Standard Genetic Algorithm Research Articles

The effect of intersection vehicle scheduling based on the service system of sensors and intelligent traffic road information

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

APPLYING SOFT COMPUTING METHODS IN BUSINESS ANALYTICS USING HYBRID GENETIC ALGORITHMS

In the era of big data and advanced analytics, the application of soft computing techniques has emerged as a powerful tool in solving complex business problems. This paper presents the use of hybrid genetic algorithms (HGAs) in business analytics to address challenges related to optimization, prediction, and decision-making processes. Traditional algorithms often struggle with large, nonlinear, and dynamic datasets typical of business environments. The incorporation of soft computing techniques such as genetic algorithms (GAs) and their hybridization with other methods like fuzzy logic and neural networks can help overcome these limitations. The problem addressed in this research is optimizing decision-making in marketing strategies, focusing on maximizing return on investment (ROI). Standard methods face difficulties in navigating through vast datasets and discovering optimal solutions. The hybrid genetic algorithm proposed in this study combines the exploration strength of GAs with the exploitative precision of local search techniques. The model was tested using a real-world dataset of marketing expenditures and revenues from a retail company. The HGA achieved an ROI improvement of 25%, significantly outperforming standard GAs and traditional optimization methods, which yielded only a 12% improvement. The flexibility and efficiency of this approach make it ideal for various business applications, including supply chain optimization, customer segmentation, and product pricing.

Centralized multi-robot logistic system: An approach using the island model genetic algorithm as task scheduler

The practicality allied with technological and logistical advances led customers increasingly to join e-commerce. This phenomenon was even more expanded during the COVID-19 pandemic. Because of the growing demand for logistic services, warehouses must adapt and seek faster and more efficient processes. A means to achieve that is to use mobile robots in transportation-related tasks. However, their effectiveness is only achieved if the system has an efficient work distributor. This research developed a centralized coordination architecture using the island model genetic algorithm for multi-robot logistic task allocation. The coordination receives, allocates, and monitors tasks performed by robots with different payload capacities and average speeds. The architecture was developed upon the robotic operating system. Thus, any robot with a robotic operating system-based internal system can work under the coordination of this architecture. On the results, the task scheduler, developed in previous work, had the evaluation complemented. The scheduler based on the island model genetic algorithm allocates more tasks than the standard genetic algorithm when using the same heuristic. Regarding coordination architecture, the system successfully managed a group of robots in a simulated environment. It also detected and notified failures during task execution. Therefore, this system provides a complete task scheduling, allocation, and monitoring solution.

Total reflection x‐ray fluorescence analysis of trace elements in highly saline samples

AbstractThe reliable trace analysis of high‐purity chemicals and environmentally relevant samples is more important than ever and has led to the development of novel analytical methods. Total reflection x‐ray fluorescence (TXRF) analysis is an increasingly known method for the determination of heavy metals at low concentration levels in environmental and industrial sample systems. However, there is still a need for optimization to obtain fast and precise results, especially for highly saline samples like brines and salts used in battery production or sea water. In this study, multi‐element standard solutions containing analytes like Co, Cr, Cu, Fe, Mn, Ni, or Zn in a concentration range between 0.1 and 1 mg/L were analyzed in the matrices lithium carbonate (Li2CO3) and artificial seawater (ASW) containing 1000 mg/L Li and 24,000 mg/L NaCl, respectively, leading to matrix‐analyte ratios of up to 240,000:1. Different sample preparation methods were compared in order to achieve the highest possible repeatability (1) and signal‐to‐noise ratios (2) with the least amount of time (3). Various parameters such as sample volume, drying conditions, time, temperature and additive concentration were varied. The relative standard deviation (RSD%) was used as a measure of repeatability for three replicates per sample. For lithium carbonate, a method with a preparation time of only 2 min and a measurement time of 500 s could be developed, which allowed to obtain RSD% well below 5%, a high linearity (R2 > 0.99) and limits of detection (LOD) in the range of 30 μg/L to 60 μg/L for most elements. Seawater analysis could be optimized with respect to signal‐to‐noise ratio, whereby the Kα‐line of the internal standard (Ga) was used for evaluation and the use of a desiccator was found to yield the best results.

Fuzzy PID control based on genetic algorithm optimization inverted pendulum system

Abstract For the first-order inverted pendulum control system, a fuzzy PID control system based on the optimization of the genetic algorithm is proposed. The traditional genetic algorithm has the problem that the difference in the fuzzy subset parameter leads to a decrease in the interpretative ability of the fuzzy system. The main problem of the current genetic algorithm is the complexity of the computation and the low efficiency. Based on this problem, this paper proposes an improved genetic algorithm, i.e., it adopts the variance operator and adaptive change of the variance index and elite retention strategy, which solves the premature and local convergence problems of the standard genetic algorithm, in order to optimize the fuzzy system. The experimental results show that the optimized genetic algorithm gives full play to the advantages of fuzzy control in terms of interpretability and robustness, and at the same time guarantees the prediction accuracy, which provides a new research idea in the field of artificial intelligence control.

The Optimization of Urban Traffic Routes Using an Enhanced Genetic Algorithm: A Case Study of Beijing South Railway Station

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system.

In-silico mechanistic analysis of adsorption of Iodinated Contrast Media agents on graphene surface

The study aims at assessing the potential of graphene-based adsorbents to reduce environmental impacts of Iodinated Contrast Media Agents (ICMs). We analyze an extensive collection of ICMs. A modeling approach resting on molecular docking and Density Functional Theory simulations is employed to examine the adsorption process at the molecular level. The study also relies on a Quantitative Structure-Activity Relationship (QSAR) modeling framework to correlate molecular properties with the adsorption energy (Ead) of ICMs, thus enabling identification of the key mechanisms underpinning adsorption and of the key factors contributing to it. A collection of distinct QSAR-based models is developed upon relying on Multiple Linear Regression and a standard genetic algorithm method. Having at our disposal multiple models enables us to take into account the uncertainty associated with model formulation. Maximum Likelihood and formal model identification/discrimination criteria (such as Bayesian and/or information theoretic criteria) are then employed to complement the traditional QSAR modeling phase. This has the advantage of (a) providing a rigorous ranking of the alternative models included in the selected set and (b) quantifying the relative degree of likelihood of each of these models through a weight or posterior probability. The resulting workflow of analysis enables one to seamlessly embed DFT and QSAR studies within a theoretical framework of analysis that explicitly takes into account model and parameter uncertainty. Our results suggest that graphene-based surfaces constitute a promising adsorbent for ICMs removal, π-π stacking being the primary mechanism behind ICM adsorption. Furthermore, our findings offer valuable insights into the potential of graphene-based adsorbent materials for effectively removing ICMs from water systems. They contribute to ascertain the significance of various factors (such as, e.g., the distribution of atomic van der Waals volumes, overall molecular complexity, the presence and arrangement of Iodine atoms, and the presence of polar functional groups) on the adsorption process.

Product marketing mode of economic zone by genetic algorithm in economic chaos combination prediction

With the development of the social economy, people continue to innovate in marketing. The most important difference between product marketing and traditional marketing is that it transcends the boundaries of time and space. Due to the development of modern network technology, the product marketing model can operate efficiently and easily, increasing people’s purchasing power in time and space. This paper is a research on the economic zone product marketing model based on a genetic algorithm in predicting the economic chaos combination. It introduces in detail the research method of combining the prediction of economic chaotic combination with a standard genetic algorithm. It also specifically analyzes the impact of three marketing models: a complete online marketing model, an online and offline marketing model, and the introduction of a third-party implanted joint marketing model on the impact of product sales in the economic zone. The research results show that, compared with the first two marketing models, after the introduction of the third-party implanted joint marketing method, the sales of product A are better. Among them, the luxury goods of group c achieved the highest sales volume of 952 in November, while the daily necessities achieved the sales volume of 1607 in December.

Using Improved Hybrid Grey Wolf Algorithm Based on Artificial Bee Colony Algorithm Onlooker and Scout Bee Operators for Solving Optimization Problems

Grey Wolf optimization (GWO) is a newly developed stochastic meta-heuristic technique motivated by nature. It shows potential in diverse optimization challenges. It replicates grey wolf hunting behaviour and social hierarchy, exploring the solution space similar to their natural process. The algorithm efficiently explores and converges to the optimal solution. However, a drawback of the standard GWO is its limited exploitation capability due to its exploration-focused iterations. This may hinder finding the optimal solution nearby, leading to lower local convergence rates and degraded solution quality. To address this, the GWO-Employed-Onlooker model suggests incorporating the onlooker and scout bee operators from the artificial bee colony algorithm (ABC) during the position-changing stage of the grey wolves. This enhances exploitation capability, resulting in improved local convergence rates and better solution quality. The proposed method’s performance is evaluated on various optimization functions and compared their convergence rate to standard GWO, Genetic Algorithm (GA), Firefly Algorithm (FA), ABC, and Ant Colony Optimization (ACO) techniques. The results demonstrate that the proposed strategy GWO-Employed-Onlooker is better, indicating that it is valuable in solving optimization problems.

Reentry trajectory optimization of hypersonic glide vehicle based on improved particle swarm algorithm

The reentry process of hypersonic glide vehicle presents a host of intricate constraint issues. A method for optimizing the reentry trajectory of hypersonic glide vehicle is presented to achieve the shortest possible reentry time. The approach utilizes the improved particle swarm optimization (IPSO) algorithm. Firstly, population initialization is performed using the quasi-oppositional differential evolution (QODE) strategy to promote diversity within the population. Then an adaptive adjustment algorithm is designed for the inertia weight coefficient and learning factor to achieve a balance between global and local search capabilities. Finally, utilizing the IPSO algorithm for optimizing the flight parameters of the reentry trajectory, the reentry time is reduced from 338 seconds to 335 seconds. The simulations results indicate that the IPSO algorithm outperforms both the standard PSO algorithm and genetic algorithm (GA) in terms of optimization performance index.

Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing

Optimizing production processes to conserve resources and reduce waste has become crucial in pursuing sustainable manufacturing practices. The solid wood panel industry, marked by substantial raw materials and energy consumption, stands at the forefront of addressing this challenge. This research delves into production scheduling and equipment utilization inefficiencies, offering innovative solutions for the solid wood panel processing line aimed at achieving environmental sustainability and operational efficiency. The study is articulated through two main segments: (1) an exhaustive analysis and the development of a simulation system for the solid wood panel processing line, delineating all production elements and operational logic, furnished with a user-friendly simulation interface, and (2) a comprehensive evaluation and enhancement of various scheduling algorithms specific to the Flexible Job-Shop Scheduling Problem (FJSP) encountered in solid wood panel workshops. A significant leap forward is made with the introduction of the Adaptive Intelligent Optimization Genetic Algorithm (AIOGA), an evolved version of the standard Genetic Algorithm (GA) engineered for optimal scheduling within the solid wood panel processing line. AIOGA incorporates advanced features such as encoding strategy, population initialization, objective function setting, selection strategy, crossover operation, and mutation operation, demonstrating the methodological depth of the study. We applied AIOGA in a designed FJSP, and AIOGA substantially reduced the maximum completion time to 90 min. It evidenced an improvement of 39.60% over the conventional GA, enhancing the equilibrium of the equipment workload across the system. This research presents a multifaceted strategy to address the scheduling complications inherent in solid wood panel production and highlights the extensive applicability of adaptive intelligent optimization in diverse industrial settings. This study establishes a new paradigm in manufacturing optimization, underlining the valuable integration of sustainability and efficiency in production methodologies.

A nondominated sorting genetic model for co-clustering

Co-clustering aims to cluster the rows and columns of data simultaneously and can be often formulated as a two-objective optimization problem (one objective for rows and the other for columns) and the solution is a Pareto-optimal solution set in principle. Existing methods usually convert the co-clustering problem into a single-objective optimization problem by setting a hyper-parameter λ between the two objectives. However, finding a good value of λ is not easy because of its large parameter space; also, there may exist many equally good λs. In this paper, a nondominated sorting genetic model (NSGC) is proposed to tackle the co-clustering problem, totally bypassing the trade-off parameter λ and returning to the original two-objective problem. The core of our model is to group a row objective function and a column objective function and integrate them into a genetic algorithm as the fitness functions. After this reformulation, we follow a standard genetic algorithm procedure to iteratively find the Pareto-optimal solutions. Finally, to fish out a single best solution we further design a sorting criterion according to which the Pareto-optimal solution set can be totally ordered. Extensive experiments with 16 public datasets are conducted, and the results demonstrate the superiority of our approach.

Adaptive fuzzy-genetic algorithm operators for solving mobile robot scheduling problem in job-shop FMS environment

Flexible Manufacturing Systems (FMS) is known as one of the recurring themes that possess these promising characteristics with a synergistic combination of productivity-efficiency transport and flexibility through a number of machine tools alongside other material handling devices. In FMS, mobile robots are commonly deployed in material handling system for the purpose of increasing the efficiency and productivity of the manufacturing process. A reliable, efficient, and optimal scheduling is the most important in manufacturing system. The scheduling problems can become highly complex, especially in large-scale systems with numerous tasks and constraints. Thus, schedule optimization becomes crucial to enhance target performance by determining the best allocations and sequences of resources under specified constraints. Recently, Genetic Algorithm (GA) is a remarkably applicable search algorithm to solve scheduling problems to the way that near optimal could be found. While the performance of GA much depends on the selection of the main parameters, a standard GA may suffer from the issue of premature convergence due to the lack of control on its parameters especially crossover and mutation operators. As there is no specific method or way to tune these parameters, the algorithm is prone to converge on the local optimum, thereby leading to performance degradation. To overcome such flaw, this paper proposed an improved Genetic Algorithm using an adaptive Fuzzy Logic to control crossover and mutation operators (FGAOC) for the solution to the NP-hard problem of scheduling mobile robot within Job-Shop FMS environment. The proposed algorithm has been evaluated in several case studies such as small and large-scale problem, various numbers of mobile robots and the 40-test benchmark problem. The results have demonstrated that the proposed FGAOC has delivered a good performance in exploration-exploitation activities with better solution quality.

Identification of ipsilateral supraclavicular lymph node metastasis in breast cancer based on LASSO regression with a high penalty factor.

Aiming at the problems of small sample size and large feature dimension in the identification of ipsilateral supraclavicular lymph node metastasis status in breast cancer using ultrasound radiomics, an optimized feature combination search algorithm is proposed to construct linear classification models with high interpretability. The genetic algorithm (GA) is used to search for feature combinations within the feature subspace using least absolute shrinkage and selection operator (LASSO) regression. The search is optimized by applying a high penalty to the L1 norm of LASSO to retain excellent features in the crossover operation of the GA. The experimental results show that the linear model constructed using this method outperforms those using the conventional LASSO regression and standard GA. Therefore, this method can be used to build linear models with higher classification performance and more robustness.

Kinetic mathematical model with induction and reversion for the vulcanization of natural rubber and ethylene propylene diene monomer blend

The complexity inherent in modeling and optimizing rubber cure processes stems from the intricate interplay between “induction” and “reversion” phenomena. During the vulcanization process, rubber compounds undergo a gradual initial decrease in torque in an “induction” phase. Following this, the primary vulcanization reaction triggers a swift surge in cross-link density. Interestingly, the density may peak and subsequently decline, leading to compromised mechanical properties with prolonged curing times. This “reversion” phenomenon is extensively documented in sulfur-cured rubbers at elevated vulcanization temperatures, typically exceeding 140 °C. Moreover, the cure conditions can influence the final structure, mechanical performance, and thermal stability of the network.This paper introduces a kinetic mathematical model that is able to predict the optimal time and temperature for a blend of natural rubber and ethylene propylene diene monomer. This model incorporates considerations for both the induction and reversion phases of the vulcanization process. These considerations prove particularly critical, especially in scenarios involving large rubber items or when the rheometer curves of the rubber exhibit a slow pace with significant reversion towards the end. Additionally, the study proposes a novel approach for determining the kinetic variables that concurrently consider all vulcanization temperatures using a standard genetic algorithm.

Particle swarm optimization method applied in total life cycle materials allocation of electricity engineering projects in the green and low-carbon supply chain

Abstract Power grid enterprises face the problem of unbalanced supply and demand of power materials and unequal distribution; intelligent allocation of power materials can optimize the distribution and use of power resources through advanced technology and algorithms to ensure the efficient use of energy and stable supply. In this paper, the whole life cycle cost model of power supply chain is constructed and solved by improved particle swarm optimization (PSO). This algorithm combines the advantages of standard PSO and genetic algorithm (GA), avoids their disadvantages effectively, and realizes the effect of complementary advantages. This paper proposes using the sum of the costs associated with warehousing, transportation, and service penalties in the supply chain of electrical power projects as the objective function, thereby achieving the goal of optimal supply chain costs. In this study, the improved PSO algorithm is used to optimize the objective function, and the optimization results are compared with those of standard PSO algorithm and GA, revealing that its convergence is superior to the other two algorithms. Considering the influence of different factors on the cost, the superiority of the improved PSO algorithm is further verified.

Comparison of Standard Calendar GA4+7 Plant Growth Regulator Programs to Applications Timed to Rainfall Events for Management of Scarf Skin, a Fruit Finish Disorder of Apple

Scarf skin is a fruit finish disorder of apple that leads to reduced grade quality and rejected crop loads. Although not established as a disorder specific to certain cultivars, the manifestation of the disorder is more apparent on red-skinned cultivars such as Gala and Stayman at harvest, where its whiteish cast is held in clear contrast with the redness of the mature fruit. The disorder afflicts fruit during the first 40 days after petal fall as the fruitlet is developing, although symptoms of the disorder may not be clearly visible until many months after. The exact causes of apple scarf skin are not well defined, although it is known that the presence of the disorder can be influenced by environment, fruit size, and some pesticides. Management currently relies on four applications of the plant hormones gibberellins A4 and A7 (GA4+7) during the first 40 days after bloom. Here, we describe our attempts to use a commercialized GA4+7 product (ProVide 10SG) starting at petal fall, with applications made after or before rainfall events, to ease rainfall's putative influence on scarf skin development. We compared our programs to the standard program over 4 years in research orchards and a commercial orchard but observed no consistent reduction in scarf skin severity or incidence of out-of-grade fruit, in part due to the year-to-year variability in the manifestation of scarf skin. Rainfall, at the thresholds defined, does not appear to be a sufficiently significant factor influencing scarf skin development such that a GA4+7 management program can be designed. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Dynamic Tugboat Scheduling for Large Seaports with Multiple Terminals

Effective utilization of tugboats is the key to safe and efficient transport and service in ports. With the growth of maritime traffic, more and more large seaports show a trend toward becoming super-scale, and are divided into multiple specialized terminals. This paper focuses on the problem of large-scale tugboat scheduling. An optimization problem is formulated considering the cross-region constraints and uncertainties during tugboat operation. An improved genetic algorithm is proposed based on the reversal operation (GA-RE) to solve the formulated Tug-SP. A task-triggered strategy is designed for dynamic scheduling and dealing with uncertainties. Taking Zhoushan Port as a representation of multi-terminal seaports, simulation experiments are carried out to demonstrate the effectiveness of the proposed method. Compared with historical scheduling data and the standard GA, the proposed method shows good performance in solving different scale instances (including a large-scale instance of 191 ships) in terms of solution quality and computational time.

A novel method for calculating harmonic contribution based on difference recurrence estimation

AbstractDetermining the harmonic contribution of each harmonic source is conducive to improving the harmonic management of the power system. The main problem with harmonic contribution calculation is determining the equivalent system harmonic impedance. The previous harmonic impedance calculation method can only calculate the impedance value when the harmonic fluctuation is significant, and the calculation fails when the harmonic fluctuation is small. Therefore, a new method called difference recurrence estimation is proposed to construct the objective function from impedance parameters. The advantages of the proposed method are low data requirement, high estimation accuracy, and excellent tracking performance. An improved genetic algorithm (GA) is presented by integrating an adaptive crossover operator and population intervention into the standard GA to improve the global searching impedance parameter ability. Based on these parameters, harmonic impedance and harmonic contribution are calculated accurately. The superiority of the proposed model is verified through simulation and field cases.

A coordinated scheduling approach for task assignment and multi-agent path planning

Path finding is an essential problem in multi-agent systems, widely employed in warehousing and logistics. However, most of the current studies focus on the problem of assigning one agent with one task in a period, which may hinder the efficiency of path planning of the systems when facing multi-tasks. To address this problem, we propose a multi-layer planner, under which a co-scheduling method for multi-agent with batch tasks and path planning in a continuous workspace is proposed. In the task allocation layer of the framework, a standard Genetic Algorithm (GA) is adopted, which optimizes the allocation of task sets, and minimizes the path lengths and the probability of conflicts. Secondly, an Improved Car-Like Conflict-Based Search (ICL-CBS) algorithm is presented as the conflict resolution layer to reduce the runtime. Finally, ICL-CBS and the Spatiotemporal Hybrid-State A* (SHA*) algorithm are jointly used to determine multi-agent paths in the path planning layer. We conduct experiments and compare our method with the baseline algorithm on random obstacles and practical scenarios. The results show that our method effectively improves the efficiency of multi-task completion, and alleviates the computational burden of underlying path planning. Specifically, our method has less runtime.