Genetic Algorithm Optimization Research Articles

Automated Optimization of Mixing Elements for Single-Screw Extrusion Using CFD Simulations

Today’s materials must meet high mechanical requirements while remaining cost-effective in production. This requires a homogeneous temperature and material distribution and splitting, as achieved by mixing elements in single-screw extrusion, which depends on the material properties, the geometry of the mixing elements, and the process conditions. Existing computational fluid dynamics (CFD) methods can help, but often optimize dispersive (split) or distributive (spread) mixing separately and neglect their mutual influence and competition, which prevents a single optimal solution. To address this, this work develops an automated optimization tool using a genetic algorithm for the holistic optimization of both mixing processes, considering pressure drop, temperature gradient, and quantitative metrics for dispersive and distributive mixing. Compromises between geometry and metrics that improve dispersive mixing while maintaining moderate temperature gradients and pressure drops were determined for four different polymers. The results of the dispersive mixing element show dependencies between mixing metrics and geometry. In contrast, the distributive mixing element shows no clear correlations between mixing metrics and geometry.

Investigation and optimal design of band gap tunability in fractal phononic crystals

This study investigates the properties of band gaps of circular core filling fractal phononic crystals (CCFFPCs), specifically focusing on the impact of different filling positions on the frequency of band gaps. The research demonstrates that core filling at the central positions significantly influences the formation and widening of low-frequency band gaps, while filling at corner edges predominantly affects mid-frequency band gaps, and filling at edge centers effectively opens and broadens high-frequency band gaps. These results reveal the relationship between filling positions and band gap tuning, providing a theoretical foundation for precise band gap control across a full frequency range. Moreover, this study is the first to systematically clarify the impact of core filling positions on band gap frequencies, expanding the design strategies for band gaps in fractal phononic crystals. Furthermore, this study employs genetic algorithm optimization to achieve the maximum band gap width at different frequencies, enhancing the practical value of fractal phononic crystals in engineering applications. This research deepens theoretical understanding and provides valuable guidance for optimizing their use in broadband acoustic control and energy harvesting applications.

Using the clustering method to find the final environmental parameters coefficients in road construction projects

Nowadays, more and more attention is being paid to environmental issues due to the development of road transportation, particularly the construction of arterial roads. Despite the existence of diverse methods to determine convenient criteria for their assessment, determining the projects with the least harmful effects on the environment and ranking them for purposes of budget allocation and prioritization are remarkably important. The case is more highlighted in regions where roads go through diverse areas with different climatic and geographical distributions. In the present study, a new method consisting of two phases was proposed to determine the optimal coefficient of environmental parameters in road construction parameters. In the first phase, the Genetic Optimization Algorithm was implemented to determine convenient coefficients for the relevant parameters. During this stage, similar coefficients were clustered together. In the second phase, an environmental index for various projects was determined based on the obtained results, and the proposed projects were ranked based on that. According to the results obtained concerning environmental parameters during the pre-implementation stage, polluting water resources was the most influential parameter, with a coefficient determined at 3.59. Moreover, the most significant parameter during the implementation was noise pollution, with a coefficient of 5.89, while damaging the ecosystem was the most significant one during the stage of land use change (5.25). Furthermore, soil pollution was the most remarkable parameter during the stage of maintenance (5.81), while damaging the local climate pollution was the most important one during the stage of road implementation (5.67). The above findings can be helpful for researchers in road construction projects.

Induction Motor Geometric Parameter Optimization Using a Metaheuristic Optimization Method for High-Efficiency Motor Design

In this study, the optimum design for an induction motor (IM) was realized by providing details of its geometric design. The IM optimization was carried out using the Artificial Ecosystem-based Optimization (AEO) algorithm, a metaheuristic method. The AEO algorithm was used for the first time in IM optimization, and the design parameters were optimized. Ten motor design parameters were used as design variables. IM efficiency was improved, as the objective function. The genetic algorithm (GA) optimization method was used for comparison with the results obtained with the AEO method. The optimized and unoptimized results of the IM design generated with codes created in the Matlab program were verified with the Ansys RMxprt program, and it could be seen that the results are in good agreement. As a result of these studies, it was observed that the use of AEO in determining the geometric parameters of the IM had better convergence accuracy and reached the optimum result in a shorter time compared to the GA optimization method. It was observed that IM efficiency increased from 90.34% to 91.575% on average with the AEO method.

ALPOA: Adaptive Learning Path Optimization Algorithm for Personalized E-Learning Experiences

In this study, we propose the Adaptive Learning Path Optimization Algorithm (ALPOA) to enhance personalized e-learning experiences by tailoring content delivery based on individual learner profiles. ALPOA employs a hybrid optimization framework combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to dynamically adjust learning paths. The algorithm considers multiple factors such as learner proficiency, learning speed, engagement level, and content difficulty. Experimental results demonstrate that ALPOA outperforms traditional static e-learning models, achieving a 25% improvement in learning efficiency, a 30% increase in learner engagement, and a 20% reduction in content redundancy. The model was tested on a dataset of 1,500 learners, showing a 97% accuracy in predicting optimal learning paths and a 15% higher knowledge retention rate compared to benchmark algorithms. ALPOA’s scalability and adaptability make it a promising solution for personalized education systems, fostering improved learning outcomes and satisfaction. Future work will focus on integrating real-time feedback mechanisms and expanding the algorithm to support diverse learning environments.

Comparison of Machine Learning Algorithms and Hybrid Computational Intelligence Algorithms for Rehabilitation Classification and Prognosis in Reverse Total Shoulder Arthroplasty

Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms’ classification accuracy and patients’ rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients’ rehabilitation prognosis.

Finance-based scheduling for projects in owners’ portfolios: MILP versus improved GA and PSO

PurposeIn the construction industry, cash flow issues can impact both contractors and owners. Although finance-based scheduling (FBS) models were developed to control contractors’ cash flow, researchers completely disregarded cash flow management for owners’ portfolios. Therefore, FBS is once again introduced in this study from the perspective of the owners of portfolios.Design/methodology/approachFor the FBS problem of portfolio owners (FBS-PO), a mixed integer linear program (MILP) model is developed. The purpose is to ensure that owners’ cash inflows are greater than cash outflows while minimizing the sum of weighted extensions of the projects in portfolios. Owing to the difficulties encountered in solving the proposed MILP model, genetic algorithm (GA) and particle swarm optimization (PSO) meta-heuristics are used. To ensure the feasibility of the solutions, a special serial schedule generation heuristic was developed in conjunction with the random key method for chromosome representation.FindingsThe GA produced higher-quality solutions compared to PSO, though it required more processing time. The GA has proven to be a far better choice than the MILP exact solver for bigger portfolios. Additionally, there is a linear correlation between the number of activities in portfolios and the amount of computational time required for the GA to converge.Originality/valueThis paper introduces the new research domain of FBS from the owners’ perspective (FBS-PO). In order to establish the FBS-PO as a stand-alone domain, a critical review of the FBS studies in the literature was conducted. In addition, the goals, characteristics, implementation and challenges of the FBS heuristics are benchmarked against those of the special serial schedule generation heuristic developed for the FBS-PO problem.

Prediction of Al2O3 concentration in aluminum electrolytic cells by BP neural network based on improved quantum genetic algorithm optimization

Prediction of Al2O3 concentration in aluminum electrolytic cells by BP neural network based on improved quantum genetic algorithm optimization

Novel decomposed genetic algorithm for equivalent circuit model parameter optimization of lithium-ion battery

Novel decomposed genetic algorithm for equivalent circuit model parameter optimization of lithium-ion battery

Breast cancer prediction using genetic algorithm and sand cat swarm optimization algorithm

Breast cancer prediction using genetic algorithm and sand cat swarm optimization algorithm

Refining COVID-19 Lesion Segmentation in Lung CT Scans Using Swarm Intelligence and Evolutionary Algorithms.

Accurately identifying lung lesions in CT (Computed Tomography) scans remains crucial during the Coronavirus Disease 2019 (COVID-19) pandemic. Swarm intelligence algorithms offer promising tools for this purpose. This study compares four swarm intelligence algorithms Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for segmenting COVID-19 lung lesions. GA, GSA, and BFOA achieved accuracies exceeding 90.5%, while the PSO algorithm further improved segmentation accuracy, reaching 91.45%, with an exceptional F1 score of 95.54%. Overall, the approach achieved up to 99% segmentation accuracy. The findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID-19 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.

Tax Risk Early Warning System for SMEs Using Auto EncoderBackpropagation Neural Network with Genetic Algorithm Optimization

Tax Risk Early Warning System for SMEs Using Auto EncoderBackpropagation Neural Network with Genetic Algorithm Optimization

A human-machine integrated optimization method for long walkway space

ABSTRACT With the development of cities, the design of long walkway spaces have exposed a series of issues. These spaces suffer from layout inefficiencies due to the complex interaction of multiple factors, affecting walking experiences. Optimization schemes for such spaces typically involve the calculation and evaluation of various influencing factors, necessitating comprehensive and systematic judgment. In recent years, the advancement of computer technology has made it possible to utilize genetic algorithms for multi-objective optimization of spatial layouts. Through human-machine collaboration based on genetic algorithms and expert judgment, diverse schemes can be generated more quickly and the optimal solution can be effectively selected. This article aims to explore how to use generative technology combined with expert judgment in a human-machine collaboration approach to address the multi-objective optimization of long walkway spaces. Research results indicate that this process can enhance the efficiency and quality of optimizing layouts for long walkway spaces. After further refinement through manual comparison, the rationality of the results is improved, achieving optimization effects based on the previous layouts. In the future, this method can be promoted to other areas and application scenarios within urban design and planning, demonstrating significant potential for development.

Fault-tolerant reconfiguration estimation and control of vehicle state under sensor fault

Purpose The purpose of this study proposes a strategy based on vehicle kinematics, dynamics and fusion estimation. The estimation signal of vehicle driving state is crucial for vehicle driving safety and stability control, and the issue of fault-tolerant reconstruction estimation of vehicle driving state under the failure of yaw rate or lateral acceleration sensors is a significant research topic. Design/methodology/approach A strategy based on vehicle kinematics, dynamics and fusion estimation is proposed. To address the issue of inaccurate calculation of tire forces because of sensor failure, a method combining adaptive sliding mode observer, genetic algorithm and particle swarm optimization algorithm is proposed to accurately calculate tire forces, and the Square Root Cubature Kalman Filter algorithm is used to reconstruct the estimation of vehicle driving state under sensor failure. To improve the accuracy of fault-tolerant reconstruction estimation of vehicle driving state, an error-weighted multi-method fusion estimation strategy for vehicle driving state is proposed. A fast terminal sliding mode control algorithm is proposed to control the stability of the fault-tolerant reconstruction estimation signal of vehicle driving state. Findings Simulation results show that the proposed fault-tolerant reconstruction estimation algorithm for vehicle driving state can accurately estimate the actual driving state of the vehicle and stably participate in the vehicle stability control system, achieving fault-tolerant reconstruction estimation and control of vehicle driving state under sensor failure. Originality/value The problem of vehicle motion state estimation under yaw velocity sensor fault or lateral acceleration sensor fault is solved, and fault tolerance control under sensor state is realized.

Enhancing Stock Portfolio Selection with Trapezoidal Bipolar Fuzzy VIKOR Technique with Boruta-GA Hybrid Optimization Model: A Multicriteria Decision-Making Approach

The investors’ main objective is to minimize the risks and to get the maximum returns in the random stock market requires them to choose the correct mix of the stocks. The traditional portfolio selection methods often struggle with market volatility, leading to less-than-the targeted profits. This research attempts to apply trapezoidal bipolar fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to help in decision-making. It also combines the fuzzy set theory with the VIKOR method, the trapezoidal bipolar fuzzy with VIKOR (TrBFV) approach offers a comprehensive and flexible system for evaluating investment options. The proposed approach has been validated through real-world illustrations. The analysis has been made with the VIKOR method and its integration with trapezoidal bipolar fuzzy sets. Financial decision-making can be very hard for investors who have access to big data due to the overwhelming amount of information they are required to interpret. The Boruta-GA approach combines the advantages of the Genetic Algorithm (GA) and Boruta Optimization Algorithm (BOA) methods, implementing the comprehensive feature selection capability of Boruta to detect all relevant features and harnessing the strength of GA to bring about improvement within a wide range of datasets. The result shows that this novel approach is effective and can help to investors to take decisions in the unpredictable financial market. This study is an attempt to provides investors with an appropriate approach to navigate the challenges of stock market investment. Abbreviations serve as a nomenclature table, detailing all acronyms.

Predicting and synthesizing terahertz spoof surface plasmon polariton devices with a convolutional neural network model

With the increasing global attention to deep learning and the advancements made in applying convolutional neural networks in electromagnetics, we have recently witnessed the utilization of deep learning-based networks for predicting the spectrum and electromagnetic properties of structures instead of traditional tools like fully numerical-based methods. In this study, a Convolutional Neural Network (CNN is proposed for predicting spoof surface plasmon polaritons, enabling the examination of the absorption spectrum of metallic multilevel-grating structures (MMGS) and designing various sensor devices and absorbers in the shortest time possible. To expedite the training process of this network, a semi-analytical method of rigorous coupled-wave analysis (RCWA) enhanced with the fast Fourier factorization (FFF) technique has been employed, significantly reducing the data generation time for training. This CNN enables the prediction of existing spoof surface plasmon polaritons (SSPPs) and their intensity within a 110% frequency range. In addition to the speed of dataset generation, the simple framework of this network has also facilitated the training of the network in a short time. By comparing the network in this study with previous works, it is apparent that in addition to structural and geometrical changes in the unit cell, the designer is afforded greater freedom in determining the material of the incident medium and, for the first time, specifying the angle of incidence of the source. Finally, for the validation of the suggested network, the predictive power of the absorption spectrum of various structures is compared with traditional methods. Three examples are provided for inversely designing several sensor devices and absorbers in the terahertz band using the proposed CNN and the genetic optimization algorithm.

Path Planning for Robots in Fire Scenarios based on Dijkstra Algorithm and Genetic Algorithm

Robots have emerged as a pivotal element in enhancing firefighting operations, offering a blend of efficiency and safety to human responders. This paper delves into the development of a path planning strategy for robots navigating through fire scenarios. Particularly we fused the Dijkstra Algorithm and Genetic Algorithm (GA). Our methodology commences with a simplified yet comprehensive definition of the fire environment, incorporating factors such as obstacle height, surface roughness, and fire sources. The environment and surroundings are represented by a 2.5-dimensional grid map. On the other hand, the robots traversal and ascent capabilities modeled to reflect varying velocities across different terrains. The Dijkstra Algorithm is subsequently utilized to identify the optimal path from the starting point to the destination, ensuring a balance between minimal traversal time and reduced thermal exposure. Our results, demonstrated through MATLAB simulations, reveal a marked improvement in path planning when GA optimization is applied. The comparative analysis across three scenarios underscores the versatility and effectiveness of our approach, showcasing a significant reduction in both traversal time and thermal exposure. Index TermsRobots, Firefighting, Path planning, Dijkstra, GA

Optimizing vertical transmission control: A hybrid neural network approach with Wolbachia for Zika virus

In this study, we investigate cutting-edge control strategies designed to address the global health threat posed by the Zika virus. Our exploration utilizes an advanced numerical approach, the artificial neural network (ANN), complemented by the hybrid efficiency of global and local search schemes. The research delves into the distinctive properties of Wolbachia, a bacterium, assessing its potential to hinder virus transmission and regulate the population of Ades aegypti mosquitoes, the primary carriers of the Zika virus. The study employs a rigorous mathematical framework, categorizing individuals into distinct compartments such as newborns, adults, Wolbachia-uninfected mosquitoes, and Wolbachia-infected mosquitoes. We pinpoint the disease-free equilibrium (DFE) and compute the basic reproductive number [Formula: see text]. Through extensive simulations, we scrutinize the model’s stability by manipulating critical parameters, including biting rates [Formula: see text], mosquito death rates [Formula: see text], and pregnancy delays [Formula: see text]. Importantly, our model incorporates the dynamics of Zika transmission and its correlation with microcephaly, both with and without the presence of Wolbachia. To numerically handle the dynamic model, we introduce a computational procedure, visually illustrating how the presence of Wolbachia significantly impacts infected classes, leading to a reduction in new cases and an improvement in overall model stability. We propose the utilization of the global optimization genetic algorithm (GA) and local search interior point algorithm (IPA) to solve the model. We devised an error-based objective function for the differential model, which was then optimized using the hybrid computational efficiency of GA-IPA. The validation of our proposed computational method, ANNPs-GA-IPA, involved a comprehensive comparison of the results obtained against reference solutions, affirming the accuracy and reliability of our approach.

Developing picking route policies with genetic algorithms and order batching with deep neural networks in picker to part warehouses

ABSTRACT This paper proposes a hybrid approach to warehouse management, combining Genetic Algorithm (GA) optimization for picking routes with Deep Neural Network (DNN) classification for order batching. The GA-DNN method significantly reduces travel distances while lowering computational time. Experimental results demonstrate up to 69.01% reduction in travel distance in large warehouses, outperforming traditional methods. This study underscores the effectiveness of GA-DNN solutions in addressing modern logistics challenges.

Application of improved multi-population genetic algorithm in structural optimization of automotive electrical equipment

Abstract The structure of automotive electrical equipment is becoming increasingly complex. How to optimize the structure without affecting the performance of the car itself to reduce the weight and manufacturing cost of electrical equipment has become particularly important. Based on this, this study introduces diversity preservation mechanism and adaptive mutation strategy to improve the multi-population genetic algorithm, and proposes the objective function and constraint conditions for the results of automotive electrical equipment, constructing an optimization model for the structure of automotive electrical equipment. The results showed that at 500 iterations, the improved multi-population genetic algorithm had optimal and average fitness values of 0.96 and 0.94, respectively, demonstrating higher accuracy and search capability compared to other algorithms. After using optimization models for different automotive electrical equipment structures, the average energy consumption decreased by 18%, the total cost decreased by 16.55%, and the average total weight of electrical equipment structures decreased by 16.475%. This model is effective in optimizing the structure of electrical equipment. This study contributes to improving the performance and design efficiency of automotive electrical equipment, and has significant implications for promoting the application of intelligent optimization technology in the automotive industry.