Advantages Of Genetic Algorithm Research Articles

Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand

ABSTRACTThis paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA‐driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research.

Optimization of Joint Scheduling for Automated Guided Vehicles and Unmanned Container Trucks at Automated Container Terminals Considering Conflicts

Port development is a critical component in constructing a resilient transportation infrastructure. The burgeoning integration of automated guided vehicles (AGVs) within container terminals, in conjunction with the orchestrated scheduling of unmanned container trucks (UCTs), is essential for the sustainable expansion of port operations in the future. This study examined the influence of AGVs in automated container terminals and the synergistic scheduling of UCTs on port operations. Comparative experiments were meticulously designed to evaluate the feasibility of integrated scheduling schemes. Through the development of optimization models that consider conflict-free paths for both AGVs and UCTs, as well as strategies for conflict resolution, a thorough analysis was performed. Advanced genetic algorithms were engineered to address task-dispatching models. In contrast, the A* optimization search algorithm was adapted to devise conflict-free and conflict-resolution paths for the two vehicle types. A range of scaled scenarios was utilized to assess the impact of AGVs and UCTs on the joint-scheduling process across various configuration ratios. The effectiveness of the strategies was appraised by comparing the resultant path outcomes. Additionally, comparative algorithmic experiments were executed to substantiate the adaptability, efficacy, and computational efficiency of the algorithms in relation to the models. The experimental results highlight the viability of tackling the joint-scheduling challenge presented by AGVs and UCTs in automated container terminals. When juxtaposed with alternative scheduling paradigms that operate independently, this integrated approach exhibits superior performance in optimizing the total operational costs. Consequently, it provides significant insights into enhancing port scheduling practices.

Simultaneous design optimisation methodology for floating offshore wind turbine substructure and feedback-based control strategy

This research article explores the application of control co-design methodologies for optimising floating offshore wind turbine systems concurrently. The primary objective is to offer insights into concurrent design approaches employing an advanced genetic optimisation algorithm. To achieve this, a reduced-order dynamic model is employed to minimise computational time requirements, complemented by a modified version of the levelized cost of energy equation serving as the cost function. Furthermore, various optimisation scenarios are investigated under diverse wind and wave conditions to assess the advantages and drawbacks of increasing the complexity of dynamic cases used in evaluating the cost function. The optimised system designs are then compared against baseline floating system designs to underscore the advantages of employing this approach to floating wind turbine design.

An Integrated Geometric Obstacle Avoidance and Genetic Algorithm TSP Model for UAV Path Planning

In this paper, we propose an innovative approach for the path planning of Uninhabited Aerial Vehicles (UAVs) that combines an advanced Genetic Algorithm (GA) for optimising missions in advance and a geometrically based obstacle avoidance algorithm (QuickNav) for avoiding obstacles along the optimised path. The proposed approach addresses the key problem of determining an optimised trajectory for UAVs that covers multiple waypoints by enabling efficient obstacle avoidance, thus improving operational safety and efficiency. The study highlights the numerous challenges for UAV path planning by focusing on the importance of both global and local path planning approaches. To find the optimal routes, the GA utilises multiple methods of selection to optimise trajectories using the Cartesian Coordinate System (CCS) data transformed from a motion capture system. The QuickNav algorithm applies linear equations and geometric methods to detect obstacles, guaranteeing the safe navigation of UAVs and preventing real-time collisions. The proposed methodology has been proven useful in reducing the total distance travelled and computing times and successfully navigating UAVs across different scenarios with varying numbers of waypoints and obstacles, as demonstrated by simulations and real-world UAV flights. This comprehensive approach provides advantageous perspectives for real-world applications in a variety of operational situations and improves UAV autonomy, safety, and efficiency.

Development of a method for managing technical systems using a bio-inspired algorithm

Today’s management solutions depend precisely on the successful solution of optimization problems, which are discontinuous, undifferentiated and multimodal. One of the approaches to increase the efficiency of solving optimization problems is bio-inspired algorithms. The object of the study is complex dynamic objects. The subject of the study is the decision-making process in the problems of managing complex dynamic objects. A management method using a bio-inspired algorithm is proposed. The research is based on the goose algorithm – for finding a solution to the state of dynamic objects with a hierarchical structure. Evolving artificial neural networks are used to train goose agents (GA) and an advanced genetic algorithm is used to select the best ones in the combined swarm algorithm. The originality of the proposed method lies in setting GA taking into account the uncertainty of the initial data, improved global and local search procedures. Also, the originality of the study lies in determining GA food locations, which allows choosing the priority of search in a given direction. The next element in the originality of the study is the ability to determine the indicators of guard GA, which allows adjusting the amount of time during which the GA flock will be located. Another original element of the study is the determination of the initial velocity of each GA. This makes it possible to optimize the speed of conducting exploration by each GA in a certain research direction. The method allows increasing the efficiency of data processing at the level of 10–12 % by using additional improved procedures. The proposed method should be used to solve problems of evaluating complex dynamic objects

Performance prediction and geometry optimization of ejector in PEMFC system using coupled CFD-BPNN and genetic algorithm

The ejector is a promising hydrogen recirculation device in proton exchange membrane fuel cell (PEMFC) systems. However, the limited entrainment performance of the ejector at wide operating conditions hinders its development and widespread application in PEMFC systems. To address this challenge and design a high-performance ejector adapted to the wide power range of PEMFC systems, a backpropagation neural network (BPNN) model with computational fluid dynamics (CFD) simulation data is developed. The sensitivity analysis of geometric parameters based on the CFD model reveals that the nozzle throat diameter and the mixing chamber diameter exert the most pronounced impact on the entrainment performance, with average influence rates of 24% and 57%, respectively. Furthermore, two advanced multi-objective genetic algorithms are applied to improve ejector performance. The linear weighted genetic algorithm (LWGA) method proves effective in elevating the overall ejector performance, achieving a remarkable enhancement in entrainment performance of up to 7.9%. On the other hand, the non-dominated sorting genetic algorithm (NSGA- II) method is favorable for expanding the operational power range of the ejector by 30%, as well as a 4.5% increase in overall ejector performance. This work provides a robust framework for designing and optimizing high-performance ejectors in high-power PEMFC systems.

Development of a solution search method using artificial intelligence

The object of the study is decision support systems. The problem of increasing decision-making efficiency in conditions of uncertainty and a set of different parameters was solved using a bio-inspired algorithm. The subject of the study is the decision-making process in management problems using the heron flock algorithm, the improved genetic algorithm and evolving artificial neural networks. A solution search method using the improved heron flock algorithm is proposed. The study is based on the heron flock algorithm to find a solution regarding the object state. Evolving artificial neural networks are used to train the heron flock algorithm, and an advanced genetic algorithm is used to select the best individuals of the heron flock. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering heron agents in the flock; – setting the initial velocity of heron agents; – waiting strategy for heron agents; – aggressive strategy; – checking the discriminatory condition; – selection of the best individuals from the heron flock; – ranking and sorting the obtained solutions; – training heron knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method consists in setting heron agents taking into account the uncertainty of the initial data, the noise degree of data about the analysis object state. The method makes it possible to reduce the time for decision-making at the level of 22–26 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations.

Development of a solution search method using a combined bio-inspired algorithm

The object of the study is decision support systems. The subject of the study is the decision-making process in management problems using a combined bio-inspired algorithm, consisting of: – the improved wolf optimization algorithm and the improved sparrow search algorithm – for solving optimization problems regarding the object state; – an advanced genetic algorithm – for selecting the best agents in flocks; – an advanced training method – for deep training of agents to improve the optimization characteristics of agents. A solution search method using an improved bio-inspired algorithm is proposed. The method has the following sequence of actions: – input of initial data; – initialization of the search for a flock of sparrows and its parameters; – ranking and selection of sparrow agents using an advanced genetic algorithm; – updating the sparrow location for the discoverer; – checking the conditions for updating the position of sparrows; – initialization of additional search parameters; − running the gray wolf optimization algorithm; – training agents’ knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method lies in the combined use of bio-inspired algorithms, setting agents taking into account the uncertainty of the initial data, advanced global and local search procedures. The method makes it possible to increase the efficiency of data processing at the level of 19 % using additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

Development of a method of increasing the efficiency of decision-making in organizational and technical systems

The object of the study is organizational and technical systems. The subject of the study is the decision-making process in the problems of management of organizational and technical systems. A method of increasing the efficiency of decision-making in organizational and technical systems using artificial intelligence is proposed. The research is based on the giant armadillo swarm algorithm to find a solution regarding the state of organizational and technical systems. Giant armadillo agents (GAA) are trained using evolving artificial neural networks, and an advanced genetic algorithm is used to select the best GAA. The method has the following sequence of actions: – input of initial data; – setting GAA on the search plane; – numbering GAA in the swarm; – determining the initial velocity of GAA; – preliminary evaluation of the GAA search area; – classification of food sources for GAA; – sorting the best GAA individuals; – attack on termite mounds by GAA; – digging termite mounds by GAA; – updating GAA positions; – checking for the presence of a GAA predator; – escape and fight against GAA predators; – checking the stop criterion; – training GAA knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method lies in setting GAA taking into account the uncertainty of the initial data, advanced procedures of global and local search taking into account the noise degree of data on the state of organizational and technical systems. The method makes it possible to increase the efficiency of data processing at the level of 14–19 % using additional advanced procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes

Advanced Genetic Algorithm for Optimal Microgrid Scheduling Considering Solar and Load Forecasting, Battery Degradation, and Demand Response Dynamics

Advanced Genetic Algorithm for Optimal Microgrid Scheduling Considering Solar and Load Forecasting, Battery Degradation, and Demand Response Dynamics

Research on genetic algorithm optimization for agricultural machinery operation path planning

Abstract Food security is a critical strategic concern in agricultural production, where agrarian machinery plays a vital role as a fundamental input and a crucial tool for enhancing production efficiency. This paper details a methodology utilizing Global Navigation Satellite System (GNSS) measurement software to analyze farmland topography. This process involves terrain measurement, data acquisition concerning the terrain, and subsequent processing to create a raster map of the farmland. The path planning for agricultural machinery is then refined through an innovative application of the A* algorithm, which selects optimal routes on the raster for initial path generation. This study introduces a genetic algorithm (GA) to facilitate global path planning optimization and designs a local path planning algorithm based on a cost function. Both unobstructed and obstructed regular farmland grids are subjected to simulation analyses to evaluate the efficacy of the path-planning approach. Comparative analysis indicates that the A*-GA algorithm significantly outperforms other algorithms in various metrics, including the number of steering occurrences, the count of repeated job grids, and the job repetition rate. Specifically, when compared to the Particle Swarm Optimization (PSO) algorithm, the A*-GA algorithm demonstrates a reduction of 6.3 in the number of repeated job grids and a 2.735% decrease in the job repetition rate. Similarly, it shows a reduction of 6.2 in repeated job grids and a 2.582% decrease in the job repetition rate compared to the standalone GA algorithm. Furthermore, the enhanced genetic algorithm enables agricultural machinery to adeptly avoid obstacles, thereby ensuring operational safety and achieving the desired endpoint along the planned path. The findings underscore that the advanced genetic algorithm effectively orchestrates obstacle avoidance for agricultural robots, thus ensuring continuous operation and adherence to safety standards in agricultural machinery deployment. This integration of GNSS with advanced algorithmic strategies marks a significant advancement in precision agriculture, optimizing machinery pathways of improved farm outcomes.

Development of a solution search method using the improved emperor penguin algorithm

The objects of the study are decision support systems. The subject of the study is the decision-making process in management problems using the Emperor Penguin Algorithm (EPA), an advanced genetic algorithm and evolving artificial neural networks. A solution search method using the improved EPA is proposed. The study is based on the EPA algorithm for finding a solution regarding the object state. Evolving artificial neural networks are used to train EPA, and an advanced genetic algorithm is used to select the best EPA. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering EPA in the flock; – setting the initial velocity of the EPA and thermal radiation of each EPA; – calculation of the position of each EPA on the total search area and its cost; – approach (attraction) of the EPA to another EPA; – changing in the trajectory of EPA movement; – selection of the best individuals from the EPA flock; – ranking the obtained solutions and sorting them; – training EPA knowledge bases; – determining the amount of necessary computing resources for an intelligent decision support system. The originality of the proposed method lies in setting EPA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the noise degree of data on the state of the analysis object. The method makes it possible to increase the efficiency of data processing at the level of 13–17 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems

Development of a solution search method using an advanced flying squirrel algorithm

The object of the study is decision support systems. The subject of the study is the decision-making process in management problems using the flying squirrel algorithm (FSA), an advanced genetic algorithm and evolving artificial neural networks. A solution search method using an advanced FSA is proposed. The study is based on the FSA algorithm for finding a solution regarding the state of an object. Evolving artificial neural networks are used to train FSA, and an advanced genetic algorithm is used to select the best FSA. The method has the following sequence of actions. Input of initial data and setting agents on the search plane take place. After that, numbering FSA in the flock and setting the initial fitness function are carried out. Then, the quality of food in the FSA search area is determined, and the classification of trees (food sources) for FSA is carried out. The next step is the creation of new locations by FSA gliding, formation of the FSA action algorithm in the presence of a predator. After that, the FSA seasonal monitoring conditions are checked, the stop criterion is checked, and new FSA positions are generated taking into account the degree of data noise. The originality of the proposed method lies in setting FSA taking into account the uncertainty of the initial data, advanced global and local search procedures taking into account the noise degree of data on the state of the analysis object. The method makes it possible to increase the efficiency of data processing at the level of 21–25 % due to the use of additional advanced procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems

The development of method for increasing the decision making efficiency in organizational and technical systems

The object of research is decision making processes in decision making support systems. The subject of the research is a method of decision making in management tasks using the walrus flock algorithm (WA), an advanced genetic algorithm and evolving artificial neural networks. A method of finding solutions using an improved walrus flock algorithm is proposed. The research is based on the walrus flock algorithm for finding a solution to the object’s condition. Evolving artificial neural networks are used to train walrus agents and an advanced genetic algorithm is used to select the best walrus agents. The method has the following sequence of actions: – input of initial data; – WA numbering in the flock; – determination of the initial speed of WA; – display of WA along the search plane; – preliminary assessment of the WA search area; – classification of food sources for WA; – sorting of the best WA individuals; – an update of WA positions; – WA migration; – checking the presence of a predator; – checking the stop criterion; – escape and struggle with predators; – checking the stop criterion; – training of WA knowledge bases; – determination of the amount of necessary computing resources, intelligent decision making support system. The originality of the proposed method lies in the placement of WA taking into account the uncertainty of the initial data, improved procedures of global and local edge taking into account the degree of noise of data on the state of organizational and technical systems. The use of the method makes it possible to increase the efficiency of data processing at the level 13–16 % due to the use of additional improved procedures. The proposed method should be used to solve problems of evaluating complex and dynamic processes

Optimized fog community framework with advanced genetic algorithm for enhanced performance dynamics

Optimized fog community framework with advanced genetic algorithm for enhanced performance dynamics

Energy, exergy, economic and environmental (4E) analysis of two-stage cascade, Linder-Hampson and reverse Brayton systems in the temperature range from −120 °C to −60 °C

Cryopreservation in the temperature range from −120 °C to −60 °C is an important way to achieve high-quality food storage. In this paper, an in-depth quantitative thermodynamic comparison of two-stage cascade cycle, Linde-Hampson cycle and reverse Brayton cycle was carried out in the temperature range from −120 °C to −60 °C. Aspen HYSYS was used to invoke advanced genetic algorithm in MATLAB to optimize the system parameters. The two-stage cascade system performs best at the evaporation temperatures above −80 °C, achieving a COP of 0.97 at −60 °C. The Linder-Hampson system performs best when the evaporation temperature is below −90 °C, achieving a COP of 0.35 at −120 °C. In order to further explore the impact of environmental change on the system performance, five typical cities in China were selected to analyze the seasonal performances of the three systems. The seasonal analysis demonstrated that Harbin has the highest latitude and its seasonal energy efficiency ratio is significantly higher than other cities, the performance of the two-stage cascade system fluctuates more obvious within 24-h. What's more, the Linder-Hampson system has the lowest annual investment cost and annual operating cost at the evaporation temperature lower than −90 °C, as well as the lowest emissions of CO2 and SO2.

The development of solution search method using improved jumping frog algorithm

The object of research is the decision making support systems. The subject of the research is the decision making process in management tasks using an advanced jumping frog algorithm (JFA), an advanced genetic algorithm and evolving artificial neural networks. A method of finding solutions with the use of improved JFA is proposed. The research is based on the JFA to find a solution regarding the object state. Evolving artificial neural networks are used to train frog agents (FA). The method has the following sequence of actions: – an input of initial data; – processing of initial data taking into account the degree of uncertainty; – calculation of the value of the criterion of optimality of each permutation from the initial FA population – global search of FA; – an improvement of the FA position in the search space; – a regulation of the speed of vehicle movement. – an improvement of the working conditions of JFA; – the FA rearrangement; – an unification of all memplexes into one group; – the verification of the fulfillment of the conditions of JFA operation; – the search for the best FA; – training of the FA knowledge bases. The originality of the proposed method consists in the arrangement of the FA taking into account the uncertainty of the initial data, the improved procedures of global and local edge taking into account the degree of data noise about the analysis object state, the adjustment of the degree of data noise during the FA movement, the adjustment of the speed of the FA movement. Also, the peculiarity of the proposed method is the use of an improved procedure for FA training. The use of the method makes it possible to increase the efficiency of data processing at the level of 14–18 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems

Development of a solution search method using an improved fish school algorithm

The object of research is decision support systems. The subject of research is the decision-making process in management problems using the fish school (FSH) algorithm, an advanced genetic algorithm and evolving artificial neural networks. A solution search method using an improved FSH algorithm is proposed. The study is based on the FSH algorithm for finding a solution on the object state. For training FSH, evolving artificial neural networks are used. The method has the following sequence of actions: – input of initial data; – processing of initial data taking into account the degree of uncertainty; – checking the fitness function of the solution found; – procedure of feeding fish agents (FA); – instinctive-collective FA swimming; – calculation of the center of school gravity; – collective voluntary FA swimming; – changing the FA swimming parameters; – training of FA knowledge bases. The originality of the proposed method lies in the arrangement of FA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the degree of noise of data about the state of the analysis object. The peculiarity of the proposed method is the use of an improved FA training procedure. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. The use of the method makes it possible to increase the efficiency of data processing at the level of 18–25 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

Predicting solar distiller productivity using an AI Approach: Modified genetic algorithm with Multi-Layer Perceptron

Solar Stills (SSs) are an eco-friendly and efficient approach to generating drinking water from brackish or saline sources. In this paper, a novel model for predicting the productivity of Hemispherical SS is proposed. This model enhances traditional MLP using an advanced Genetic Algorithm (GA), which mimics the process of “seeded selection” in natural selection to determine optimal MLP parameters. The resulting model, named Enhanced Genetic Algorithm – Multi-Layer Perceptron (EGA-MLP), is compared against two other models, standalone MLP and Particle Swarm Optimization with MLP (PSO-MLP), as well as experimental results from SSs. The models were applied to predict the yield of three different SS designs, including Conventional Hemispherical Still Without Fins (CHSWF), Cylindrical Finned Hemispherical Still (CyFHS), and Conical Finned Hemispherical Still (CoFHS), under varying environmental conditions and fins spacing. Statistical criteria such as Root Mean Square Error (RMSE), coefficient of determination (R2), and Mean Absolute Error (MAE) were used to assess the models' performance. The EGA-MLP model exhibits the highest R2 values across all tests, with an average R2 score of 0.994. In comparison, the PSO-MLP and MLP models achieve average R2 scores of 0.949 and 0.93, respectively. Furthermore, the EGA-MLP model demonstrates the lowest values of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) among the three models. The proposed model demonstrated the most accurate predictions of SS yield compared to both the experimental results and the other two models in predicting SS yield and has the potential to improve the design and optimization of SS systems.