Immune Genetic Algorithm Research Articles

Research on Path Planning for Industrial Cleaning Robots in Complex Environments: An Enhanced Immunogenetic Algorithm Approach

As industrial automation advances, cleaning robots in complex environments have demonstrated significant value in enhancing efficiency and reducing labor costs. This paper presents a path planning method based on an improved Immune Genetic Algorithm (IGA) for comprehensive coverage path planning in complex industrial settings. Initially, the Boustrophedon Cellular Decomposition (BCD) method is employed to effectively segment the complex scene, identifying key areas for cleaning tasks. Subsequently, the path planning efficiency and adaptability are enhanced through an optimized genetic algorithm that integrates immune memory, Inver-Over operators, and 3-opt operators to refine the traversal sequence between regions. Moreover, this study incorporates the A* algorithm and the Biologically Inspired Neural Network (BINN) algorithm for efficient inter-regional path transitions and precise intra-regional traversal, respectively. A series of simulation experiments validate the superiority of the proposed algorithm in terms of path length, coverage rate, and computational time. The results demonstrate that this method not only effectively addresses the challenges of path planning in complex industrial environments but also significantly improves cleaning efficiency, offering high practical value and potential for broader application.

Development of Differential Evolution Algorithm for Simple Assembly Line Balancing Problem Type 1

The Simple Assembly Line Balancing Problem Type 1 (SALBP-1) is a widely embraced method in the industry for its simplicity in organizing production processes and enhancing efficiency. Consequently, a Differential Evolution Algorithm (DDE) using a backward task sequence was developed in this study to assist in production process management by determining the optimal number of stations. The efficacy of this method was assessed by juxtaposing it with heuristic approaches, including Longest Operation Time (LOT), Most Following Tasks (MFT), Ranked Positional Weight (RPW), Shortest Operation Time (SOT), Fewest Following Tasks (FFT), Ant Colony Optimization (ACO), Differential Evolution (DE), and Immune Genetic Algorithm (IGA). The findings reveal that DDE outperforms LOT, MFT, RPW, SOT, and FFT in discovering superior solutions and consistently matches solutions achieved by ACO, DE, and IGA methods across all problems. Notably, the DDE method exhibits a shorter time frame for solution discovery.

Study on the Fast Search Planning Problem of Lost Targets for Maritime Emergency Response Based on an Improved Adaptive Immunogenetic Algorithm.

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region. It establishes a "prediction-scheduling" search strategy model, planning a rapid search task for disconnected targets based on overlapping probability through the IAIGA. By incorporating an immune mechanism, the algorithm enhances its global search capability and robustness. Additionally, the adaptive strategy enables dynamic adjustment of the algorithm's parameters to accommodate varying search scenarios. The experimental results demonstrate that the proposed IAIGA significantly outperforms traditional methods, providing higher search speeds and more accurate search results in the context of maritime emergency response. These findings offer effective technical support for maritime emergency operations.

A multistage simulation-optimization-integrated methodology framework for user-oriented electric vehicle carsharing reallocation under dynamic price subsidy

Electric vehicle (EV) carsharing operation is facing major challenge to balance the increasing user demand with insufficient vehicle availability and station capacity. Previous researchers mainly focus on reallocating EVs in limited stations and discrete periods through operator-oriented strategies, while containing scant study of dynamic pricing scheme that can benefit supply-demand balance and service profitability in continuous operation periods. Therefore, this paper proposes a multistage simulation-optimization-integrated methodology framework for adaptively incentivizing users based on dynamic price subsidy (DPS). In stage I, Simulation of Urban Mobility (SUMO) is used to determine the real-time supply-demand distributions from user historical data. In stage II, a novel user-oriented reallocation optimization model is established to maximize the operation profit based on the obtained SUMO results. To obtian feasibile solutions, an improved immune genetic algorithm is developed to effectively solve the model. The real-world case study in Shanghai shows that, compared with other benchmark methods, the proposed methodology can significantly increase the daily profit from 78981 RMB to 89506 RMB and improve the system balance from 78.1 % to 94.5 %. Sensitivity analysis and cost-benefit analysis show that the user-oriented DPS can reduce operation cost and order waiting time, while promoting EV utilization within different user populations.

Retracted: Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm

Retracted: Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm

Research on Base Station Siting Method Based on 0-1 Planning Model and Immune Genetic Algorithm

With the rapid development of 5G technology, the high bandwidth required for communication shows a growing trend. However, the coverage area of the original base station gradually decreases, resulting in the expansion of the weak coverage area, in order to guarantee the quality and reliability of the communication network, there is an urgent need to establish new base stations in the weak coverage area. In this paper, a 0-1 planning model is developed with base stations and weak coverage points as the core elements. In this model, we aim to minimize the cost, and also consider the distance limitation and coverage demand, in order to ensure the premise of service demand, through the immunogenetic algorithm to achieve the optimization of the base station site selection. Ultimately, we have visualized it with the help of MATLAB to fully present the effectiveness and practical application value of this base station siting scheme.

Techno-economic and environmental benefits-oriented human–robot collaborative disassembly line balancing optimization in remanufacturing

The remanufacturing process, driven by human–robot collaboration technology, is becoming an important carrier for the circular economy, contributing to economic development while significantly reducing environmental pressure. However, existing researches on human–robot collaboration disassembly line have certain limitations and fails to address economic and environmental considerations adequately. Therefore, in this study, a techno-economic and environmental benefit-oriented human–robot collaborative disassembly line balancing problem (TEBHRC-DLBP) was formulated that requires the utilization of human and robot resources to improve disassembly efficiency and quality and to facilitate decision-making on recycling options for disassembled parts to maximize economic and environmental benefits. First, a mixed-integer programming (MIP) model was developed for the TEBHRC-DLBP to minimize the number of workstations, minimize the smoothing index, and maximize techno-economic and environmental benefits. Second, a multi-objective immune genetic algorithm (MIGA) was developed to solve the proposed TEBHRC-DLBP efficiently. A five-layer encoding method and a triple decoding strategy were constructed based on the problem characteristics, and immune operators were introduced into the well-known NSGA-II structure to interfere with the global search process with a certain degree of strength to avoid invalid work and to improve algorithm efficiency. In addition, the correctness and validity of the proposed MIP model and the MIGA were verified using the case of a small-scale personal computer. In 21 benchmark tests with scales ranging from 7 to 148, the proposed MIGA achieved significantly better results than the seven algorithms reported in the literature and obtained the best value for 16 benchmarks. Finally, the application of the MIGA to the disassembly case of a power battery module demonstrates its good stability, convergence, and diversity, as well as its excellent practical application ability. In particular, the proposed method can change the disassembly scheme according to different disassembly planning periods to maximize economic and environmental benefits.

Adaptive Data‐Driven Modeling Strategy Based on Feature Selection for an Industrial Natural Gas Sweetening Process

AbstractAs the core process of natural gas purification plant, natural gas sweetening directly affects the production efficiency and product quality of the purification plant. However, process modeling based on sulfur content prediction presents challenges in adaptability and accuracy. To tackle this, a machine learning‐based modeling approach is proposed that integrates an adaptive immune genetic algorithm with random forest (RF) to intelligently select process features as input variables for RF modeling. The industrial result indicates that the proposed method is able to remove interfering variables and adaptively achieve optimal model precision for different scenarios. It offers a novel research instrument for product quality monitoring in natural gas sweetening plants.

Integrating Artificial Immune Genetic Algorithm and Metaheuristic Ant Colony Optimizer with Two-Dose Vaccination and Modeling for Residual Fluid Catalytic Cracking Process

Integrating Artificial Immune Genetic Algorithm and Metaheuristic Ant Colony Optimizer with Two-Dose Vaccination and Modeling for Residual Fluid Catalytic Cracking Process

Efficient energy valley optimization approach for reconfiguring thermoelectric generator system under non-uniform heat distribution

A thermoelectric generator (TEG) suffers from low conversion efficiency, especially when it has been operated under non-uniform temperature distribution. Dynamic reconfiguration is an effective method to enhance the TEG overall efficiency although it requires many electrical switches that make the process more complex. This paper proposes a new approach of energy valley optimizer (EVO) to optimally reconfigure the TEG array operated at non-uniform temperature distribution and enhance the harvested electrical energy. The selection of EVO is due to fast convergence rate, parameter-free, and avoiding stuck in local optima. Minimization of absolute error between the TEG system current at maximum power and the actual one is the proposed fitness function. The analysis is performed on small 9 × 9 TEG array and large 15 × 15 TEG array. Eight temperature distribution patterns of non-uniform row, non-uniform column, short wide, long wide, diagonal, external, internal, and random are analyzed. The proposed EVO is assessed via comparing to reported immune genetic algorithm (IIGA) and other programmed approaches of osprey optimization algorithm (OOA), chef-based optimization algorithm (CBOA), artificial hummingbird algorithm (AHA), and northern goshawk optimizer (NGO). Moreover, statistical investigation is implemented via performing Kruskal-Wallis test, Friedman test, ANOVA test, and Wilcoxon rank test. The fetched results revealed that, the best enhanced power from small 9 × 9 array is 9.143% compared to the original arrangement occurred at long wide temperature distribution. Regarding large 15 × 15 TEG array, the best enhanced power happened during diagonal pattern with 3.359%. The results confirmed the preference of the proposed EVO in solving the TEG system reconfiguration problem with great efficiency.

Simulation analysis of visual perception model based on pulse coupled neural network

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

Research on gas emission quantity prediction model based on EDA-IGA

In order to accurately predict the possible gas emission quantity in coal mines, it is proposed to use the multi-thread calculation of the Immune Genetic Algorithm (IGA) and injection of vaccines to improve the accuracy of prediction and combine the Estimation of Distribution Algorithm (EDA) to the distribution probability of excellent populations. Calculating, and selecting excellent populations for iteration, optimize the population generation process of the Immune Genetic Algorithm, so that the population quality is continuously optimized and improved, and the optimal solution is obtained, thereby establishing a gas emission quantity prediction model based on the Immune Genetic Algorithm and Estimation of Distribution Algorithm. Using the 9136 mining face with gas emission hazards in a coal mine from Shandong Province in China as the prediction object, the absolute gas emission quantity is used to scale the gas emission quantity, and it is found that the model can accurately predict the gas emission quantity, which is consistent with the on-site emission unanimous. In the prediction comparison with IGA, it is found that the accuracy of the prediction results has increased by 9.51%, and the number of iterations to achieve the required goal has been reduced by 67%, indicating that the EDA has a better role in optimizing the population update process such as genetic selection of the IGA. Comparing the prediction results of other models, it is found that the prediction accuracy of the EDA-IGA is 94.93%, which is the highest prediction accuracy, indicating that this prediction model can be used as a new method for the prediction of coal mine gas emission. Accurately predicting the gas emission quantity can provide guidance for safe mining in coal mines. The gas emission quantity can also be used as a safety indicator to reduce the possibility of coal mine accidents, ensure the personal safety of coal miners and reduce economic losses in coal mines.

Material-structure collaborative design for broadband microwave absorption metamaterial with low density and thin thickness

There is a great demand for the metamaterials with low density, thin thickness and broadband microwave absorption in the aeronautic stealth domain. However, it is still a great challenge to achieve satisfactory impedance matching between free space and metamaterials subjected to above constraints. To address this issue, a material-structure collaborative approach was proposed for designing excellent microwave absorption metamaterial with low density and thin thickness, which includes nanomaterial synthesis, unit cell optimal design and absorption mechanism analysis. The 3D Graphene-Carbon nanotubes-Nickel nanomaterial was firstly synthesized by in-situ growth method for fabricating the low-density metamaterial. Inspired by the natural anti-reflection microstructure of Polygonia c-aureum's eye, the unit cell of metamaterial was designed. Size parameters optimization of designed unit cell was conducted with the efficient immune genetic algorithm. The optimized metamaterial is extremely advantageous in effective absorption bandwidth (EAB, reflection loss ≤ −10 dB), low density and thin thickness, which can achieve 29.1 GHz EAB and equivalent density as low as 0.65 g/cm3 (the areal-density is 3.58 kg/m2) with thickness of only 5.5 mm. The proposed material-structure collaborative approach provides an efficient and systematic avenue for designing broadband microwave absorption metamaterials.

Reliable design of urban surface-underground integrated logistics system network with stochastic demand and social-environmental concern

Transforming urban goods movement from roads to underground is an emerging concept to improve supply chain performance and alleviate overcrowded environments. Existing literature merely discussed the underground logistics system (ULS) network design problem within a simplified boundary, whereas several critical system features (e.g., stochastic freight demand and co-modality) were omitted. To bridge the knowledge gaps, this paper proposes a reliable modeling approach to solve an urban surface-underground integrated logistics network (USUIL) design problem with a holistic consideration of cost-benefit objectives, road-ULS modal split, and demand uncertainties. To begin with, we introduce a city-wide hub-and-spoke USUIL network structure with a two-tier parallel distribution process. Five major external benefits of underground goods movement are formulated using real-world data. Next, a deterministic model is developed to optimize the network decisions including hub-satellite location-allocation, deployment of tunnels and pipelines, logistics service assignment, and flow routing. Then, to deal with the influences of case-wise demand uncertainties, the model is reformulated into a reliable one by incorporating the ideas of solution robustness and stochastic equivalents. A two-stage heuristic-based optimization tool that hybridizes the adaptive immune genetic algorithm, plant growth simulation algorithm, and Clarke-Wright saving algorithm was proposed to solve the problem. Finally, numerical experiments are conducted on the Beijing city case to validate the proposed method, and the best network schemes obtained from different models and scenarios are compared. Results show that a greater robust control factor in our model helps the network to lower the integrated freight transport cost while improving the social-environmental benefit at the expense of deploying more facilities. The reliable model performs well in balancing the conservativeness (i.e., less capacity insufficiency risk) and optimality (i.e., less cost due to redundant facilities) of the USUIL network design when faced with diverse demand cases.

A spatial contextual immune genetic algorithm to building cartographic displacement

AbstractIn map generalization, displacement is the most frequently used operator to reduce the proximity conflicts caused by reducing scales or other generalization operations. Building displacement can be formalized as a combinatorial optimization problem, and a heuristic or intelligent search algorithm can be borrowed to obtain the solution. In this way, we can explicitly resolve minimum distance conflicts and control positional accuracy during the displacement. However, maintaining spatial relations and patterns of buildings can be challenging. To address spatial conflicts as well as preserve the significant spatial relations and patterns of buildings, we propose a new spatial contextual displacement algorithm based on an immune genetic algorithm. To preserve important spatial relations and global patterns of map objects and avoid topology errors, displacement safety zones are constructed by overlapping the Voronoi tessellation and buffer areas of the buildings. Additionally, a strategy to shift the buildings in a building group synchronously is used to maintain local building patterns. To demonstrate the effectiveness of our algorithm, two data sets with different building densities were tested. The results indicate that the new algorithm has obvious advantages in preventing topology errors and preserving spatial relations and patterns.

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

Fuzzy Optimal Control of Radon Exhalation in Uranium Tailings under Double-Layer Coverage

The use of covering material is an important measure to control the radon migration of uranium tailings. Radon diffusion and migration are affected by cover layer parameters, such as diffusion coefficient, overburden thickness, particle size, and ore body width. The radon reduction effect of single-layer mulching is often less than that of double-layer, and the material parameters of the cover layer are uncertain; however, they can be explained by a fuzzy dynamic equation. Firstly, the radon exhalation model is constructed with the radon percolation diffusion and migration method in a double-layer covering. Secondly, a fuzzy target of radon exhalation and a fuzzy constraint model are constructed subject to the total cost and thickness of covering material by a triangular membership function. Lastly, the models are aimed at solving the corresponding extreme value interval of the fuzzy target of radon exhalation by immune genetic algorithm, to reconstruct the fuzzy target, fuzzy constraint, and fuzzy aggregation function, where, ultimately, the optimal radon control decision can be obtained by swarm intelligence algorithm subject to different levels between possibility and importance. An example demonstrates a database of optimal decision-making schemes for double-layer coverage, and flexible management of radioactive pollutants is realized.

Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and time-of-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77% and 6.64% for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed.

A Study of the Purely Azimuthal Passive Positioning Problem of UAVs

In order to maintain electromagnetic silence and avoid external interference, UAV clusters usually use purely azimuthal passive positioning methods to adjust the position of UAVs when attempting formation flights. However, due to the large number of UAVs flying in formation, it is difficult to perform effective localization, although they can keep flying at an altitude based on their own sensors. Therefore, this paper establishes a passive positioning model based on the triangulation method under the condition of pure azimuthal passive positioning, solves the coordinates of the UAV position of the received signal by using the least squares problem, and designs the UAV position adjustment scheme by using the optimal scheduling model based on the immune-genetic algorithm, so as to improve the positioning accuracy and survivability of the UAV.