GA-based Approach Research Articles

Research on Broadband Oscillation Suppression Strategy in Power System Based on Genetic Algorithm

This examination presents an original Broadband Oscillation Concealment Procedure in Power Systems utilizing a Genetic Algorithm (GA). The philosophy’s suitability is deliberately assessed through comprehensive examinations, including affiliation investigation, strength appraisal, and near investigations with elective optimization algorithms. Results show that the GA-based approach displays predominant affiliation, appearing at a health worth of 0.05 after 100 ages, beating Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA). Strength examination features the versatility of the proposed procedure, with a standard wellbeing worth of 0.08 ± 0.02 under changing power framework conditions. Similar investigation against related work reveals the procedure’s advantage, showing its genuine breaking point with regards to helpful broadband oscillation concealment. The GA-based philosophy changes speedy mixing and computational capacity, with an ordinary execution season of 120 seconds. The examination contributes important pieces of information into power framework strength, offering a good answer for mitigating broadband oscillations in various working situations.

A Novel Algorithm Application in Vocal Signals Processing.

This study proposes an innovative application of the Goertzel Algorithm (GA) for the processing of vocal signals in dysphonia evaluation. Compared to the Fast Fourier Transform (FFT) representing the gold standard analysis technique in this context, GA demonstrates higher efficiency in terms of processing time and memory usage, also showing an improved discrimination between healthy and pathological conditions. This suggests that GA-based approaches could enhance the reliability and efficiency of vocal signal analysis, thus supporting physicians in dysphonia research and clinical monitoring.

Cloud Based Ccmc Model for Application of Genetic Algorithm Based on Cloud Computing in Art Product Design

Art design has entered a new era with the integration of cloud computing technology, revolutionizing the creative process and expanding artistic possibilities. By leveraging the vast computational power and storage capabilities of cloud platforms, artists can explore innovative techniques, collaborate with peers remotely, and access a wealth of digital resources from anywhere in the world. Cloud computing enables artists to work with large-scale datasets, create complex visualizations, and render high-resolution artworks with ease. Moreover, cloud-based tools and applications offer flexibility and scalability, allowing artists to experiment freely and iterate rapidly. Whether creating digital paintings, 3D animations, or interactive installations, cloud computing empowers artists to push the boundaries of their creativity and bring their visions to life in ways previously unimaginable. This paper presents an innovative application of genetic algorithm (GA) leveraging cloud computing technology for art product design, with a focus on the Centralized Clustering Middle Chain (CCMC) framework. By harnessing the computational power and scalability of cloud platforms, GA facilitates the optimization of design parameters to generate novel and aesthetically pleasing art products. The CCMC framework streamlines the design process by centralizing data clustering and analysis, enabling efficient exploration of design space and identification of optimal solutions. Through simulated experiments and empirical evaluations, the effectiveness of the GA-based approach in art product design is assessed. Results demonstrate significant improvements in design quality and efficiency, with the GA-enabled cloud computing solution achieving a 40% reduction in design iteration time and a 30% increase in product innovation compared to traditional methods.

An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History

The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system.

A Solution to Graph Coloring Problem Using Genetic Algorithm

INTRODUCTION: The Graph Coloring Problem (GCP) involves coloring the vertices of a graph in such a way that no two adjacent vertices share the same color while using the minimum number of colors possible.
 OBJECTIVES: The main objective of the study is While keeping the constraint that no two neighbouring vertices have the same colour, the goal is to reduce the number of colours needed to colour a graph's vertices. It further investigate how various techniques impact the execution time as the number of nodes in the graph increases.
 METHODS: In this paper, we propose a novel method of implementing a Genetic Algorithm (GA) to address the GCP.
 RESULTS: When the solution is implemented on a highly specified Google Cloud instance, we likewise see a significant increase in performance. The parallel execution on Google Cloud shows significantly faster execution times than both the serial implementation and the parallel execution on a local workstation. This exemplifies the benefits of cloud computing for computational heavy jobs like GCP.
 CONCLUSION: This study illustrates that a promising solution to the Graph Coloring Problem is provided by Genetic Algorithms. Although the GA-based approach does not provide an optimal result, it frequently produces excellent approximations in a reasonable length of time for a variety of real-world situations.

Exploring Heuristic and Optimization Approaches for Elevator Group Control Systems

This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time.

GA-based QOS-aware workflow scheduling of deadline tasks in grid computing

Grid computing is the aggregation of the power of heterogeneous, geographically distributed computing resources to provide high-performance computing. To benefit from the grid computing capabilities, effectual scheduling algorithms are primarily essential. This paper presents a GA-based approach, called Grid Workflow Tasks Scheduling Algorithm (GWTSA), for scheduling workflow tasks on grid services based on users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time be within the time constraints (deadline) imposed by the user. In GWTSA, the workflow tasks are modeled as a DAG, which is divided, then the optimal sub-schedules of all task divisions are computed and used to obtain the execution schedule of the entire workflow. A GA-based technique is employed in GWTSA to compute the optimal execution sub-schedule for each branch division that consists of a set of sequential tasks. In this technique, the chromosome represents a branch division, where each gene holds the id of the service provider chosen to execute the corresponding task in the branch; and the fitness function is formulated as a multi-objective function of time and cost, this gives users the ability to determine their requirements if speed against cost or vice versa, by changing the weighting coefficients in the fitness function. The paper also exhibits the experimental results of assessing the performance of GWTSA with workflow samples of different sizes.

Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture.

Nowadays, federated learning is one of the most prominent choices for making decisions. A significant benefit of federated learning is that, unlike deep learning, it is not necessary to share data samples with the model owner. The weight of the global model in traditional federated learning is created by averaging the weights of all clients or sites. In the proposed work, a novel method has been discussed to generate an optimized base model without hampering its performance, which is based on a genetic algorithm. Chromosome representation, crossover, and mutation-all the intermediate operations of the genetic algorithm have been illustrated with useful examples. After applying the genetic algorithm, there is a significant improvement in inference time and a huge reduction in storage space. Therefore, the model can be easily deployed on resource-constrained devices. For the experimental work, sports data has been used in balanced and unbalanced scenarios with various numbers of clients in a federated learning environment. In addition, we have used four famous deep learning architectures, such as AlexNet, VGG19, ResNet50, and EfficientNetB3, as the base model. We have achieved 92.34% accuracy with 9 clients in the balanced data set by using EfficientNetB3 as the base model using a GA-based approach. Moreover, after applying the genetic algorithm to optimize EfficientNetB3, there is an improvement in inference time and storage space by 20% and 2.35%, respectively.

Hybrid Renewable Energy Microgrids: A Genetic Algorithm Approach to System Design

The paper examines the use of genetic algorithm (GA) methods to optimize hybrid renewable energy microgrids by merging various renewable sources and energy storage technologies. An examination of meteorological data over many days reveals fluctuations in solar irradiance ranging from 4.8 kW/m² to 5.5 kW/m² and wind speed oscillating between 3.9 m/s and 4.5 m/s, indicating the presence of dynamic weather conditions. An analysis of energy generating capabilities reveals a wide range of potentials, with solar capacities varying from 80 kW to 150 kW and wind capacities ranging from 60 kW to 120 kW across different sources. An analysis of Energy Storage System (ESS) specifications shows a range of values for maximum capacities, charge/discharge efficiencies (ranging from 85% to 96%), and maximum charge/discharge rates (from 60 kW to 100 kW), highlighting the need for flexible energy storage systems. The examination of microgrid load profiles reveals the presence of diverse energy needs, with residential loads oscillating between 48 kW and 55 kW, commercial loads ranging from 40 kW to 47 kW, and industrial loads spanning from 30 kW to 36 kW. A percentage change study reveals the ability to adapt, with solar irradiance and wind speed showing mild fluctuations of roughly 14% and nearly 15% respectively. In contrast, renewable source capacity demonstrate significant percentage changes ranging from around 40% to 50%. These results highlight the ever-changing characteristics of renewable energy sources, underlining the need for strong optimization tactics in microgrid systems. The study emphasizes the potential of GA-based approaches in developing efficient microgrids, promoting sustainable and dependable energy solutions in the face of changing environmental circumstances and varied energy requirements.

Enhancing acoustic contrast in multi-zone sound field reproduction through optimization of loudspeaker arrangements

In this study, we propose a stepwise-based approach, predefined in descending order of source power, to optimize loudspeaker placement in multi-zone sound field reproduction systems. The aim is to enhance the acoustic contrast (AC) between acoustically bright and dark zones. The proposed method is benchmarked against two previously studied approaches: the Genetic Algorithm (GA) and the Greedy-based approach. Numerical simulations are conducted to evaluate and compare the performance of each method. Results demonstrate that the proposed method surpasses the Greedy-based approach in terms of AC, while offering performance comparable to the GA-based approach. Importantly, the proposed method exhibits lower computational complexity than the GA, suggesting a more practical solution for real-world applications.

Optimizing power transfer in selective wireless charging systems: A genetic algorithm-based approach

This paper presents a novel approach to optimize selective wireless power transmission to multiple receivers utilizing a single transmitter. The primary focus is on achieving target power levels at specific resonant frequencies on each receiver while ensuring maximum transmission efficiency. To address this, an analytical model based on magnetic coupling between coils is employed to explore different solutions, and a Genetic Algorithm (GA) is applied to identify the optimal configuration. The application of a GA for system design optimization is introduced, which proves highly effective, especially when dealing with multiple receivers where analytical models become difficult to manage. The GA-based approach facilitates the discovery of numerical solutions that might be challenging to deduce analytically. The validity of the proposed approach is confirmed through simulation and experimental measurements.

An improved GA-based approach for reduced non-discriminatory renewable energy curtailment

An improved GA-based approach for reduced non-discriminatory renewable energy curtailment

Hybrid flow-shop manufacturing network reliability optimization using genetic algorithm and absorbing Markov chain

In this study, we adopt a strategy of machine configuration to construct a reliable hybrid flow-shop manufacturing system (HFSMS) with reworking and scrapping actions, in which the reworking and scrapping actions are due to the yield rates of the configured machines. The machine configuration determines the machine suppliers and the number of machines for each production stage in the HFSMS, which may affect the stability of the HFSMS. The machines configured to a production stage in parallel are provided by the same supplier. Accordingly, each production stage has multiple states, following a probability distribution. The HFSMS is a typical stochastic-flow network under machine configuration, and network reliability indicates the probability that order demand d can be fulfilled by the HFSMS and used as a decision reference. Our study integrates genetic algorithm (GA) and absorbing Markov chain (AMC) to solve the reliability-oriented machine configuration problem of HFSMS subject to a configuration budget, where the GA is utilized to find the optimal machine configuration with maximum network reliability and the AMC model constructed based on the yield rates is used for network reliability evaluation. For validating the applicability and computational efficiency of the proposed AMC and GA-based approach, a simple manufacturing system and two practical manufacturing systems are used to compare the proposed approach with four popular meta-heuristic algorithms. The experimental results show that the proposed approach can find the exact optimal solution and has better computational efficiency than the other four meta-heuristic algorithms.

Robustness Estimation and Optimisation for Semantic Web Service Composition With Stochastic Service Failures

Service-oriented architecture (SOA) is a widely adopted software engineering paradigm that encourages modular and reusable applications. One popular application of SOA is web service composition, which aims to loosely couple web services to accommodate complex goals not achievable through any individual web service. Many approaches have been proposed to construct composite services with optimized Quality of Service (QoS), assuming that QoS of web services never changes. However, the constructed composite services may not perform well and may not be executable later due to its component services' failure. Therefore, it is important to build composite services that are robust to stochastic service failures. Two challenges of building robust composite services are to efficiently generate service composition with near-optimal quality in a large search space of available services and to accurately measure the robustness of composite services considering all possible failure scenarios. This article proposes a novel two-stage GA-based approach to robust web service composition with an adaptive evolutionary control and an efficient robustness measurement. This approach can generate robust composite service at the design phase, which can cope with stochastic service failures and maintain high quality at the time of execution. We have conducted experiments with benchmark datasets to evaluate the performance of our proposed approach. Our experiments show that our method can produce highly robust composite services, achieving outstanding performance consistently in the event of stochastic service failures, on service repositories with varying sizes.

A GA-based approach with an interval VIKOR method for solving the constrained QoS-aware service composition in dynamic IoT environments

A GA-based approach with an interval VIKOR method for solving the constrained QoS-aware service composition in dynamic IoT environments

A GA-based approach with an interval VIKOR method for solving the constrained QoS-aware service composition in dynamic IoT environments

A GA-based approach with an interval VIKOR method for solving the constrained QoS-aware service composition in dynamic IoT environments

Power-Aware Testing for Maximum Fault Coverage in Analog and Digital Circuits Simultaneously

In this paper, a method for maximum fault coverage with minimum power dissipation, during the testing of analog and digital circuits of mixed-signal System-on-Chip (SOC) simultaneously using Genetic Algorithm, (GA) is proposed. Mixed-signal SOC consists mainly of an analog block, a digital block and a DAC/ADC (Digital to Analog Converter/Analog to Digital Converter). Due to the presence of analog and digital circuits in mixed-signal SOCs, the testing procedure is difficult from that of only analog or digital circuit testing. Here stuck at 0/1 faults are considered for digital circuits, and stuck open/short faults are considered for analog circuits. In analog testing fault modeling, fault injection and fault simulation are done. The outputs of the analog block and some independent digital signals are given to the digital block. The GA-based approach is used for power-aware ordering of test patterns considering pattern dependency on previous patterns at the input of digital block as input patterns. The effect of noise on analog test signals has also been investigated here with the area analysis of the circuit. An average of 92.43% fault coverage and 13.56% maximum power saving is achieved when this methodology is applied to ITC 97 (Analog block) and ISCAS 85 (Digital block) benchmark circuits, respectively. A trade-off between fault coverage and power dissipation has been presented.

Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment.

With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition. We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically. As a proof of concept, we implemented the suggested GA-based approach in popular Matt and Caretta multiple protein 3D-structure alignment (M3DSA) algorithms, leading to a statistically significant improvement of the TM-score quality indicator by up to 220-1523% on 'SABmark Superfamilies' (in 49-77% of cases) and 'SABmark Twilight' (in 59-80% of cases) datasets. The observed improvement in collections of distant homologies highlights the potentials of GA to optimize 3D-alignments of diverse protein superfamilies as one plausible tool to study the structure-function relationship. The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps. Supplementary data are available at Bioinformatics online.

A novel genetic algorithm for curriculum sequence optimization

A curriculum sequence represents a match between learners’ preferences, needs, and surroundings from one side, and the learning content characteristics and the pedagogical requirements from the other side. The curriculum sequence adaptation problem (CSA) is considered as an important issue in adaptive and personalized learning field. It concerns the dynamic generation of a personal optimal learning path for a specific learner. This problem has gained an increased research interest in the last decade, and heuristics and meta-heuristics are usually used to solve it. In this direction, this paper summarizes existing works and presents a novel GA-based approach modeled as an objective optimization problem to deal with this problem. The experimental results from simulations showed that the proposed GA could outperform particle swarm optimization (PSO) and a random search approach in many simulated datasets. Moreover, from a pedagogical perspective, positive learners’ feedback and high acceptance towards the proposed approach is indicated.

Artificial intelligence applied to investment in variable income through the MACD (moving average convergence/divergence) indicator

PurposeThis study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.Design/methodology/approachThe study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.FindingsThe paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.Research limitations/implicationsLimitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.Practical implicationsThe findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.Originality/valueThis paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.