Classical Genetic Algorithm Research Articles

Analyzing vehicle path optimization using an improved genetic algorithm in the presence of stochastic perturbation matter

By analyzing the influence of stochastic perturbation matters on vehicle path optimization, a perturbation scheduling model for logistics and distribution with a carbon tax mechanism is established under the premise of time window variation and load capacity constraints. Herein, we propose an enhanced Genetic Algorithm (GA) based on a Gaussian matrix mutation (GMM) operator, which maintains the diversity of the population while speeding up the algorithm’s convergence. The model builds a Gaussian probability matrix using the site positional order distribution characteristics implied in the original site data information, and applies the Gaussian probability matrix to individual gene mutations using a roulette-wheel-selection method; thus, the study guarantees the genetic diversity of the population while guiding it to evolve in the high-fitness direction. Finally, an experimental simulation is performed using data obtained from a commercial supermarket, thereby verifying the effectiveness of the proposed algorithm and comparing it with other algorithms. The results reveal that compared with the classical GA, the average convergence speed of the improved GA can be increased by 50–60% and the consumed algorithm time can be reduced by 48% while maintaining the difference in solution accuracy within 1%.

Batch-sizing and machinability data systems for milling operations: An optimal sustainable cost of quality approach

Nowadays, manufacturers make every effort to achieve a higher quality of their products at an attractive cost. With all the introduced legislation and incentives in the developed world to address global warming, machining shops in the West also strive to cut greenhouse emissions. This article offers an optimal approach to the micro Computer-Aided Process Planning (CAPP) problem to optimize the internal quality cost and buffering effect while keeping the environmental impact low. To optimize the machining parameters, the mathematical model is developed for different milling operations, face, side, and peripheral. cutting speed, feed rate, axial depth of cut, radial depth of cut, nose radius, and batch sizing while maximizing profit and meeting customer demand. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated and solved using Classical Constrained Nonlinear Optimization (CCNO) and Genetic Algorithms (GAs). Surface roughness, used as a metric to evaluate the desired quality level of a finished machined part type, is modeled as a Gaussian random variable to model the surface roughness of the machined part utilizing a cumulative normal distribution. The ratio of rework and scrap is calculated in terms of the surface roughness of the machined part shifting away from the target and exceeding upper and lower specification limits. The internal failure cost model, addressing both scrap and rework, is developed based on Taguchi’s quadratic loss function. CCNO is employed to validate the results obtained by GAs, relaxing the lot-sizing integrality constraint and, thus, the convexity of the produced relaxed model. An iterative method employing a developed multi-regression model is used to solve for the expended power consumption (an inherent highly nonlinear environmental criterion of the developed model) within both GAs and CCNO. This study reveals that the machining parameters substantially impact the cost components of the objective function as well as the scrap and rework quantities. A stringent quality cost target can force the model to optimize the feed rate and nose radius to minimize the internal failure quality cost while improving the environmental impact, including direct and indirect power consumption and CO2 emissions considerations.

Hybrid quantum search with genetic algorithm optimization

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover’s algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome (i.e., the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), where n is the number of qubits in the individuals’ quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals’ register and fixing the remaining ones as classical values of ‘0’ and ‘1’ with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) solves search problems in O(2(n−k)/2) oracle queries, where k is the number of fixed classical bits in the individuals’ register.

Finite Element Analysis and Optimization of the Rotational Stiffness of Semi-Rigid Base Connection under Simultaneous Moment and Tension

The base connection is flexible, not fully pinned/fixed, implying a nonlinear moment–rotation relationship. This deviates from a linear response, where rotation is not directly proportional to the applied moment. Numerical investigations using the commercial software ABAQUS were conducted to analyze the steel base plate connections. The finite element (FE) models were verified against previous experimental results. Moreover, numerical findings of a comprehensive parametric investigation were conducted. The studied connections were examined with different configurations, including variations in the diameter, spacing, and number of the anchor bolts; the thickness of the base plate; and the applied axial force. The current study aims to use numerical results combined with the whale optimization algorithm (WOA) and classical genetic algorithm (GA) to derive a formulation for the moment–rotation (M-θr) relationship. The distinctive aspect of this formulation is that it aims to simulate the nonlinear rotational behavior exhibited by flexible base connections under combined moment and tension loads, while also considering various parameters such as bolt number/diameter and plate thickness. The findings indicate that the WOA is capable of obtaining an optimal equation for accurately simulating the M-Ɵr relationship. This underscores the ability of the WOA to effectively address the complexity of the problem and provide a reliable equation for predicting the rotational behavior of such connections. Consequently, the WOA method can be utilized to calculate the rotational stiffness at H/150, offering valuable support for engineering design processes.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.

Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer

This research addresses the parametric estimation problem in three-phase induction motors by applying a recently developed metaheuristic method known as the generalized normal distribution optimizer (GNDO). A nonlinear programming model based on the steady-state circuit of the induction motor, which uses its Thevenin equivalent, is employed to model the estimation problem. Estimation is carried out by minimizing the mean square error between torque data (obtained from measurements or provided by the manufacturer) and the values calculated with the model. The main advantage of using the GNDO is its effective balance between the exploration and exploitation of the solution space via Gaussian distributions. Numerical tests in two three-phase induction machines confirm the effectiveness of this approach in comparison with the classical genetic algorithm, the particle swarm optimizer, and the sine cosine algorithm. The GNDO approach reports objective function values of about 9.7834×10−14 and 2.6500×10−14, while the sine cosine algorithm reaches solutions of about 4.6327×10−10 and 1.2400×10−6 in both tested motors. All numerical simulations were performed in the MATLAB software, version 2022b.

Hybrid metaheuristics for selective inference task offloading under time and energy constraints for real-time IoT sensing systems

The recent widespread of AI-powered real-time applications necessitates the use of edge computing for inference task offloading. Power constrained edge devices are required to balance between processing inference tasks locally or offload to edge servers. This decision is determined according to the time constraint demanded by the real-time nature of applications, and the energy constraint dictated by the device’s power budget. This problem is further exacerbated in the case of systems leveraging multiple local inference models varying in size and accuracy. In this work, we tackle the problem of assigning inference models to inference tasks either using local inference models or by offloading to edge servers under time and energy constraints while maximizing the overall accuracy of the system. This problem is shown to be strongly NP-hard and therefore, we propose a hybrid genetic algorithm (HGSTO) to solve this problem. We leverage the speed of simulated annealing (SA) with the accuracy of genetic algorithms (GA) to develop a hybrid, fast and accurate algorithm compared with classic GA, SA and Particle Swarm Optimization (PSO). Experiment results show that HGSTO achieved on-par or higher accuracy than GA while resulting in significantly lower scheduling times compared to other schemes.

Route optimization for hazardous materials transportation vehicles based on BFA-ACO under carbon tax policy

Excessive emission of greenhouse gases leads to the increasing greenhouse effect, adversely affecting the global climate. Carbon dioxide is the dominant part of greenhouse gases; reducing its emission is the most important way to solve the climate problem. Aiming at the characteristics of the dangerous goods transportation market and the development of the carbon tax policy, the carbon tax cost and the cost of dangerous goods transportation are introduced, and the characteristics of the dangerous goods transportation and the road traffic network and the road traffic in each period are fully analyzed. The optimization model of the path of the hazardous goods vehicles is established with the optimization objective of the lowest total cost. Then, by analyzing the advantages and disadvantages of bacterial foraging algorithm (BFA) and ant colony algorithm (ACO), the hybrid BFA-ACO algorithm is established by combining the two, and the replication and convergence operations of bacterial foraging algorithm are introduced into the ACO algorithm to improve the convergence speed and global convergence ability of the algorithm. The hybrid algorithm is then used to optimize and solve the path optimization model of hazardous materials vehicles and compared with the classical algorithms Genetic Algorithm (GA) and ACO for solving the path of dangerous materials vehicles. A comparison of the optimization results reveals that optimizing the model by bacterial foraging-ACO algorithm is better than optimizing the model by a single algorithm.

Modified genetic algorithms for generating S-boxes with high nonlinearity

This article discusses the use of modified genetic algorithms to generate S-boxes with high nonlinearity, which is critical for ensuring the security of cryptographic algorithms. S-boxes play a key role in providing resistance to cryptanalysis, in particular to linear and differential cryptanalysis attacks. In the course of the study, a series of experiments were conducted using a genetic algorithm modified by additional storage of the current population and the use of selection. This approach has significantly improved the efficiency of the algorithm compared to the classical genetic algorithm. The best results were achieved with the minimum number of instances in the population and the optimal number of mutations. It was found that the algorithm finds S-blocks with a probability of 99% with a nonlinearity of 104, which demonstrates its high efficiency. The results of the study showed that the modified algorithm is able to provide stable generation of S-boxes with the required attack resistance properties. The proposed method also proved to be flexible in parameter settings, which allows it to be adapted for various cryptographic applications. The paper also discusses the possibilities of further improving the algorithm, including the study of other mutation and selection methods, as well as parameter optimization to achieve even better results. Additionally, the possibility of using distributed computing to increase the speed of S-boxes generation is considered. The results of the study indicate the prospects of the proposed approach for creating attack-resistant cryptographic systems. This study considered the use of genetic algorithms to generate S-boxes with high nonlinearity. The proposed approach was based on the use of an objective function with optimal parameters, which allowed achieving high efficiency in generating bioactive S-blocks. The proposed algorithm showed high stability and efficiency in generating S-boxes with nonlinearity, which is confirmed by a 99% probability of achieving the goal. These results indicate the prospects of using genetic algorithms in cryptographic applications that require high resistance to attacks. In future research, it is planned to further improve the algorithm by exploring other methods of mutation and selection, as well as optimizing parameters to achieve even better results. In addition, it is possible to use distributed computing to further speed up the process of generating S-boxes.

Two-Phase Fuzzy Real-Time Approach for Fuzzy Demand Electric Vehicle Routing Problem with Soft Time Windows

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers’ demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers’ demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs.

Source code obfuscation with genetic algorithms using LLVM code optimizations

Abstract With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering on the program even with the help of computing resources. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. Three program complexity metrics are used to define three different single-objective combinatorial optimization versions of the problem, which are solved and analysed. Additionally, three multi-objective problems are defined, those considering each of the selected metrics together with the program execution time, in order to avoid strong obfuscations penalizing the performance. The goal of the defined problems is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo-assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Classical genetic algorithms (GAs) are used to solve the studied problems, namely a basic cellular GA for the single-objective problems and the popular NSGA-II for the multi-objective ones. The promising results show the potential of the proposed technique.

Handling the balance of operators in evolutionary algorithms through a weighted Hill Climbing approach

Evolutionary Algorithms (EAs) are a well-known domain within Artificial Intelligence. EAs have demonstrated their ability to tackle intricate optimization problems using evolutionary theory principles. However, balancing the dual exploration and exploitation processes remains a crucial concern. This paper introduces the Balanced Hill Climbing Weight Algorithm with Diversity (BHWEAD), an innovative approach that combines elements from classic Genetic Algorithm and Differential Evolution. BHWEAD uniquely employs the Hill Climbing local search to guide the influence of its operators, ensuring an optimal interplay between exploration and exploitation. Additionally, it incorporates a diversity control mechanism, resetting specific solutions to prevent premature convergence to suboptimal solutions. The main contribution of the BHWEAD is the mechanism that permits the balance of the exploration and exploitation stages; also, the incorporation of Hill Climbing permits a proper balance of the influence of the operators. Notice that the proposal can escape from suboptimal solutions using a diversity-based strategy. Tested against the CEC2017 benchmark functions in both 50 and 100 dimensions, BHWEAD outperformed 12 notable EAs, underscoring its potential for high-dimensional optimization problems. Besides, the proposed BHWEAD has also been tested over seven engineering problems, and the comparisons include some memetic algorithms., The paper provides additional insights into the algorithm’s design, conducts a comparative analysis, and identifies potential areas for improvement.

An Efficiency Boost for Genetic Algorithms: Initializing the GA with the Iterative Approximate Method for Optimizing the Traveling Salesman Problem—Experimental Insights

The genetic algorithm (GA) is a well-known metaheuristic approach for dealing with complex problems with a wide search space. In genetic algorithms (GAs), the quality of individuals in the initial population is important in determining the final optimal solution. The classic GA using the random population seeding technique is effective and straightforward, but the generated population may contain individuals with low fitness, delaying convergence to the best solution. On the other side, heuristic population seeding strategies provide the advantages of producing individuals with high fitness and encouraging rapid convergence to the optimal solution. Using background information on the problem being solved, researchers have developed several population seeding approaches. In this paper, to enhance the genetic algorithm efficiency, we propose a new method for the initial population seeding based on a greedy approach. The proposed method starts by adding four extreme cities to the route, creating a loop, and then adding each city to the route through a greedy strategy that calculates the cost of adding every city to different locations along the route. This method identifies the best position to place the city as well as the best city to add. Employing local constant permutations improves the resultant route even more. Together with the suggested approach, we examine the GA’s effectiveness while employing conventional population seeding methods like nearest-neighbor, regression-based, and random seeding. Utilizing some of the well-known Traveling Salesman Problem (TSP) examples from the TSPLIB, the standard library for TSPs, tests were conducted. In terms of the error rate, average convergence, and time, the experimental results demonstrate that the GA that employs the suggested population seeding technique performs better than other GAs that use conventional population seeding strategies.

Modified genetic algorithm for optimal design of truss structures

In this paper, topology and shape optimization of truss or frame structures is discussed. The optimization starts from a structure, into which a finite number of nodesare set; all the nodes are connected together by trusses in all possible variants. Unfit variants of the truss system are rejected. Two alternative ways of the optimization are compared: topology optimization starting from initial structure with a larger number of nodes, and topology optimization starting from initial structure with smaller number of nodes but with additional shape optimization of the obtained topology. The topology optimization is solved with original modified genetic algorithm, giving better results in comparison with classical genetic algorithm. Instead of further development of constraint system, the additional step is introduced into algorithm – purification of genotype, which allows complementary improvement of particular population individuals, and together for the optimization process retains more possibilities than stiffening of constraints. The shape optimization is solved by classical genetic algorithm. Both strategies effectively improve the solution, however the common topology/shape optimization requires less computer resources. All numerical examples are obtained with original software developed by the authors.

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Abstract Background Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

Epigenetik Algoritma ile Konferans Çizelgeleme

The most important of the activities where the presentations of scientific studies take place are academic conferences. The days, halls, and sessions are determined in advance to organize multidisciplinary conferences and this process is called conference scheduling. In multidisciplinary conferences, in the scheduling of presentations, the coexistence of studies belonging to the same fields in the same sessions is very important for the conference listener and the conference speaker. In this context, the multidisciplinary conference scheduling problem is considered a multi-constraint optimization problem. Multi-constraint optimization problems are solved with heuristic optimization techniques, not traditional optimization methods. In this study, the problem of conference scheduling is addressed using multidisciplinary conference data. The solution to the conference scheduling problem was realized with Genetic Algorithm (GA) and Epigenetic Algorithm (EGA) using C# programming language. In the study, experimental results obtained with GA and EGA were examined. As a result of this examination, it was seen that EGA achieved better results in fewer iterations compared to classical GA.

Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle for Efficient Placement of Unmanned Aerial Vehicles in Natural Disaster Areas

A Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle (HQGAFARA) is introduced in this work to determine the optimal placements for Unmanned Aerial Vehicles (UAVs) aimed at maximizing coverage in disaster-stricken areas. The HQGAFARA is a hybrid quantum fuzzy meta-heuristic that uses the Deutsch–Jozsa quantum circuit to generate quantum populations synergistically working as haploid recombination and mutation operators that take advantage of quantum entanglement, providing exploitative and explorative features to produce new individuals. In place of the conventional lookup table or mathematical equation, we introduced a fuzzy heuristic to adapt the rotation angle employed in quantum gates. The hybrid nature of this algorithm becomes evident through its utilization of both classical and quantum computing components. Experimental evaluations were conducted using two distinct test sets. The first set, termed the “best case”, represents conditions that are the most favorable for determining the UAV positions, while the second set, the “worst-case”, simulates highly challenging conditions for locating the UAV positions, thereby posing a significant test for the proposed algorithm. We carried out statistical comparative analyses, assessing the HQGAFARA against other hybrid quantum algorithms that employ different rotation angles and against the classical genetic algorithm. The experimental results demonstrated that the HQGAFARA performed comparably, if not better, to the classical genetic algorithm regarding precision. Furthermore, quantum algorithms showcased their computational prowess in experiments related to the convergence time.

CSGA: A DUAL POPULATION GENETIC ALGORITHM BASED ON MEXICAN CAVEFISH GENETIC DIVERSITY

Genetic algorithms (GAs) are search algorithms based on population genetics and natural selection concepts. Maintaining population variety in GAs is critical for ensuring global exploration and mitigating the risks of premature convergence. Rapid convergence to local optima is one such challenge in the application of genetic algorithms. To address this issue, we provide Cave-Surface GA (CSGA), an alternative method based on the Dual Population Genetic Algorithm and inspired by the genetic variety observed in Mexican cavefish. Through inter-population crossbreeding, CSGA increases diversity via a secondary population (Cave population) and facilitates the exchange of information between populations, effectively counteracting premature convergence. Several experiments are carried out utilizing benchmark instances of the Traveling Salesman Problem (TSP) obtained from TSPLIB, a well-known TSP problem library. Our experimental results over many TSP instances show that CSGA outperforms both classic GAs and other GAs that use diversity preservation techniques, such as Multipopulation GA (MPGA). CSGA has the potential to give promising solutions to challenging optimization issues like TSP.

Destructive Ability Analysis of Genetic Algorithm Operator When Solving Business Process Problem of Structural and Parametric Synthesis

The work analyzes the destructive ability of operators of a classical genetic algorithm when solving the problem of controlling the trajectory of a population during the search for solutions. In accordance with the hypothesis put forward, when solving the problem of structural-parametric synthesis of large discrete systems with a given behavior, it is advisable to change the settings of the functioning of the genetic algorithm operators to ensure: better convergence of the genetic algorithm, avoid attenuation, signal the need to restart the solution synthesis procedure, and also facilitate the derivation of the population from local extrema. It is proposed to use an artificial neural network as a control element (control add-on), which should implement control of the process of synthesis of simulation models of business processes based on a given behavior (the ability of a simulation model to convert a given input vector into a reference output), which is especially important when working with large systems. In accordance with the same hypothesis, an increase in the destructive ability of the crossing over and mutation operators allows the population to be dispersed across the solution space, which is advisable when attenuation occurs and is in a local extremum, and a decrease in the destructive ability contributes to a more thorough search for solutions in a certain area of the solution space. The paper provides examples of the work of operators and the behavior of the population when synthesizing simulation models of business processes based on the theory of Petri nets. To model the operation of a genetic algorithm and an artificial neural network, it is proposed to use the theory of Petri nets, which simplifies the process of managing the synthesis procedure and allows the use of parallel programming tools with distributed GPGPU computing on CUDA technology. As part of this study, an analysis of the behavior of the population when changing the operating settings of operators was carried out, which confirmed the hypothesis put forward.

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.