Energy efficiency is a critical issue in data centre management, which is the foundation for cloud computing. The VM placement has a considerable impact on a data centre's energy efficiency and resource utilisation. The assignment of VMs to PMs is an NP-hard problem without an easy way to find an optimal solution, particularly in large-scale data centres. In this study, the VM placement problem is formulated as a constrained optimisation problem. The Genetic Algorithm (GA) is a suitable method for solving this problem in terms of the quality of the solution. However, GA is time-consuming to obtain an optimal solution in the large scale optimisation problem. Therefore, this paper focuses on accelerated GA for energy-efficient VM placement. As the most time-consuming element of the GA is the calculation of its fitness function, this paper simplifies this calculation through a new fitness function in GA. Simulation results of small-, medium-, and large-scale data centres demonstrate that our accelerated GA is faster than the standard GA and gives better quality of solution than the First Fit Decreasing (FFD) algorithm, respectively. The findings of our GA with the new fitness function reveal an 8% energy saving for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA with standard energy formula as a fitness function. The number of generations in our GA is reduced by about 50% in comparison with the standard GA. Moreover, we started with 3000 PMs in the large-scale dataset, and only 1086 PMs were actually used after running our GA. Therefore, we may switch off far more PMs for energy savings from our GA results than those from the standard GA.