改进人工蜂群算法及其在电子商务中的应用研究

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3.0 侯斌 2024-11-19 6 4 1.53MB 70 页 15积分

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摘要

人工蜂群算法是一种新型的群智能算法，它模拟的是工蜂采蜜过程中各种蜜

蜂分工合作以开采最佳蜜源的原理。该算法将采蜜蜂群分为引领蜂、跟随蜂和侦

查蜂三类，通过三种蜜蜂轮流开采来寻找全局最优解。食物源的花蜜量与食物源

被选择的可能性成正比，蜜蜂能及时停止对较差食物源的开采。且蜜蜂能与其他

蜜蜂共同分享食物源的信息。因此，人工蜂群算法有其自身优势，但与其他的智

能算法一样，人工蜂群算法也存在着易收敛到局部极值点、应用领域有待进一步

拓宽等问题。

本文从改进人工蜂群算法的缺陷，以及该算法在电子商务领域问题中的应用

两个大的方面出发，进行了深入研究。主要工作包括：

（1）综述了人工蜂群算法的思想及求解步骤，介绍蜂群优化算法的历史研

究背景，以及目前蜂群优化算法在国内外的应用情况，总结了人工蜂群算法的优

缺点。

（2）针对人工蜂群算法容易早熟收敛的缺陷，利用元胞自动机原理设计了

求解 0-1规划问题的元胞人工蜂群算法。算法将元胞演化和人工蜂群搜索相结合，

利用元胞及其邻居的演化提高了种群多样性，避免陷入局部最优解。算例分析通

过与标准人工蜂群算法、基于最优值的蜂群算法求解得到的结果进行了比较，元

胞人工蜂群算法具有更好的求解效率和稳定性。

（3）针对人工蜂群算法种群多样性不足和求解精度低的缺陷利用自然选择

和蝙蝠回声定位原理对人工蜂群算法进行改进，将改进的算法用于求解频率分配

问题。通过对不同分配频点的 21 小区的频率分配问题进行求解，并与蝙蝠算法、

标准人工蜂群算法相对比，改进的人工蜂群算法求解更快速准确。

（4）介绍了多 Agent 自动谈判的特性、协议以及过程。结合人工蜂群算法

的原理及求解流程，给出一种电子商务自动谈判模型。模型基于多 Agent 多属性

的自动谈判模式，融合了智能化电子商务谈判技术，着重将自动谈判过程与人工

蜂群算法求解的过程相结合，以快速准确获得使整体利益最大的解。通过仿真实

验，验证了模型的有效性。

（5）结合人工蜂群算法的原理和求解流程，给出一种供应商投标策略模型。

模型基于多属性组合采购拍卖模式，融合了对竞争供应商策略的分析，着重将人

工蜂群算法优化流程与当前供应商决策过程相结合，分析竞争供应商的投标策

略，以得到当前供应商下一轮的最优投标策略。经仿真实验，验证了模型的有效

性。

（6）将模糊量化的 QoS 约束加入到 Web 电子物流采购模型，构造一种带有

QoS 约束的 Web 电子物流采购模型，再利用人工蜂群算法进行求解。模型基于

现有 Web 采购系统的采购模式，融合了非功能性 Web 服务评价理论，着重将带

QoS 约束的电子物流采购选择过程与人工蜂群算法求解过程相结合，从而能快速

准确地获得使整体利益最大的解。经仿真计算，验证了模型的有效性。

最后，对所做工作进行总结，并提出进一步研究的方向。

关键词：人工蜂群算法电子商务智能优化自动谈判反向拍卖

电子采购

ABSTRACT

Artificial Bee Colony Algorithm（ABC）is a new type of swarm intelligence

algorithm, which is based on the the process of all kinds of bees to cooperate to get

the best sources. ABC divided bee populations into employee bee, onlooker bee and

Scouts bee. Three kinds of bees take turns to detect to find the global optimal solution.

The nectar volume of a food source are proportional to the detected possibility of the

food source, so it is easy for the bees to stop to exploit the poor food source.

Additionally, bee can share the information about food source to others. In conclusion,

ABC has its own advantages, but ABC, just like other intelligence algorithm, has

some disadvantages such as it’s easy to convergence to the local extreme value point

and its application should be broaden.

This paper improved ABC and researched its application in E-commerce relative

problem. The main research work is as follows.

(1) Summarize the idea and solving steps of ABC algorithm, introduce the

research background of ABC and its application in home and abroad. Summarize the

merit and demerit of ABC for the follow-up study.

(2) Aiming at the premature convergence problem in artificial bee algorithm, a

kind of cellular artificial bee algorithm is proposed, which is based on the principles

of cellular automata theory. The evolution rule of cellular and its neighbor are

introduced into the algorithm to maintain the bee population’s diversity and the

algorithm uses evolutionary rule of cellular to avoid local optima. Simulated tests of

typical 0-1proramming problem and comparisons with standard ABC and Global-best

ABC show the algorithm has fast convergence speed and good global optimization

ability.

(3) To improve the accuracy and efficiency of the local search, the echolocation

mechanisms from the bat algorithm and Natural selection threshold (NST) were

introduced to improve ABC algorithm. Improved ABC was used to solve frequency

assignment problem. Simulated tests of 21-point frequency assignment problem and

comparisons with bat algorithms and standard ABC show that the algorithm had high

global convergence speed, high quality of solution and efficiency.

(4) Characteristics, protocol and process of multi-agent automated negotiation

were introduced. A kind of E-commerce automated negotiation model based on the

theory of the ABC algorithm is presented. Based on the multi-Agent and

multi-attribute automated negotiation mode, the model integrates the intelligent

electronic commerce negotiation techniques. To obtain the solution that maximized

the overall interests quickly and accurately, the process of negotiation combined with

the process of the ABC algorithm solution. The result of the simulation experiment

validate the efficiency of the model.

(5) An E-commerce automated negotiation model based on the theory of the

ABC algorithm is presented. Based on the multi-Agent and multi-attribute automated

negotiation mode, the model integrates the intelligent electronic commerce

negotiation techniques. To obtain the solution that maximized the overall interests

quickly and accurately, the process of negotiation combined with the process of the

ABC algorithm solution. The result of the simulation experiment validate the

efficiency of the model.

(6) An e-procurement with fuzzy QoS-constraint is built. And an improved

artificial bee colony algorithm is developed to solve the model. Based on the existing

web procurement mode, the model integrates the non-functional evaluation theory of

web service. To obtain the solution that maximized interests quickly and accurately,

the theory and process of artificial bee colony algorithm is used to design the

solution process of the model. The results of simulation experiments validate the

effectiveness of the model.

Key Words: Artificial bee Colony Algorithm, E-commerce,

Intelligent Optimization, Automated Negotiation, Reverse Auction,

E-procurement

中文摘要

ABSTRACT

第一章绪论------------------------------------------------------------------------------------ 1

1.1 研究背景 ------------------------------------------------------------------------------- 1

1.2 国内外研究现状 ---------------------------------------------------------------------- 2

1.2.1 国内外对人工蜂群算法的改进 --------------------------------------------- 2

1.2.2 人工蜂群算法在国内外的应用 --------------------------------------------- 3

1.3 研究内容-------------------------------------------------------------------------------- 4

第二章标准人工蜂群算法 ------------------------------------------------------------------- 6

2.1 蜜蜂采蜜的生物学原理-------------------------------------------------------------- 6

2.2 人工蜂群算法的基本原理----------------------------------------------------------- 7

2.3 人工蜂群算法特点 ------------------------------------------------------------------10

2.3.1 算法优点 -----------------------------------------------------------------------10

2.3.2 算法缺点 -----------------------------------------------------------------------10

2.4 本章小结------------------------------------------------------------------------------- 11

第三章基于元胞自动机原理的改进人工蜂群算法------------------------------------12

3.1 改进人工蜂群算法 ------------------------------------------------------------------12

3.1.1 标准人工蜂群算法的改进思路 --------------------------------------------12

3.1.2 元胞自动机原理 --------------------------------------------------------------12

3.1.3 元胞人工蜂群算法 -----------------------------------------------------------13

3.2 0-1 规划问题 --------------------------------------------------------------------------15

3.2.1 0-1 规划问题概述 ------------------------------------------------------------15

3.2.2 算例选择 ----------------------------------------------------------------------16

3.3 算例求解 ------------------------------------------------------------------------------17

3.4 本章总结 ------------------------------------------------------------------------------20

第四章自然选择的改进人工蜂群算法---------------------------------------------------21

4.1 算法改进思想-------------------------------------------------------------------------21

4.1.1 提高局部搜索能力 -----------------------------------------------------------21

4.1.2 提高种群多样性 --------------------------------------------------------------22

4.2 改进算法求解流程-------------------------------------------------------------------22

4.3 频率分配问题 ------------------------------------------------------------------------24

4.4 算例求解 ------------------------------------------------------------------------------25

4.5 本章总结 ------------------------------------------------------------------------------28

第五章人工蜂群算法用于电子商务多 AGENT 自动谈判模型 -------------------29

5.1 自动谈判模型 ------------------------------------------------------------------------29

5.2 电子商务自动谈判 ------------------------------------------------------------------29

5.2.1 多Agent 谈判系统的特征 ------------------------------------------------29

5.2.2 Agent 属性的权重和效用-------------------------------------------------30

5.2.3 自动谈判过程及协议 -------------------------------------------------------31

5.3 人工蜂群算法的谈判模型设计 ---------------------------------------------------31

5.3.1 人工蜂群算法建模及求解 -------------------------------------------------31

5.4 模型验证-------------------------------------------------------------------------------35

5.4.1 实例描述 ----------------------------------------------------------------------35

5.4.2 实例验证 -----------------------------------------------------------------------35

5.5 结论-------------------------------------------------------------------------------------36

第六章人工蜂群算法用于多属性反向拍卖中投标策略模型------------------------37

6.1 反向拍卖问题 ------------------------------------------------------------------------37

6.2 建模准备 ------------------------------------------------------------------------------37

6.2.1 投标规则 -----------------------------------------------------------------------37

6.2.2 竞争供应商非价格属性的成本函数--------------------------------------38

6.3 人工蜂群算法建模 ------------------------------------------------------------------39

6.3.1 人工蜂群算法 ----------------------------------------------------------------39

6.3.2 供应商投标策略建模 --------------------------------------------------------40

6.4 实例验证-------------------------------------------------------------------------------44

6.5 结论-------------------------------------------------------------------------------------47

第七章人工蜂群算法求解支持 QOS 约束的电子采购模型 -------------------------48

7.1 电子采购模型现状 ------------------------------------------------------------------48

7.2 电子采购系统 ------------------------------------------------------------------------48

7.3 QoS 属性 -------------------------------------------------------------------------------49

7.3.1 QoS 属性选择 -----------------------------------------------------------------49

7.3.2 QoS 模糊量化 -----------------------------------------------------------------50

7.4 模型及求解----------------------------------------------------------------------------51

7.4.1 电子物流采购模型 -----------------------------------------------------------51

7.4.2 人工蜂群算法求解模型 -----------------------------------------------------52

7.5 实例测试-------------------------------------------------------------------------------53

7.5.1 问题描述 -----------------------------------------------------------------------53

7.5.2 验证求解 -----------------------------------------------------------------------54

7.6 本章小结-------------------------------------------------------------------------------55

第八章结论与展望 ---------------------------------------------------------------------------56

8.1 全文总结-------------------------------------------------------------------------------56

8.2 展望 ------------------------------------------------------------------------------------56

参考文献-----------------------------------------------------------------------------------------58

在读期间公开发表的论文和承担的科研项目及取得成果 ----------------------------64

致谢 ------------------------------------------------------------------------------------------65

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摘要：

摘要人工蜂群算法是一种新型的群智能算法，它模拟的是工蜂采蜜过程中各种蜜蜂分工合作以开采最佳蜜源的原理。该算法将采蜜蜂群分为引领蜂、跟随蜂和侦查蜂三类，通过三种蜜蜂轮流开采来寻找全局最优解。食物源的花蜜量与食物源被选择的可能性成正比，蜜蜂能及时停止对较差食物源的开采。且蜜蜂能与其他蜜蜂共同分享食物源的信息。因此，人工蜂群算法有其自身优势，但与其他的智能算法一样，人工蜂群算法也存在着易收敛到局部极值点、应用领域有待进一步拓宽等问题。本文从改进人工蜂群算法的缺陷，以及该算法在电子商务领域问题中的应用两个大的方面出发，进行了深入研究。主要工作包括：（1）综述了人工蜂群算法的思想及求解步骤，介绍蜂群优化...

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