Traditional Genetic Algorithm Research Articles

Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoothness constraints are proposed in the adaptive function. These constraints reduce the large-angle turns and non-essential turns, improving the smoothness of the generated path. The algorithm integrates the roulette and tournament methods in the selection stage to enhance the searching ability and prevent premature convergence. Additionally, the crossover stage introduces the edit distance and a two-layer crossover operation based on it to avoid ineffective crossover and improve convergence speed. In the mutation stage, we propose a new mutation method and introduce a three-stage mutation operation based on the idea of simulated annealing. This makes the mutation operation more effective and efficient. The three-stage mutation operation ensures that the mutated paths also have high weights, increases the diversity of the population, and avoids local optimality. Additionally, we added a deletion operation to eliminate redundant nodes in the paths and shorten them. The simulation software and experimental platform of ROS (Robot Operating System) demonstrate that the improved algorithm has better path search quality and faster convergence speed. This effectively prevents the algorithm from maturing prematurely and proves its effectiveness in solving the path planning problem of AGV (automated guided vehicle).

Research on Genetic Algorithm Optimization with Fusion Tabu Search Strategy and Its Application in Solving Three-Dimensional Packing Problems

Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensional packing problem is an NP-hard combinatorial optimization problem in the field of modern logistics, with high computational complexity. This paper proposes an improved genetic algorithm by incorporating a fusion tabu search strategy to address this problem. The algorithm employs a three-dimensional loading mathematical model and utilizes a wall-building method under residual space constraints for stacking goods. Furthermore, adaptation of fitness variation strategy, chromosome adjustment, and tabu search algorithm are introduced to balance the algorithm’s global and local search capabilities, as well as to enhance population diversity and convergence speed. Through testing on benchmark cases such as Bischoff and Ratcliff, the improved algorithm demonstrates an average increase of over 3% in packing space utilization compared to traditional genetic algorithms and other heuristic algorithms, validating its feasibility and effectiveness. The proposed improved genetic algorithm provides new insights for solving three-dimensional packing problems and optimizing logistics loading schedules, offering promising prospects for various applications.

A Novel Single Parent Mating Technique in Genetic Algorithm for Discrete – Time System Identification

System identification is concerned with the construction of a mathematical model based on given input and output data to represent the dynamical behaviour of a system. As a step-in system identification, model structure selection is a step where a model perceived as adequate system representation is selected. A typical rule is that the model must have a good balance between parsimony and accuracy in estimating a dynamic system. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the optimality of GA. This paper introduces a novel mating technique in GA based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that demonstrates high exploration capability. In investigating this, four systems of linear and nonlinear classes were simulated to generate discrete-time sets of data i.e. later used for identification. The outcome shows that GA incorporated with the mating technique within 10% - 20% of the population size is able to find optimal models quicker than the traditional GA.

Construction of a Sustainable Training System for Engineering and Technological Innovation Talents Based on CIPP Model in the Digital Era

With the advent of the digital era, the demand for engineering and technological talents in the market is increasing. It is crucial to design a sustainable training and evaluation system suitable for engineering and technological innovation talents. However, traditional data evaluation models have problems with unsuitable evaluation indicators and low model accuracy. Therefore, the paper selects the decision-making-oriented evaluation model to build the basic talent training evaluation indicators, and design expert questionnaires based on the Delphi method to screen indicators. Moreover, the paper also combines the adaptive genetic algorithm optimised backpropagation neural network to establish a talent cultivation evaluation model, and conducts simulation experiments using MATLAB to verify its feasibility. The results showed that the evaluation accuracy [Formula: see text] value of the research model was 0.99635, and the fitness value was 1.34, which was 0.5 higher than the unmodified model and achieved good evaluation results. At the same time, by comparing with the traditional genetic algorithm optimised model and the unimproved backpropagation model, the average evaluation accuracy of the research model increased by 66.44% and 13.59%, and the recall rate increased by 10.79% and 23.96%. The research model has improved the accuracy of evaluation, and its adaptability has also been enhanced, achieving superior evaluation results, which has important value in the cultivation of engineering and technological innovation talents.

Multi-objective optimization approach for coverage path planning of mobile robot

AbstractCoverage path planning (CPP) is a subfield of path planning problems in which free areas of a given domain must be visited by a robot at least once while avoiding obstacles. In some situations, the path may be optimized for one or more criteria such as total distance traveled, number of turns, and total area covered by the robot. Accordingly, the CPP problem has been formulated as a multi-objective optimization (MOO) problem, which turns out to be a challenging discrete optimization problem, hence conventional MOO algorithms like Non-dominated Sorting Genetic Algorithm-2 (NSGA-II) do not work as it is. This study implements a modified NSGA-II to solve the MOO problem of CPP for a mobile robot. In this paper, the proposed method adopted two objective functions: (1) the total distance traveled by the robot and (2) the number of turns taken by the robot. The two objective functions are used to calculate energy consumption. The proposed method is compared to the hybrid genetic algorithm (HGA) and the traditional genetic algorithm (TGA) in a rectilinear environment containing obstacles of various complex shapes. In addition, the results of the proposed algorithm are compared to those generated by HGA, TGA, oriented rectilinear decomposition, and spatial cell diffusion and family of spanning tree coverage in existing research papers. The results of all comparisons indicate that the proposed algorithm outperformed the existing algorithms by reducing energy consumption by 5 to 60%. This paper provides the facility to operate the robot in different modes.

The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm

The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships’ single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper’s algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution’s efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines.

Monitoring and Early Warning Method of Debris Flow Expansion Behavior Based on Improved Genetic Algorithm and Bayesian Network

Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis for emergency rescue and post-disaster recovery. The function of the debris flow disaster monitoring network of the spreading behavior disaster chain is constructed. According to the causal reasoning of debris flow disaster monitoring information, the influence factors of debris flow, such as rainfall intensity and duration, are selected as the inputs of the Bayesian network, and the probability of a debris flow disaster is obtained. The probability is compared with the historical data threshold to complete the monitoring and early warning of debris flow spreading behavior. Innovatively, by introducing niche technology to improve traditional genetic algorithms by learning Bayesian networks, the optimization efficiency and convergence speed of genetic algorithms are improved, and the robustness of debris flow monitoring and warning is enhanced. The experimental results show that this method divides debris flow disasters into the following five categories based on their danger: low-risk area, medium-risk area, high-risk area, higher-risk area, and Very high-risk area. It accurately monitors the expansion of debris flows and completes early warning. The disaster management department can develop emergency rescue and post-disaster recovery strategies based on early warning results, thus providing a scientific basis for debris flow disasters. The improved genetic algorithm has a higher learning efficiency, a higher accuracy, a faster convergence speed, and higher advantages in learning time and accuracy of the Bayesian network structure.

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively.

Ship Global Traveling Path Optimization via a Novel Non-Dominated Sorting Genetic Algorithm

Due to the intensification of economic globalization and the impact of global warming, the development of methods to reduce shipping costs and reduce carbon emissions has become crucial. In this study, a multi-objective optimization algorithm was designed to plan the optimal ship route for safe cross-ocean navigation under complex sea conditions. Based on the traditional non-dominated sorting genetic algorithm, considering ship stability and complex marine environment interference, a non-dominated sorting genetic algorithm model considering energy consumption was designed with the energy consumption and navigation time of the ship as the optimization objectives. The experimental results show that although the proposed method is 101.23 nautical miles more than the large ring route, and the voyage is increased by 10.1 h, the fuel consumption is reduced by 92.24 tons, saving 6.94%. Compared with the traditional genetic algorithm, the voyage distance and time are reduced by 216.93 nautical miles and 7.5 h, and the fuel consumption is reduced by 58.82 tons, which is almost 4.54%. Through experimental verification, the proposed model can obtain punctual routes, avoid areas with bad sea conditions, reduce fuel consumption, and is of great significance for improving the safety and economy of ship routes.

An efficient parameter-retrieval-based surrogate-assisted optimization of on-platform honeycomb absorbing structures

An electromagnetic parameter-retrieval-based surrogate-assisted optimization (PSAO) algorithm is presented to reduce radar cross section (RCS) by optimizing the on-platform honeycomb absorbing structures. To facilitate the optimization process, the honeycomb structure is transformed to an anisotropic homogeneous slab, and the effective parameters of the slab are extracted by the retrieval algorithm. A multi-fidelity model is employed to reduce the computing-time consumption, in which a Gaussian process (GP) regression model is used as the substitute for the coarse model. The GP model establishes a relationship between the geometry of the honeycomb structure and the RCS response of the target coated with the equivalent slab. Finally, the optimization result of the fine model is achieved through a space mapping strategy. The accuracy of the parameter extraction algorithm is verified by analyzing the honeycomb absorbing structure. Subsequently, the proposed optimization method is applied to a metal plate and a metal cylinder, resulting in a 10 dB reduction of RCS in broadband and wide-angle scenarios. This demonstrates the applicability of the proposed PSAO algorithm to both planar and conformal on-platform honeycomb absorbing structures. Furthermore, an NACA0015 foil is analyzed, showing an average RCS reduction of 10 dB and a minimum RCS reduction of 5 dB in the X-band. These results indicate that the PSAO approach can effectively apply to complicated targets. Additionally, the proposed method exhibits significant advantages in terms of computational accuracy and efficiency compared to the traditional genetic algorithm.

Mine water cooperative optimal scheduling based on improved genetic algorithm

This article addresses the issues of unreasonable water scheduling and high costs in coal mine shafts, proposing a hierarchical optimization scheduling strategy. Taking the water quality and quantity of a certain mining area in Inner Mongolia as the research object, it designs the objective function with the highest reuse efficiency and the lowest reuse cost of mine water resources, and establishes the constraint conditions of water quality and quantity for each water-using unit. In response to the problem that traditional genetic algorithms are prone to local optima, an adaptive autobiographical operator is proposed and improved based on Metropolis principle of simulated annealing algorithm. The improved algorithm is applied to the calculation of the scheduling model, and the results show that the recovery cost in the heating season is reduced by 66779.36 CNY/month, a decrease of 10.34%; the recovery cost in the non-heating season is reduced by 61469.28 CNY/month, a decrease of 9.91%. At the same time, the heating season and the non-heating season have reduced by 136.99 h/month and 154.52 h/month respectively, significantly reducing the recovery cost and time.

Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems

Genetic Algorithms (GAs) are known for their efficiency in solving combinatorial optimization problems, thanks to their ability to explore diverse solution spaces, handle various representations, exploit parallelism, preserve good solutions, adapt to changing dynamics, handle combinatorial diversity, and provide heuristic search. However, limitations such as premature convergence, lack of problem-specific knowledge, and randomness of crossover and mutation operators make GAs generally inefficient in finding an optimal solution. To address these limitations, this paper proposes a new metaheuristic algorithm called the Genetic Engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts. GEA redesigns the traditional GA while incorporating new search methods to isolate, purify, insert, and express new genes based on existing ones, leading to the emergence of desired traits and the production of specific chromosomes based on the selected genes. Comparative evaluations against stateof-the-art algorithms on benchmark instances demonstrate the superior performance of GEA, showcasing its potential as an innovative and efficient solution for combinatorial optimization problems.

A rapid evaluation method of blasting effect based on optimized image segmentation algorithm and application in engineering

To quickly determine the blasting block degree and conduct an accurate and objective analysis of the tunnel blasting effect, this study has enhanced and improved upon the traditional genetic algorithm and Otsu algorithm. It has combined it with the marking watershed method and utilized ground digital acquisition to capture images of blasting debris. These images are then used in our custom-developed blasting analysis software to calculate the blasting block degree distribution and provide a quantitative analysis of blasting block degree. The research results show that the optimized image segmentation algorithm effectively improves the traditional threshold segmentation method on the poor effect of segmentation of the edge of the adherent block or the direct application of the watershed segmentation of the over-segmentation problem, to improve the segmentation accuracy based on the new segmentation technology is close to the traditional technology in terms of time. Through the self-developed software, the construction personnel in the project site to quickly obtain the blasting block degree histogram, block degree cumulative curve and other important indicators of the evaluation of the effect of blasting block degree, to provide data support for on-site construction, to assist in the modification of the blasting program, and to improve the efficiency of construction. This study realizes the rapid detection and block identification of blasting blocks, provides data support for the optimization of blasting parameters, and has good application and promotion value.

An Assembly Sequence Planning Method Based on Multiple Optimal Solutions Genetic Algorithm

Assembly sequence planning (ASP) is an indispensable and important step in the intelligent assembly process, and aims to solve the optimal assembly sequence with the shortest assembly time as its optimization goal. This paper focuses on modular cabin construction for large cruise ships, tackling the complexities and challenges of part assembly during the process, based on real engineering problems. It introduces the multiple optimal solutions genetic algorithm (MOSGA). The MOSGA analyzes product constraints and establishes a mathematical model. Firstly, the traditional genetic algorithm (GA) is improved in the case of falling into the local optimum when facing complex problems, so that it can jump out of the local optimum under the condition of satisfying the processing constraints and achieve the global search effect. Secondly, the problem whereby the traditional search algorithm converges to the unique optimal solution is solved, and multiple unique optimal solutions that are more suitable for the actual assembly problem are solved. Thirdly, for a variety of restrictions and emergencies that may occur during the assembly process, the assembly sequence flexible planning (ASFP) method is introduced so that each assembly can be flexibly adjusted. Finally, an example is used to verify the feasibility and effectiveness of the method. This method improves the assembly efficiency and the diversity of assembly sequence selection, and can flexibly adjust the assembly sequence, which has important guiding significance for the ASP problem.

Interface Reliability Modeling of Coaxial Through Silicon Via Based on WOA-BP Neural Network

Abstract Due to the complex structure and thermal mismatch of coaxial through silicon via (TSV), cracks easily occur under thermal load, leading to interface delamination or spalling failure. The reliability issue of coaxial TSV is important for its application in three-dimensional packaging, so it is of great significance to predict the crack trend and evaluate the reliability of coaxial TSV. In this paper, an algorithm model with the combination of whale optimization algorithm (WOA) and back propagation (BP) neural network for the reliability prediction of coaxial TSV is proposed. Based on finite element method (FEM), the training and validation datasets of the energy release rates (ERR) of the crack at the critical interface are calculated to construct the deep learning neural network. Six key structure parameters affecting the reliability of coaxial TSV are selected as the input values of the BP neural network. The maximum relative error of whale optimization algorithm optimized back propagation (WOA-BP) neural network model is 0.88%, which is better than the prediction results of the traditional BP and genetic algorithm (GA) optimized BP models. The WOA-BP neural network model was also compared with BP and GA-BP neural network models with four error metric models. It is verified that WOA-BP neural network model has the best prediction performance. The proposed model can be used to achieve improved prediction accuracy for the interface reliability of coaxial TSV under complex structural conditions since it has higher accuracy and stronger robustness.

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

This study aims to optimize the speed control effect of fuzzy PID controller in brushless DC motor control, and improve the traditional genetic optimization algorithm to improve fuzzy PID algorithm, mainly for genetic algorithm coding, crossover, and mutation process. In the three processes of the genetic algorithm, real number field coding is used, Taguchi orthogonal table is introduced into the cross-process, and Gaussian variation is applied to the mutation process. In the part of model construction and verification, this study uses Simulink software to build a brushless DC motor model and sets the same conventional parameters for simulation. The simulation results show that the algorithm overshoot and stability time of the fuzzy PID controller optimized by the improved genetic algorithm are obviously improved.

Cooperative Exploration Model of Coal–Gallium Deposit: A Case Study of the Heidaigou Coal–Gallium Deposit in the Jungar Coalfield, Inner Mongolia, China

Gallium (Ga) is a typical scattered trace element that is irreplaceable in strategic sectors such as national defense, wireless communications, new materials, renewable energy, and healthcare. The coal–Ga deposit is an important complement to traditional Ga resources and has become a significant focus for Ga mineral resource exploration. Therefore, there is an urgent need to research the coal–Ga cooperative exploration model from both technical and economic perspectives. Taking the Heidaigou coal–Ga deposit as an example, the enrichment zone of coal–Ga is predominantly situated in the northern part of the exploration area, adjacent to the fault zone. The Ga concentration demonstrates a gradual decline from the north–central region towards the northeast and southeast. Similar vertical Ga distribution patterns are observed in adjacent drillings, with notably higher concentrations in the roof, floor, and parting layers. The cooperative exploration model for coal–Ga deposits is proposed based on the above features. The model employs a comprehensive set of cooperative technical methods, such as remote sensing, geological mapping, seismic exploration, drilling, petrogeochemistry, and well logging. The layout of exploration engineering and the concentration of Ga provide the basis for the estimation of Ga resources. Additionally, the model provides an important scientific basis for the improvement of the strategic coordination ability of Ga mineral resources.

A math-heuristic approach for scheduling the production and delivery of a mobile additive manufacturing hub

This paper investigates an innovative printing-while-delivering paradigm within a mobile additive manufacturing (MAM) hub. Different from the conventional production-first-delivery-second (PFDS) mode, the MAM hub, comprising a 3D printer and a truck, can seamlessly integrate the production and delivery processes. Throughout the delivery process of the MAM hub, the embedded 3D printer within the truck concurrently prints products for customers in batches. This capability significantly diminishes customer dissatisfaction caused by time delays between order placement and product receipt, which requires coordinating the delivery routing of truck and the production scheduling of 3D printer. To tackle this problem, a mixed integer programming model is firstly established to minimize the required time of the MAM hub returning to depot after completing all deliveries. Then, a math-heuristic approach combining the genetic algorithm and branch-and-bound is devised to solve the mathematical model efficiently. The delivery sequence of truck is represented as a chromosome in the genetic algorithm; while, for each delivery sequence, the optimal production scheduling of 3D printer is obtained by branch-and-bound based on the theoretical analysis about the relationship between production and delivery sequences. In the numerical experiments, the proposed math-heuristic algorithm produces results identical to CPLEX solver outcomes in small-scale cases. For larger instances, it outperforms traditional genetic algorithms by 3.39% to 19.54%. The proposed batch printing mode consistently surpasses the non-batch mode by 10% to 25%. Moreover, compared to PFDS, the printing-while-delivering paradigm reduces trip time by over 40%. Finally, we analyze the effects of customer quantity, distribution radius, and printing time on the objective, providing management insights about optimal production and delivery strategy. The research finding not only aids in scheduling the MAM hub but also holds the potential to address other applications characterized by simultaneous production and delivery requirements.

Research on Inbound Jobs’ Scheduling in Four-Way-Shuttle-Based Storage System

The four-way-shuttle-based storage and retrieval system is a recent innovative intelligent vertical warehousing system that has been widely applied in manufacturing and e-commerce environments due to its high flexibility and density. As a complex multi-device cooperative operational system, this system features the parallel operation of multiple elevators and four-way shuttles. During large-scale-batch inbound operations, the quality of scheduling solutions for inbound-operation equipment significantly impacts the system’s efficiency and performance. In this paper, a detailed analysis of the inbound-operation process in the system is conducted, taking into consideration the motion characteristics of both the elevators and four-way shuttles. Furthermore, we establish operational time constraints that account for equipment acceleration and deceleration characteristics and introduce a flexible flow-shop-scheduling model to address the scheduling problem in the system. Additionally, we propose an improved genetic algorithm based on double-layer encoding to solve this problem. Comparative experiments with a traditional genetic algorithm and ant-colony algorithm demonstrate the superior efficiency and accuracy of our approach. Finally, the effectiveness of the proposed algorithm is validated through comparisons with large-scale practical experiments.

Hybrid optimization algorithm for estimating soil parameters of spoil hopper deposition model for trailing suction hopper dredgers

Trailing suction hopper dredger is a kind of hydraulic dredger, it has the characteristics of self-propelled, selfloading, self-dredging, self-unloading, it is the main force in dredging and blowing works, it is widely used in the world, it can be said that where there is a big dredging project where there is a trailing suction hopper dredger’s figure. The loading optimization process of trailing suction hopper dredger contains a lot of dredging parameters related to soil type, and the soil type under different working conditions is not very clear. In this study, we present a hybrid optimization technique based on simulated annealing and multi-population genetic algorithm to enhance the loading efficiency of a trailing suction hopper dredger and to examine the variation of dredged soil parameters. The soil parameters of the spoil hopper deposition model were estimated using this hybrid optimization algorithm. The experimental results show that the soil parameters are successfully estimated and verified by our measured construction data of a trailing suction hopper dredger. In addition, our proposed method has the highest accuracy of soil parameter estimation, the fastest algorithm convergence, and excellent robustness compared to the other three intelligent optimization methods. In addition, our method successfully avoids the phenomenon of premature convergence that usually occurs in traditional genetic algorithms, and the parameters show strong adaptability to different vessels under the same dredging area.