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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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fuzzy genetic algorithm based on principal operation and inequity degree

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3rd International Conference on Intelligent Computing

作者： Li, Fachao Jin, Chenxia Hebei Univ Sci & Technol Sch Econ & Management Shijiazhuang 050018 Hebei Peoples R China Hebei Univ Sci & Technol Sch Sci Shijiazhuang 050018 Hebei Peoples R China

ISBN: (纸本)9783540742012

In this paper, starting from the structure of fuzzy information, by distinguishing principal indexes and assistant indexes, give comparison of fuzzy information on synthesizing effect and operation of fuzzy optimization on principal indexes transformation, further, propose axiom system of fuzzy inequity degree from essence of constraint, and give an instructive metric method;Then, combining genetic algorithm, give fuzzy optimization methods based on principal operation and inequity degree (denoted by BPO&ID-FGA, for short);Finally, consider its convergence using Markov chain theory and analyze its performance through an example. All these indicate, BPO&ID-FGA can not only effectively merge decision consciousness into the optimization process, but possess better global convergence, so it can be applied to many fuzzy optimization problems.

关键词： fuzzy optimization fuzzy inequity degree principal index fuzzy genetic algorithm BPO&ID-FGA Markov chain

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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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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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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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A fuzzy genetic algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第1期213卷 96-106页

作者： Maiti, Manas Kumar Mahishadal Raj Coll Dept Math Purba Medinipur 721628 W Bengal India

A genetic algorithm (GA) with varying population size is developed where crossover probability is a function of parents' age-type (young, middle-aged, old, etc.) and is obtained using a fuzzy rule base and possibility theory. It is an improved GA where a subset of better children is included with the parent population for next generation and size of this subset is a percentage of the size of its parent set. This GA is used to make managerial decision for an inventory model of a newly launched product. It is assumed that lifetime of the product is finite and imprecise (fuzzy) in nature. Here wholesaler/producer offers a delay period of payment to its retailers to capture the market. Due to this facility retailer also offers a fixed credit-period to its customers for some cycles to boost the demand. During these cycles demand of the item increases with time at a decreasing rate depending upon the duration of customers' credit-period. Models are formulated for both the crisp and fuzzy inventory parameters to maximize the present value of total possible profit from the whole planning horizon under inflation and time value of money. fuzzy models are transferred to deterministic ones following possibility/necessity measure on fuzzy goal and necessity measure on imprecise constraints. Finally optimal decision is made using above mentioned GA. Performance of the proposed GA on the model with respect to some other GAs are compared. (C) 2011 Elsevier B.V. All rights reserved.

关键词： fuzzy genetic algorithm fuzzy rule base Credit-linked demand Imprecise planning horizon

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Committee neural networks with fuzzy genetic algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2011年第3-4期76卷 217-223页

作者： Jafari, S. A. Mashohor, S. Varnamkhasti, M. Jalali Univ Putra Malaysia Fac Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia Inst Math Res Lab Appl & Computat Stat Serdang 43400 Selangor Malaysia

Combining numerous appropriate experts can improve the generalization performance of the group when compared to a single network alone. There are different ways of combining the intelligent systems' outputs in the combiner in the committee neural network, such as simple averaging, gating network, stacking, support vector machine, and genetic algorithm. Premature convergence is a classical problem in finding optimal solution in genetic algorithms. In this paper, we propose a new technique for choosing the female chromosome during sexual selection to avoid the premature convergence in a genetic algorithm. A bi-linear allocation lifetime approach is used to label the chromosomes based on their fitness value, which will then be used to characterize the diversity of the population. The label of the selected male chromosome and the population diversity of the previous generation are then applied within a set of fuzzy rules to select a suitable female chromosome for recombination. Finally, we use fuzzy genetic algorithm methods for combining the output of experts to predict a reservoir parameter in petroleum industry. The results show that the proposed method (fuzzy genetic algorithm) gives the smallest error and highest correlation coefficient compared to five members and genetic algorithm and produces significant information on the reliability of the permeability predictions. (C) 2011 Elsevier B.V. All rights reserved.

关键词： back propagation neural network committee neural network fuzzy genetic algorithm reservoir properties

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Optimization of a subset of features based on fuzzy genetic algorithm

Optimization of a subset of features based on fuzzy genetic ...

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IEEE International Symposium on IT in Medicine and Education

作者： Liu Peiyu Zhu Zhenfang Xu Liancheng Chi Xuezhi Shandong Normal Univ Sch Informat Sci & Technol Jinan 250014 Peoples R China Shandong Police coll Sch Comp Jinan 250014 Peoples R China

ISBN: (纸本)9781424439294

To overcome global situation problem tradition genetic algorithm has very strong robustness in finding the solution, but crossover probability and mutation probability is fixed and invariable, it caused premature convergence and running inefficient to the solution on complicated problem at later evolution process of tradition genetic algorithm. To this problem the paper proposed a new algorithm with varying population size based on lifetimes of the chromosomes to realize population size adjust adoptively and crossover probability adjust adaptively and mutation probability adjust adoptively, which called fuzzy genetic algorithm. Compare to tradition genetic algorithm, experiment results show that the approach proposed is effective in the capability of global optimization and significantly improves the convergence rate.

关键词： fuzzy genetic algorithm population size Subset genetic algorithm (GA) Global optimization mean life

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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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Multi-Objective Portfolio Optimization Based on fuzzy genetic algorithm

Multi-Objective Portfolio Optimization Based on Fuzzy Geneti...

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9th International Conference on Computational Intelligence and Security (CIS)

作者： Yi, Huilin Yang, Jianhui S China Univ Technol Sch Business Adm Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9781479925483

Based on the Markowitz portfolio model, this paper considered the liquidity of the risk assets, set the minimum expected return rate of investors, and changed the inequality constraint to semi-gradient fuzzy number. Then used the penalty factor to adjust the objective function, taking the maximum liquidity and minimum risk of portfolio as the objectives, we established the Multi-objective portfolio optimization model. Using the weekly return rate and weekly turnover rate data of eight stocks that are typical in several industries, we can get the Pareto optimal solution set of risk assets investment proportion by fuzzy genetic algorithm. Investors can accord to the personal attitude toward return, risk, liquidity to choose better investment combination proportion of risk assets.

关键词： Portfolio Multi-objective fuzzy genetic algorithm Pareto Optimality Risk appetite

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