Convergence Of Genetic Algorithm Research Articles

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures.

Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA's crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (-40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results.

Online learning resources recommendation model based on improved NSGA-Ⅱ algorithm

<abstract> <p>Due to the characteristics of online learning resource recommendation such as large scale, uneven quality and diversity of preferences, how to accurately obtain various personalized learning resource lists has become an urgent problem to be solved in the field of online learning resource recommendation. This paper proposes an online learning resource recommendation model based on the improved NSGA-Ⅱ algorithm, which integrates the Tabu search algorithm to improve the local search ability of NSGA-Ⅱ algorithm. It takes background fitness, cognitive fitness and diversity as the objective functions for optimization. The dynamic updating of crowding degree is used to avoid the risk that the individuals with low crowding degree in the same area are deleted at the same time. Meanwhile, an adaptive genetic algorithm is applied to assign the optimal crossover rate and the mutation rate according to individual adaptability level, which ensures the convergence of genetic algorithm and the diversity of population. The experimental results show that the proposed model is superior to the traditional recommendation algorithm in terms of accuracy index, mean fitness, recall rate, F1 mean, HV, GD and IGD, etc., thus verifying the feasibility and effectiveness of the algorithm.</p> </abstract>

Prediction Method of Heavy Load Wheel/Rail Wear Mechanical Properties Based on GA-BP Hybrid Algorithm

Due to its large axle load and high-density operation mode, heavy haul transportation has greatly improved the cargo transportation capacity, and is receiving unprecedented attention from all countries in the world. Since the development of heavy haul freight transport in China, wheel rail wear has been paid much attention, especially the use of heavy axle load locomotives on upgraded heavy haul lines, which makes reducing wheel rail wear and damage become a technical problem to be solved urgently. Considering that there are too many mechanical parameters involved in the prediction of heavy load wheel rail wear mechanical properties, the prediction accuracy is reduced. Therefore, this paper proposes a method based on GA-BP hybrid algorithm to predict the mechanical properties of heavy load wheel/rail wear. Hertz contact theory is used to simplify the wheel rail contact relationship, and the wheel rail contact model is established. According to the wheel/rail contact model, the expressions of heavy load wheel/rail in the case of vertical, horizontal, direction and gauge irregularity are analyzed, and based on this, a mechanical model of heavy load wheel/rail wear is established. In order to solve the problems of slow convergence speed and easy to fall into local optimum of BP neural network in the prediction of heavy load wheel/rail wear mechanical properties, the global convergence of genetic algorithm is used to optimize the BP network. According to the obtained mechanical parameters of heavy load wheel/rail wear, the mechanical parameters are input into the optimized model, and the relevant prediction results are output. So far, the research on the prediction method of heavy load wheel/rail wear mechanical properties based on GA-BP hybrid algorithm has been realized. The experiment is designed from three aspects of wear degree, hardness and tensile strength, and compared with the measured value, reference [4] method, reference [5] method and reference [6] method to verify the effectiveness of the proposed method. The experimental results show that the predicted results of wear degree, hardness and tensile strength by this method are closer to the measured results. It is proved that the proposed method has higher prediction accuracy and better practical application effect.

Multi-objective macrogeometry optimization of gears: Comparison between sequential quadratic programing and genetic algorithm

Deciding a gear pair’s macrogeometry parameters requires consideration of the factors such as cost of production, gear strength, and noise contributing parameters. A practical engineering problem is encountered when best possible gear geometry is needed for a fixed center distance to achieve the conflicting objectives. Further, constraints of practical utility on input parameters need to be taken into consideration for deciding the design space. This work represents comprehensive compilation of formulations of these often-conflicting objectives and constraints as non-linear functions and selection of an appropriate optimization technique to achieve a feasible design through a case study of a helical gear pair in high load carrying capacity mesh. For comparison of the quality of results and computational efficiency between gradient based and gradient free algorithms, Sequential Quadratic Programming and Genetic algorithm were used. The differences in the formulations of the objective functions for two algorithms were analyzed and addressed. Quadratic mutation rates and random crossovers were used to prevent premature convergence of Genetic Algorithm. Significant possible improvement in the base design was observed with both the algorithms.

Inversion method of particle size distribution of milk fat based on improved MPGA.

Milk fat’s particle size and distribution not only affect product quality, but also have great impacts on food safety in the economy and society. Based on total light scattering method, this paper has studied the inversion method of particle size distribution under dependent mode condition by combining multi-population genetic algorithm (MPGA) with Tikhonov smooth function. It has minimized the influence from light-absorb medium to improve the inversion accuracy. The approach introduces Tikhonov smooth function and apparent optical parameters to build an objective fitness function and weaken the ill condition of the particle size inversion equation. It also introduces multi-population genetic algorithm to solve the premature convergence of genetic algorithms. The results show that the relative error of the milk fat simulation solution with a nominal diameter is -3.52%, which meets the national standard of ±8% and better than the relative error of -5.01% of the standard genetic algorithm. Thus, the improved MPGA can reconstruct particle size distribution, with a good reliability and stability.

Rapid Control of Government Economic Environment Management Cost Based on Balanced Score Card.

EMC (economic management cost) of the government has become a hot topic of concern from all walks of life. Controlling and reducing government EMC is the requirement for building a conservation-oriented society and deepening the reform of public budget. Dynamic cost accounting calculates the cost of cost objects through the confirmation, measurement, and distribution of production costs. In this article, it is innovatively proposed to integrate BSC (balanced score card) into EMC control, and analyze the logical relationship between system objectives and functions by using the mathematical model. Based on GA (genetic algorithm) cost optimization control concept and specific control ideas, individuals who meet the evolutionary characteristics are stored in the crossover database and participate in crossover operation as parents when crossing. In order to improve the local optimization ability of the algorithm, parallel mutation operation mechanism is introduced, which can execute multiple mutation rules at the same time. The results show that the average convergence time of this algorithm is 0.186s and the variance of population fitness is 288.19. The conclusion shows that the algorithm proposed in this article can overcome the problems of slow convergence speed, low accuracy, and local convergence of GA, and effectively improve the overall performance of the algorithm.

Optimization of a Can Size Problem Using Real Encoded Chromosome in Genetic Algorithm

One of the major drawback of Genetic Algorithm (GA) based solutions to many optimization problems is the difficulty to obtain convergence to an optimal solution. One of the possible reason for not obtaining good convergence is due to the improper encoding of chromosomes. Many techniques were proposed in some previous researches for improving the convergence of GA based solutions. However, no consideration regarding the role of chromosome encoding in achieving convergence and optimality both has been discussed in the past. In the present work, a can volume optimization problem is solved with the help of two types of chromosome encoding techniques that are proposed and evaluated in GA environment. First, based on single random gene selection and second based on mean value of genes of the encoded chromosome. A numerical example with an objective function and constraints has been solved and the results for each of the scheme is being discussed.

50 kJ SMES magnet design optimization using real coded genetic algorithm

SMES systems are being made and utilized for various applications i.e. dampening low frequency oscillations, instantaneous voltage drop compensation, pulsating power supply etc. Due to tape like structure of 2G HTS and fabrication limits of long continuous length , SMES coil is made via assembling individual single pancake coil/double pancaked coil in the solenoid or toroid fashion. A SMES magnet is designed optimally, using real coded genetic algorithm, with minimum length as objective function for fxed amount of stored energy(50 kJ), using 2G HTS tape. Simulated binary crossover and polynomial mutation function are used for crossover and mutation respectively. Elitism is also used for faster convergence of genetic algorithm. An minimum length is found for 50 kJ stored energy. Stress calculation is carried out for this confguration.

Investigation of the Genetic Algorithm Convergence Used to Solve the Problem of Eliminating the Temperature Imbalance in Heated Rooms

The article justifies the relevance of artificial intelligence application for energy saving and studies of genetic algorithms used to solve the problems of eliminating the temperature imbalance in heated rooms. The objective of this study is to determine the relation between the rate of genetic algorithm convergence used to optimize the temperature of heated rooms and the number of adjustable parameters. The article also provides a brief overview of works in the field of automation and optimization of energy saving and overview of works in the field of genetic algorithms research, including studies of the dependence of the convergence of genetic algorithms on various parameters and the use of genetic algorithms for solving highly specific problems. This paper presents a mathematical formulation of the optimization problem arising from the problem of eliminating the temperature imbalance in heated rooms, including the formula for calculating the objective function. According to this formula, the objective function is the standard deviation of temperatures in heated rooms from the standard. The article also briefly provides a methodology for calculating the equilibrium temperature in heated rooms. The paper describes a genetic algorithm, including block design with explanations. The results of a computational experiment are presented with diagrams and a table. In the conclusion gives the experiment results: if the number of parameters to be regulated during optimization increases then the convergence of the genetic algorithm decreases (the number of iterations for which the stop condition is reached increases). This relation is nonlinear.

On the computation of the automorphisms group of low density parity check codes using genetic algorithm

<p>The genetic algorithm (GA) is an adaptive metaheuristic search method based on the process of evolution and natural selection theory. It is an efficient algorithm used for solving the combinatorial optimization problems, e.g., travel salesman problem (TSP), linear ordering problem (LOP), and job-shop scheduling problem (JSP). The simple GA applied takes a long time to reach the optimal solution, the configuration of the GA parameters is vital for a successful GA search and convergence to optimal solutions, it includes population size, crossover operator, and mutation operator rates. Also, very recently, many research papers involved the GA in coding theory, In particular, in the decoding linear block codes case, which has heavily contributed to reducing the complexity, and guaranting the convergence of searching in fewer iterations. In this paper, an efficient method based on the genetic algorithm is proposed, and it is used for computing the Automorphisms groups of low density parity check (LDPC) codes, the results of the aforementioned method show a significant efficiency in finding an important set of Automorphisms set of LDPC codes.</p>

Flipped Classroom Design of College Ideological and Political Courses Based on Long Short-Term Memory Networks

The advancement and rising of information technology have promoted the flipped classroom in an effective way. It flips knowledge transfer and knowledge internalization from two levels of teaching structure and teaching process, reversing the traditional teaching knowledge transfer in class and knowledge deepening after class from time and space. Although the use of flipped classrooms in ideological and political theory courses is relatively uncommon in colleges and universities, realistic teaching and related study findings in some colleges and universities provide some reference value for the use of flipped classrooms in ideological and political theory courses. As a result, the short- and long-time memory network-based flipped classroom design algorithm for ideological and political courses in colleges and universities has a wide range of applications. A neural network prediction model based on a hybrid genetic algorithm is developed in this paper. The hybrid genetic algorithm is used in this model to determine the optimal dropout probability and the number of cells in the hidden layer of the neural network. The hybrid genetic algorithm will lengthen the memory neural network to predict the teaching quality of root mean square error between real value and predictive value as a fitness function, in the process of optimization, genetic algorithm convergence to the local optimal solution of the area.

Parallel performance analysis of nonlinear equations algorithm based on hybrid genetic algorithm

For a long time, people have done a lot of research on nonlinear equations in theory and numerical calculation. In this paper, the basic theories and methods of genetic algorithm and traditional algorithm are expounded, and the advantages of genetic algorithm and quasi-Newton algorithm are analyzed, thus a new hybrid genetic algorithm for solving nonlinear numerical problems is proposed, and the effectiveness of the algorithm is verified by numerical examples. The hybrid algorithm gives full play to the group search and global convergence of genetic algorithm, and effectively overcomes the initial point sensitivity problem of classical algorithm At the same time, the classical algorithm is introduced into genetic algorithm for local search, which overcomes the shortcomings of slow convergence speed and poor accuracy of genetic algorithm. The algorithm in this paper provides an effective way to solve nonlinear equations from another angle.

Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

Hybrid model for tasks scheduling in distributed real time system

Resource allocation and their scheduling to optimize performance measures in heterogeneous environments are famous such as an NP-hard issue, not only for the resource heterogeneity, but also for the possibility of applying allocation to take advantage of idle resource. This article proposes a scheduling technique for communicating tasks by using two hybrid genetic algorithms (HGAs) which minimizes system cost and response time and maximizes the reliability of the distributed real time system. In the present technique, convergence of genetic algorithm (GA) is made better by offering new encoding and population initialization method and genetic operations. This technique is completed in two phases: Phase I develops hybrid c-mean genetic algorithm (HCMGA) which is a fusion of fuzzy c-means (FCM) technique and genetic algorithm (GA) and Phase II develops hybrid branch and bound genetic algorithm (HBBGA) which is a fusion of branch and bound (BB thus, superior results are obtained. This technique is suitable for arbitrary number of processors and tasks.

Non-Differentiable Learning of Quantum Circuit Born Machine with Genetic Algorithm

The Quantum Circuit Born Machine (QCBM) is a generative quantum machine learning model that can be efficiently trained and run on the NISQ era quantum processors. QCBM has greater expressive power than comparable classical neural networks such as Restricted Boltzmann Machine (RBM) and, therefore, has potential to demonstrate quantum advantage by generating high quality samples from the learned empirical distribution while using less computational resources than its classical counterpart. However, efficient training of QCBM remains a challenging problem. Traditional differentiable learning approach may not work well when the loss function is highly non-smooth. In such cases it may be more efficient to use the non-differentiable learning methods. This paper proposes a non-differentiable learning approach to the training of QCBM based on Genetic Algorithm (GA). The paper also presents results of the numerical experiments which compare performance of QCBM trained with GA against performance of the equivalent classical RBM and investigates the question of GA convergence as a function of QCBM architecture and the choice of algorithm’s hyperparameters.

Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization

With the rapid development of mobile networks and the proliferation of mobile devices, Spatial Crowdsourcing (SC) has attracted the interest of industry and research groups. In addition to considering the specific spatial constraints in the existing research spatial crowdsourcing, each task has an effective duration, operational complexity, number of workers required, and incentive budget constraints. In this scenario, we studied the MQC-TA (Maximum Quality and Minimum Cost Task Assignment) problem. Firstly, the worker incentive model is established. The MQC-GAC algorithm is designed according to the MQC-TA problem to maximize the task completion quality and minimize the incentive budget. The algorithm combined the fast convergence of Genetic Algorithm and the positive feedback mechanism of Ant Colony Optimization Algorithm. Finally, the effectiveness and efficiency of the proposed method are verified by a comprehensive experiment on the data set.

Learning-Aided Intelligent Cooperative Collision Avoidance Mechanism in Dynamic Vessel Networks

With the increasing traffic density and the highly maritime safety requirements, it is a great challenge to make the optimal motion decision for avoiding the collision in the complex vessel networks. However, the current collision avoidance mechanisms are only considering unilateral static information, ignoring the multilateral dynamic information, which is quite important. In this paper, we investigate the information transmission and the collision avoidance problem in the complex vessel networks, in which the devices are equipped with the capability of Artificial Intelligence (AI). With the empowered learning ability in the networks, we propose a novel two-step-smooth-turn cooperative collision avoidance mechanism for the dynamic vessel networks, considering the motion states of the vessels. In particular, the AI-powered vessels network motion model is designed, which can learn the motion information to predict the safety motion states by automatic collection of the information. Then, a K-Means algorithm combining with genetic algorithm is proposed by expanding the operation of cross genetic and genetic mutations, which can achieve the optimal multi-vessels coordination motion policy for the collision avoidance. Simulation results show that the proposed approach could improve the stability of genetic algorithm and weaken the early convergence of genetic algorithm efficiently. In addition, it shows that the proposed mechanism can achieve an optimal safety route plan for cooperative collision avoidance.

Improved niche genetic algorithm based parameter identification of excitation system considering parameter identifiability

In order to obtain the parameters of excitation system accurately, and improve the accuracy and efficiency of parameter identification much further, an improved niche genetic algorithm was adopted, which can overcome local convergence of genetic algorithm by the fitness sharing mechanism that could increase population diversity. Moreover, the proposed algorithm could avoid the disadvantage of usual niche genetic algorithm that is difficult in determining the niches. By using the method of fuzzy clustering, niche groups can be created dynamically. In addition, regarding the problem that the partial parameters of excitation system cannot identify stably because of the relevance among these parameters, this study analysed the identifiability of excitation system model primarily, separated associated parameters into several parameter sets then. This type of relevance would be removed by assigning typical values to representative parameters, which belong to different sets. Identifiability analysis can not only avoid identifying parameters blindly, but also increase the credibility of the results. The test study shows that the proposed method can acquire accurate and reliable parameter values.

Research on complex network community discovery based on adaptive cross mutation operator

Community discovery in complex networks has become a core issue in multidisciplinary cross-disciplinary research and has been successfully applied in many areas, such as social network analysis, protein network analysis, and link prediction. This paper proposes a genetic algorithm based on adaptive cross mutation operator for complex network community mining. The population is generated by establishing fitness value calibration and adaptive cross mutation operator, and a good individual is selected from it. An improved adaptive cross mutation operator is proposed to ensure the convergence of genetic algorithm and accelerate the generation of optimal solution while maintaining the diversity of population. Finally, experiments were carried out in multiple real networks to verify the stability and efficiency of the algorithm.

Signal Multiobjective Optimization for Urban Traffic Network

This paper proposes a multiobjective optimization method for signal control design at intersections in urban traffic network. The cell transmission model is employed for macroscopic simulation of the traffic. Additional rules are introduced to model different route choices from origins to destinations. Vehicle turning, merging, and diverging behaviors at intersections are considered. A multiobjective optimization problem (MOP) is formulated considering four measures in network traffic performance, i.e., maximizing system throughputs, minimizing traveling delays, enhancing traffic safety, and avoiding spillovers. The design parameters for an intersection include turning signal type, cycle time, signal offset, and green time in each phase. The resulting high-dimensional MOP is solved with the genetic algorithm (GA). An algorithm is proposed to assist the user to select and implement the optimal designs from the Pareto optimal solution set. A case study in a grid network of nine intersections is carried out to test the optimization algorithm. It is observed that the proposed method is able to achieve the optimal network performance with different traffic demands. The convergence and coefficient selection of GA are discussed. The guidelines for network signal design and operation from the current studies are presented.