Adaptive Mutation Probability Research Articles

Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm.

Optimal Configuration of Additional Heat Source for CHP System considering Demand Response Based on Comprehensive Benefits

An optimal configuration method for the combined heat and power (CHP) system considering demand response is proposed to scientifically and reasonably configure the parameters of the additional heat source and reduce unnecessary investment and construction costs. First, additional heat sources and demand response are utilized to decouple power generation and heating supply, enhancing the flexibility of the CHP system. Second, a multiobjective optimization configuration model of the CHP system is established, taking the system’s comprehensive satisfaction, economic cost, and wind power consumption capacity as the objectives, the unit output, and the capacity parameters of the additional heat source as the decision variables. Furthermore, an improved memetic algorithm (IMA) combined with a hierarchical sequence method is designed to solve the optimization model characterized by multiple objectives, hierarchical levels, and nonlinearity. The hierarchical sequence method solves problems sequentially based on the importance of optimization goals, ensuring the satisfaction of the configuration scheme. The IMA employs adaptive crossover and mutation probabilities, enhancing the algorithm’s convergence and quality. Finally, case analysis demonstrates that the CHP system achieves the best benefits when heat storage tanks and electric boilers are configured simultaneously. Moreover, compared to the MA, IPSO, and IABC algorithms, the IMA algorithm reduces the average economic cost by approximately 5.11%, 2.70%, and 8.43%, respectively.

Optimization of elastic wave propagation in a reconfigurable medium by genetic algorithms with adaptive mutation probability

We introduce a reconfigurable medium for the manipulation of elastic propagation properties of Lamb waves. It is based on a shape memory polymer (SMP) with temperature-dependent Young’s modulus. Waves are excited by a laser pulse and detected by a laser vibrometer. A two-dimensional temperature field is controlled by a scanning heating laser. We use genetic algorithms to determine optimal distributions of mechanical properties for the following criteria: the wave amplitude has to be maximized at a given location and at the same time minimized at one or two other locations. Due to the reconfigurability of the medium, the optimization process is performed directly on the object of optimization, and not on a numerical or analytical representative, based on a direct measurement of the fitness. The optimized configuration makes the waves propagate away from (or around) the point of minimization towards the point of maximization. We improve the genetic algorithm by adapting the mutation probability of individual genes according to specific criteria, which depend on the surrounding genes (distributed in two dimensions). This provides the advantages: concentrating the mutations in the areas of genetic inconsistencies and counterbalancing the error of the fitness measurement. The method is applicable for the intelligent design of wave energy harvesters, ultrasonic transducers, and analogue wave computing devices.

An Improved Genetic-XGBoost Classifier for Customer Consumption Behavior Prediction

AbstractIn an increasingly competitive market, predicting the customer’s consumption behavior has a vital role in customer relationship management. In this study, a new classifier for customer consumption behavior prediction is proposed. The proposed methods are as follows: (i) A feature selection method based on least absolute shrinkage and selection operator (Lasso) and Principal Component Analysis (PCA), to achieve efficient feature selection and eliminate correlations between variables. (ii) An improved genetic-eXtreme Gradient Boosting (XGBoost) for customer consumption behavior prediction, to improve the accuracy of prediction. Furthermore, the global search ability and flexibility of the genetic mechanism are used to optimize the XGBoost parameters, which avoids inaccurate parameter settings by manual experience. The adaptive crossover and mutation probabilities are designed to prevent the population from falling into the local extremum. Moreover, the grape-customer consumption behavior dataset is employed to compare the six Lasso-based models from the original, normalized and standardized data sources with the Isometric Mapping, Locally Linear Embedding, Multidimensional Scaling, PCA and Kernel Principal Component Analysis methods. The improved genetic-XGBoost is compared with several well-known parameter optimization algorithms and state-of-the-art classification approaches. Furthermore, experiments are conducted on the University of California Irvine datasets to verify the improved genetic-XGBoost algorithm. All results show that the proposed methods outperform the existing ones. The prediction results provide the decision-making basis for enterprises to formulate better marketing strategies.

A multi-strategy improved slime mould algorithm for global optimization and engineering design problems

In the application of metaheuristic algorithms (MAs) to complicated optimization problem solving, it is significant to balance the exploitation and exploration to obtain a good near-optimum solution to the problem. Therefore, in this study, to balance the exploitative and explorative features of conventional MAs, a multi-strategy improved slime mould algorithm called MSMA is introduced. In MSMA, a new search equation is developed to achieve a tradeoff between exploitation and exploration. Then the dynamic random search technique is utilized as a local search engine to enhance the search efficiency of the algorithm. Finally, the adaptive mutation probability is designed to avoid premature convergence. MSMA is evaluated using 28 benchmark functions and several practical engineering issues such as welded beam design, pressure vessel design, tension/compression spring design, and UAV path planning. The simulation results based on 30 independent runs demonstrate that it is more efficient and robust than other state-of-the-art techniques from the literature according to the selected performance metrics such as mean values and standard deviations. The source code of MSMA is publicly available at https://github.com/denglingyun123/Multi-strategy-improved-slime-mould-algorithm.

Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared.

In the past few decades, there has been significant progress made in metasurfaces and integrated and miniaturized optical devices. As one of the most prominent applications of metasurfaces, the metalens is the subject of significant research. In this paper, for achieving better focusing performance of the initial metalens designed by the Pancharatnam-Berry (PB) phase, a concept of micro-dimensional oscillation is proposed to optimize the geometric parameters of nanopillars. A strategy of grouping iteration is proposed to reduce the loss rate and computational effort in a holistic way. Its essence is to divide an extremely large-scale optimization space into many overlapping groups. Meanwhile, an improved genetic-simulated annealing (IGSA) algorithm is presented for the optimal solution of each group. By introducing the adaptive crossover and mutation probabilities in traditional genetic algorithms, the IGSA algorithm has both strong global searching capability and excellent local searching capability. After optimization, the maximum field intensity of the central hot spot can be increased by about 8% compared to the initial metalens. Moreover, the field intensity of the side lobes around the hot spot is almost constant, and the central hot spot increases, which provides a potential for the realization of high imaging contrast.

Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics

To optimize the colors used in cultural and creative products, this paper proposes a color matching design method that considers the color image and visual aesthetics. First, 99 color samples are identified based on Chinese traditional colors, and user preferences for 30 image semantic terms are measured by the semantic differential method. This leads to six color image factors being extracted by factor analysis. Second, quantitative analysis of the color visual aesthetics is applied, and formulas for calculating the harmony, balance, and symmetry are derived. On this basis, an interactive genetic algorithm is developed to promote and optimize the color scheme of cultural and creative products, and a fitness function based on subjective image evaluation and objective visual aesthetics is constructed. The subjective image evaluation adopts interval numbers, and a grayscale approach is used to measure the uncertainty of the subjective evaluation. Through grayscale analysis of the interval fitness values, information reflecting the evolutionary distribution of the population is extracted, before adaptive crossover and mutation probabilities are applied to the evolutionary individuals. Finally, the proposed method is verified through the example of color matching design for a speaker box. The results demonstrate that the proposed approach can effectively assist industrial designers.

Quality Evaluation Method of College Sports Long Jump Training Course Based on Genetic Algorithm

In order to improve the accuracy of the quality evaluation of college sports long jump training courses, this paper studies the quality evaluation method of college sports long jump training courses based on a genetic algorithm. According to the purpose of college long jump training course evaluation, basic principles of teaching quality evaluation and classroom teaching evaluation standards constructs the establishment process of evaluation index system and calculates the weight of long jump training course quality evaluation index based on AHP method, so as to provide index basis for subsequent quality evaluation; The quality evaluation model of the long jump training course is constructed based on the genetic algorithm. Through cross operation and mutation operation based on adaptive mutation probability, the high accuracy evaluation of college sports long jump training course quality is realized. The experimental results show that the evaluation accuracy of this design method is higher than 97.21%, and the evaluation satisfaction is higher than 97.15%, which significantly improves the accuracy of the university sports long jump training courses and has practical significance in the course evaluation.

Optimization Model and Algorithm of Cigarette Distribution Route Based on Cluster Analysis

Cigarette distribution is an important part of the tobacco logistics supply chain, and it will affect the distribution cost, efficiency, and service quality. In this paper，we choose a two-stage optimization method to analyze the cigarette distribution route across the Administrative Region in China. First, we use a K-means clustering algorithm to generate an initial center. Then, we optimize the clustering region with the vehicle load and workload as constraints. Finally, we establish the cigarette distribution route optimization model by taking the lowest distribution cost as the objective function. In the model calculation, we not only select the adaptive genetic algorithm as the solution algorithm but also use the scanning algorithm to generate the initial population ,design adaptive crossover and mutation probability, and also design inverse operator to correct the error, and we wish to improve the solution performance of the algorithm by this way.In addition, taking the cigarette distribution of Chongqing Tobacco Logistics as an example, this paper analyzes the number of vehicles, average loading rate, and total distribution distance and distribution cost, and finds that the cross-regional joint distribution route is better than independent distribution. And we also find that using clustering analysis and the genetic algorithm could reduce the amount of computation and solve the problem effectively.

A novel evacuation path planning method based on improved genetic algorithm

In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm.

Customer Segmentation Using K-Means Clustering and the Hybrid Particle Swarm Optimization Algorithm

AbstractIn a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination; the roulette operator is used to select excellent particles; then, the selected particles are crossed according to their adaptive crossover probabilities; when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means’ sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation.

Application of Improved Genetic Algorithm in FJSP

In this paper, the traditional genetic algorithm is improved, through MSOS coding, combined with IPOX and MPX adaptive crossover operators, the fitness function is improved, the adaptive crossover mutation probability function is introduced, and the simulation experiments are carried out on two standard instances of MK01 and 8×8. The solution of the 8×8 standard instance with the traditional genetic algorithm verifies the effectiveness and superiority of the improved genetic algorithm proposed in this paper.

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

Due to their working environments, limited resources and communication characteristics, wireless sensor networks face some challenges including energy optimization and security enhancement to extend the network lifetime and guarantee the network security. Therefore, an energy-aware and trust-based routing protocol for wireless sensor networks using adaptive genetic algorithm called TAGA is proposed to not only resist common routing attacks and special trust attacks, but also minimize the energy consumption caused by data transmission. To this end, TAGA constructs the nodes’ comprehensive trust values based on their direct trust values considering the volatilization and adaptive penalty factors, and indirect trust values with the filtering mechanisms. In addition, a novel threshold function is presented to select the optimal cluster heads, which considers the dynamic changes of the nodes’ comprehensive trust values and residual energy. Finally, a genetic algorithm with adaptive crossover probability and mutation probability is applied to find the optimal secure routing for the cluster heads. The simulation results show that TAGA can reduce the number of packets discarded by malicious nodes when facing common attacks and special trust attacks, and effectively improve the energy efficiency compared to the relative secure routing protocols EOSR and IASR.

Genetic Algorithm With Three-Dimensional Population Dominance Strategy for University Course Timetabling Problem

In recent years, with the growing expansion of the recruitment scale and the further reform in teaching, how to use the limited teacher resources and the limited classroom resources to schedule a reasonable university course timetable has gotten great interest. In this paper, the authors firstly hashed over the university course timetabling problem, and then they presented the related mathematical model and constructed the relevant solution framework. Subsequently, in view of characteristics of the university course timetabling problem, they introduced genetic algorithm to solve the university course timetabling problem and proposed many improvement strategies which include the three-dimensional coding strategy, the fitness function design strategy, the initial population generation strategy, the population dominance strategy, the adaptive crossover probability strategy, and the adaptive mutation probability strategy to optimize genetic algorithm. Simulation results show that the proposed genetic algorithm can solve the university course timetabling problem effectively.

Clustering and Prediction Analysis of the Coordinated Development of China’s Regional Economy Based on Immune Genetic Algorithm

Since the opening of the economy, China’s regional economy has developed rapidly, the overall national strength has been increasing, and the people’s living standards have been continuously improved. The issue of coordinated regional development has become an important issue in today’s society. Genetic algorithm is a kind of prediction algorithm that has developed rapidly in recent years and is widely used. However, when solving engineering prediction problems, there are often problems such as premature convergence and easiness to fall into local optimal solutions. Therefore, on the basis of studying the related theories of genetic algorithm and artificial immune algorithm, this paper uses the advantages of the two algorithms, combines the two algorithms, and proposes an improved algorithm for genetic algorithm-adaptive immune genetic algorithm. Taking genetic algorithm as the basic framework, the operators and selection methods of artificial immune algorithm are integrated. Using the adaptive concept, the formulas of adaptive crossover probability and mutation probability are innovatively designed. Compared with the fixed value of the immune genetic algorithm, the introduction of the adaptive concept can intelligently adjust the optimization process and increase the optimization speed. Considering the double uncertain factors of product market demand and waste product recycling in the remanufacturing supply chain system, the maximization of logistics network operating profit, the minimization of environmental impact, and the maximization of customer satisfaction are the forecast goals. The market demand of uncertain products is effectively controlled through the option contract mechanism, and a multiobjective forecasting model based on the option contract mechanism is established. According to the characteristics of the model, an improved immune genetic algorithm is designed to solve the problem, and the effectiveness of the immune genetic algorithm is verified through an example.

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.

Adaptive Elitist Genetic Algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

This paper applies the elitist genetic algorithm to the electric vehicle routing problem with time window. In initialization, the paper proposes an improved neighbor routing initialization method for adaptive elitist genetic algorithm. The improved neighbor routing method is used to select the nearest EV customer as the next route to be scheduled and make the route start from the suitable first customer in the initialization of the elitist GA. It makes the scheduled route begins with a neighboring directionality, which can be inherited in selection, crossover, and mutation operations. For effective convergence, new adaptive crossover probability and mutation probability are provided to make the algorithm converge faster. Experimental studies on randomly distributed customers and Solomon benchmark cases show the effective performance of the algorithm. The algorithm is demonstrated in the simulation of a U.S. Postal Service system.

A modified genetic algorithm optimized SVM for rapid classification of tea leaves using laser-induced breakdown spectroscopy

This work provides a modified adaptive mutation probability selection genetic algorithm to optimize the SVM model, which improved the accuracy of tea sample classification by LIBS and its recognition accuracy was higher than CV-SVM and PSO-SVM.

Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions

In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

Hierarchical Path Planning of Unmanned Surface Vehicles: A Fuzzy Artificial Potential Field Approach

Under unforeseen circumstances, a hierarchical path planning (HPP) scheme combining global and local tasks of an unmanned surface vehicle (USV) is proposed by devising fuzzy artificial potential field (FAPF). Within the HPP scheme, the elite reservation, diversity increment, adaptive mutation probability and adaptive genetic algorithm are incorporated to generate optimally sparse waypoints together with smooth path. To exclusively accommodate unpredictable environments, the innovative FAPF method with fuzzy decision-making is developed to remove singular dilemmas, pertaining to which the obstacles on the path make the USV hesitate. By inserting virtual returning points onto the global path, a hybrid compound combining the global and local paths is established. Finally, the simulations and comparisons in various geographical areas demonstrate the effectiveness and superiority of the proposed HPP scheme.