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Optimization of urban terminal delivery routes using fuzzy genetic algorithm and its practical application

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Tang, Weiguang Wuhan Railway Vocat Coll Technol Sch Railway Transportat Management & Logist 1 Canglong Ave Wuhan 430205 Peoples R China

As urban populations grow and delivery demands surge, the computational complexity of route optimization problems escalates significantly. Traditional algorithms often struggle with low efficiency, making it challenging to achieve satisfactory solutions within a reasonable timeframe. To address these limitations, this paper introduces a fuzzy genetic algorithm (FGA) that integrates fuzzy logic to model uncertainties inherent in the delivery process, such as traffic congestion, weather disruptions, and demand fluctuations. By leveraging the multi-objective optimization capabilities of genetic algorithms, the proposed FGA comprehensively considers key factors such as delivery time, transportation costs, and customer satisfaction to generate optimal routes. The practical application of this approach demonstrates its effectiveness: the optimized delivery routes significantly reduce delivery times and transportation costs while enhancing customer satisfaction levels. Statistical analysis reveals p-values below 0.05, confirming the significant impact of the FGA on urban terminal delivery optimization. This research not only addresses the computational inefficiencies of traditional methods but also provides a robust framework for handling dynamic and uncertain urban environments. The integration of fuzzy logic and genetic algorithms represents a pioneering step toward sustainable urban logistics, offering both economic value-through cost savings-and social benefits-via improved service quality. In summary, the fuzzy genetic algorithm emerges as a powerful tool for modern urban delivery systems, enabling smarter decision-making and fostering greener, more efficient cities.

关键词： urban terminal delivery fuzzy genetic algorithm route optimization multi-objective optimization Pearson correlation

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Evaluation Model of Click Rate of Electronic Commerce Advertising Based on fuzzy genetic algorithm

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MOBILE NETWORKS & APPLICATIONS 2022年第3期27卷 936-945页

作者： Song, Peisen Chen, Chen Zhang, Lei China Univ Min & Technol Sch Management Xuzhou 221000 Jiangsu Peoples R China Xuzhou Coll Ind Technol Sch Informat & Elect Engn Xuzhou 221000 Jiangsu Peoples R China Xuzhou Coll Ind Technol Coll Business Adm Xuzhou 221000 Jiangsu Peoples R China

In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on fuzzy genetic algorithm is proposed. The big data information sampling model of e-commerce advertisement click rate evaluation, based on the mining result of e-commerce advertisement click rate evaluation information, carries on the adaptive mining and fusion clustering processing to the e-commerce advertisement click rate evaluation data, extracts the similarity information of the e-commerce advertisement click rate distribution set, carries on the e-commerce advertisement click rate adaptive evaluation according to the similarity contrast method, carries on the feature coding in the e-commerce advertisement click rate evaluation process through the fuzzy genetic optimization method, establishes the e-commerce advertisement click rate reliability feature distribution function, combines the statistical feature analysis and the fuzzy feature cluster analysis method to evaluate and predict the e-commerce advertisement click rate. The simulation results show that this method has better adaptability, higher accuracy and better convergence.

关键词： fuzzy genetic algorithm e-commerce advertisement Click rate Evaluation Adaptive mining Clustering

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Security Protection Technology in Multi-Attribute Data Transmission Based on fuzzy genetic algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期127卷 897-917页

作者： Lv, Shanguo Chen, Hongli East China Jiaotong Univ Sch Software Nanchang 330013 Jiangxi Peoples R China

Because the traditional data transmission security protection methods ignore the process of multi-attribute data detection, resulting in the abnormal data false alarm rate, high missing alarm rate, eliminating accuracy and other problems, a multi-attribute data transmission security protection method based on fuzzy genetic algorithm is proposed. The anomaly detection method based on fuzzy data mining and genetic algorithm is adopted to detect and obtain the abnormal data in the multi-attribute data transmission, and the abnormal data in the multi-attribute data transmission is eliminated through the abnormal data elimination method based on PSO and SVM, so as to realize the security protection of multi-attribute data transmission. It is verified that the recall rate and accuracy rate of abnormal data of the proposed method are higher than 95%, and the removal accuracy of abnormal data is higher. Moreover, this method is far better than the comparison method in positive likelihood ratio and Jordan index, and has higher application value.

关键词： fuzzy genetic algorithm Multiple attributes Data Transmission Security Protection

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Automatic fuzzy genetic algorithm in clustering for images based on the extracted intervals

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第28-29期80卷 35193-35215页

作者： Dinh Phamtoan Tai Vovan Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Van Lang Univ Fac Engn Ho Chi Minh City Vietnam Can Tho Univ Coll Nat Sci Can Tho Vietnam

This research proposes the method to extract the characteristics of images to become the intervals. These intervals are used to build the automatic fuzzy genetic algorithm for images (AFGI). In the proposed model, the overlap measure is the criterion to evaluate the closeness of intervals, and the new Davies and Bouldin index is the objective function. The AFGI can determine the proper number of clusters, the images in each cluster, and the probability to belong to clusters of images at the same time. The experiments with different types of images illustrate the steps of AFGI, and show its significant benefit in comparing to other algorithms.

关键词： Cluster analysis fuzzy genetic algorithm Image processing Interval data Pattern recognition Unsupervised learning

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Modeling of Main Steam Temperature Using an Improved fuzzy genetic algorithm 39

Modeling of Main Steam Temperature Using an Improved Fuzzy G...

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39th Chinese Control Conference (CCC)

作者： Cheng, Chuanliang Peng, Chen Zeng, De-liang Zhang, Tengfei Shanghai Univ Sch Mechatron Engn & Automat Shanghai 201900 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

ISBN: (纸本)9789881563903

Main steam temperature is an important parameter in thermal power plants. In order to get a better control effect, an improved fuzzy genetic algorithm (FGA) modelling method is proposed in this paper. Different from traditional identification methods, the improved FGA is more suitable for modeling the dynamic process of the main steam temperature which has the characteristics of strong coupling and nonlinearity. Firstly, to solve the nonlinear problem, a fuzzy thought is introduced to partition the whole operating region into several local regions. Then an improved genetic algorithm (GA) which has better identification capability is proposed to build the linear model in each local region. The nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. The simulation results show that the improved FGA can achieve satisfactory effects even in the case of strong coupling and nonlinearity, which provides theory foundation for other control methods.

关键词： Main steam temperature fuzzy genetic algorithm Scheduling program Local linear models

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Diabetes Classification with fuzzy genetic algorithm 14th

Diabetes Classification with Fuzzy Genetic Algorithm

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14th International Conference on Computing and Information Technology (IC2IT)

作者： Thungrut, Wissanu Wattanapongsakorn, Naruemon King Mongkuts Univ Technol Dept Comp Engn 126 Pracha Utid Rd Bangkok Thailand

ISBN: (纸本)9783319936925;9783319936918

In this research work, we consider the diabetes classification on PIMA Indian dataset with fuzzy genetic algorithm. We applied two algorithms consisting of fuzzy algorithm and genetic algorithm to combine the process to enhance the classification performance. In addition, we used Synthetic Minority Over-sampling Technique (SMOTE) to handle the imbalance data set. We conducted the experiments and found out that 5-fold cross-validation is the suitable approach, providing very good results compared with those obtained from other techniques.

关键词： fuzzy genetic algorithm Diabetic SMOTE BaggingLOF Imputation technique

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Modeling of Main Steam Temperature Using an Improved fuzzy genetic algorithm

Modeling of Main Steam Temperature Using an Improved Fuzzy G...

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第三十九届中国控制会议

作者： Chuanliang Cheng Chen Peng De-liang Zeng Tengfei Zhang School of Mechatronic Engineering and Automation Shanghai University School of Control and Computer Engineering North China Electric Power University College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications

Main steam temperature is an important parameter in thermal power plants. In order to get a better control effect, an improved fuzzy genetic algorithm（FGA） modelling method is proposed in this paper. Different from traditional identification methods, the improved FGA is more suitable for modeling the dynamic process of the main steam temperature which has the characteristics of strong coupling and nonlinearity. Firstly, to solve the nonlinear problem, a fuzzy thought is introduced to partition the whole operating region into several local regions. Then an improved genetic algorithm（GA） which has better identification capability is proposed to build the linear model in each local region. The nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. The simulation results show that the improved FGA can achieve satisfactory effects even in the case of strong coupling and nonlinearity, which provides theory foundation for other control methods.

关键词： Main steam temperature fuzzy genetic algorithm Scheduling program Local linear models

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fuzzy genetic algorithm of Differential and Integral of Courseware and Closed Loop Automatic Control of Courseware System in 2nd Generation CAI

Fuzzy Genetic Algorithm of Differential and Integral of Cour...

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第29届全国计算机新科技与教育学术会议

作者： Fengxiang Zhang School of Computer Science&Technology Huazhong University of Science&Technology Wuhan P.R.China 430077

This paper describes the Differential and Integral of courseware in 2nd generation CAI,courseware differential point database,closed loop automatic control,the degree of difficulty of process control,and the fuzzy genetic *** have successfully used this structure in developing the courseware,which has some features of second generation CAI..This is one of critical projects completed by author,supported by Ministry of Science and Technology of China.

关键词： Second Generation CAI Differential and integral of courseware fuzzy genetic algorithm

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Automatic Generation Control using fuzzy genetic algorithm Considering Wind Fluctuations 8

Automatic Generation Control using Fuzzy Genetic Algorithm C...

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8th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Chen, Qiyu He, Guoqing Lv, Chen Yang, Xiuyuan Yang, Yuhang China Elect Power Res Inst Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Coll Automat Beijing 100192 Peoples R China Cpecc Eastchina Design Branch Beijing Branch Beijing 100101 Peoples R China

ISBN: (纸本)9781538657744

With the increasing of the wind power capacity, new requirements for load frequency control considering wind power penetrate is put forward. To solve the wind power fluctuations on the power system frequency, designed a model of the load frequency controller using fuzzy genetic algorithm. Selecting the crossover probability with fuzzy decision-making, adaptively improve the adjustment of Proportional-Integral (PI) parameters. Simulation of two area's wind power and load disturbance verified the effectiveness of fuzzy genetic algorithm for load frequency control which penetrated by wind power;the superiority between the conventional Automatic Generation Control (AGC) and standard genetic algorithm was shown.

关键词： Wind power Load frequency control fuzzy genetic algorithm Interconnection power systems AGC

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Intrusion Detection System using fuzzy genetic algorithm

Intrusion Detection System using Fuzzy Genetic Algorithm

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International Conference on Pervasive Computing (ICPC)

作者： Danane, Yogita Parvat, Thaksen Sinhgad Inst Technol Dept Comp Sci Lonavala Sppu India

ISBN: (纸本)9781479962723

Computer security has become an important part of the day today's life. Not only single computer systems but an extensive network of the computer system also requires security. In achieving the safety of the systems, an Intrusion Detection System (IDS) plays a significant role. IDS is a software that monitors the computer network and detects the suspicious activities that occur in the systems or network. The process of intrusion detection includes detecting intrusion. Intrusion is a suspicious activity attempted by the attacker. This paper presents a fuzzy-genetic approach to detecting network intrusion. Paper presents the results of the proposed system in terms of accuracy, execution time, and memory allocation. To implement and measure the performance of the system the KDD99 benchmark dataset is used. The KDD99 dataset is a benchmark dataset that researchers use in various network security researches. genetic algorithm includes a development and collection that uses a chromosome like data structure and develop the chromosomes using selection, crossover and mutation operators. fuzzy rule sorts network attack data.

关键词： fuzzy algorithm fuzzy genetic algorithm genetic algorithm intrusion detection intrusion detection system KDD 1999 dataset

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