人工蜂群算文：人工蜂群算法混合人工蜂群算法并行

人工蜂群算文：混合人工蜂群算法的改进研究

【中文摘要】人工蜂群算法(Artificial Bee Colony, ABC)是近年来流行的一种进化计算方法,受启发于蜂群个体间相互协作的特定社会群体行为,是一种基于种群搜索策略的启发式优化算法。人工蜂群算法优点明显,如原理简单、参数少和容易实现等,且已被证明是一种优秀的全局优化算法,得到了众多学者的关注。但是人工蜂群算法还存在一些不足,如易早熟收敛,进化后期寻优速度慢等。针对人工蜂群算法的不足,本文在对人工蜂群算法的原理、模型和信息共享机制进行深入探讨的基础上对人工蜂群算法进行改进,提出了两种改进算法,实验结果表明改进算法达到了预期效果。本文具体工作如下：首先详细介绍了人工蜂群算法和两种其他蜂群算法。全面分析了人工蜂群算法,包括人工蜂群算法的原理、组织框架以及算法的参数选择,同时分析了算法的发展动机、特征及优缺点。其次对混合人工蜂群算法进行了改进。将混沌搜索算法的思想引入人工蜂群算法,在观察蜂进化后期应用混沌搜索的思想,防止陷入局部最优。同时在采蜜蜂寻优过程中,利用两个进化因子来引导进化趋势,加快进化速度。实验结

果表明：混合人工蜂群算法能在保证蜂群多样性,避免陷入局部最优的情况下,提高算法的进化速度,从而较好地达到了全局寻优和局部寻优的平衡。再次,在前面混合人工蜂群算法的基础上,进一步提出了基于并行的混合人工蜂群算法。算法应用当前流行的并行多线程技术,使两个种群在同时进化的过程中,进行信息交流,有效加快了算法的进化速度,提高了该算法的性能。实验表明：算法有效提高了寻优效率,取得了全局与局部寻优的平衡,与人工蜂群算法和混合人工蜂群算法相比,具有更高的综合性能。最后,对本文的分析研究以及相关工作进行了概括和总结,提出了下一步研究的几个方向。

【英文摘要】Artificial Bee Colony (ABC), a novel evolutionary computation technique originally inspired by certain social behaviors of bee flocking and bee schooling, is an adaptive stochastic algorithm based on swarm searching strategies. Due to its simplicity of implementation and little number of parameters, ABC has been approved to be a good global optimization algorithm and has won more and more attention. However, there are some drawbacks, such as premature convergence, slow down of the evolution of post-optimization and so on.To improve the shortcomings of ABC algorithm, The ABC algorithm theory, the algorithm framework model and the model of information exchage are also deeply studied in this paper.

Based on these theoretical investigations, this paper presents two improved algorithms of ABC. The experiments show that the improved algorithm is feasible and effective. This paper is carried out as follows:Firstly, this paper studies and analyzes ABC algorithm. The principles, implementation and developing causes of ABC algorithm are elaborated here. Then the author probes into the information exchanging model of ABC. In addition, some typical strategies of perfecting ABC algorithm are also introduced and the principles and methods of those algorithms are elaborated, all of which help the understanding of the meanings of ABC research and development.Secondly, this paper presents a Hybrid Artificial Bee Colony(HABC) algorithm, into which the idea of chaos searching is introduced. In later phase of onlooker bees evolution, applying the idea of chaos searching to avoid local convergence. Meantime, in phase of employed bees evolution, introduction of two evolution factors increases the speed of evolution. The experiment shows that HABC not only guarantees diversity, but also increases the speed of evolution.Thirdly, based on the previous study, Hybrid Artificial bee colony is further presented. This algorithm(Hybrid Artificial bee colony based on

parallelization, HABCBP) uses the idea of popular

parallelization technology nowadays, exchanging information in process of two colonys’evlution, improve the optimization ability. The experiment shows that, compared to ABC, HABCBP has better optimization ability and efficiency. Generally speaking HABCBP wins the balance of local optimization and global optimization, and gets better performance.Finally, the author summarizes the study as well as puts forward further research expectation.

【目录】混合人工蜂群算法的改进研究摘要4-511-1912-1413

ABSTRACT5-6

目录7-9

CONTENTS9-11

第一章 绪论

1.1 研究背景11-121.2 人工蜂群算法的研究现状

1.2.2 设计研究现状

1.3.1

1.2.1 理论研究现状12-13

1.2.3 应用研究现状13-141.3 优化问题14-16

全局优化14-15新点1619-27

1.3.2 局部优化15-161.4 本文研究思路与创

1.5 本文的组织16-192.1 群体智能19-20

第二章 人工蜂群算法

2.2.1

2.2 人工蜂群算法20-24

人工蜂群算法原理20-2222-2324-2625-26

2.2.2 人工蜂群算法流程

2.3 其他典型的蜂群算法

2.3.2 蜜蜂算法

2.2.3 参数分析23-24

2.3.1 蜜蜂婚配优化算法24-252.4 本章小结26-27

第三章 混合人工蜂群算法

27-43出28-3231-3232-3535-31-4343

3.1 引言273.2 混沌搜索算法27-283.3 HABC的提

3.3.1 HABC的基本思想29-313.4 仿真实验及结果分析32-413.4.2 结果的度量方法35

3.3.2 HABC算法流程3.4.1 标准测试函数

3.4.3 算法的参数设置

3.5 本章小结

4.1 引言4.2.2

3.4.4 实验结果及其分析36-41

第四章 基于并行的混合人工蜂群算法43-57

4.2 并行的思想43-484.2.1 多线程技术44-45

多线程编程方法45-47的提出48-51算法流程50-51参数设置5156-57

4.2.3 线程同步47-484.3 HABCBP算法4.3.2 HABCBP4.4.1 算法的

4.3.1 HABCBP的基本思想48-504.4 数值实验及结果分析51-56

4.4.2 实验结果及其分析51-56

参考文献59-62

4.5 本章小结攻读硕士学位期

第五章 结论57-59

间发表的论文62-65致谢65