具有混合群智能行为的萤火虫群优化算法研究

萤火虫群优化算法是一种新型的群智能优化算法,基本的萤火虫群优化算法存在收敛精度低等问题。为了提高算法的性能,借鉴蜂群和鸟群的群体智能行为,改进萤火虫群优化算法的移动策略。运用均匀设计调整改进算法的参数取值。若干经典测试问题的实验仿真结果表明,引入混合智能行为大幅提升了算法的优化性能。     

蜂群算法研究综述*

蜂群算法是一种模仿蜜蜂繁殖、采蜜等行为的新兴的群智能优化技术,近几年备受研究者关注。初步探讨了蜂群算法的理论基础,详细论述了基于蜜蜂繁殖行为和采蜜行为的两类蜂群算法的生物学机理及其最常见算法的应用研究情况,并分析比较了遗传算法、蚁群算法、粒子群算法和蜂群算法的优缺点、适用范围及性能。最后,总结了现有蜂群算法存在的问题,并指出其未来的研究方向。     

一类基于蜜蜂采集模型的智能算法

利用蜂群的群智能行为特征及其采集行为机制,能够设计出有效的智能算法及人工系统。这是一个较新的研究方向,近年来国内外许多学者已对该研究方向进行了广泛探索。本文阐述两个主要的基于蜜蜂采集模型的智能优化算法,蜂群算法（Bees Algorithm,BA）和人工蜂群算法（Artificial Bee Colony,ABC）,并简要介绍其它一些该类智能算法。     

蜂群优化算法在车辆路径问题中的应用

车辆路径问题（VRP）是组合优化中典型的NP难题。根据车辆路径问题的实际情况,考察车辆数和总行程两个目标函数,给出了该问题的一种新的算法,蜂群算法。通过计算若干benchmark问题,并将结果与其他算法相比较与分析,验证了算法的有效性。蜂群算法是刚刚起步的智能优化算法,目前国内外关于蜂群算法的文献较少,故不仅是拓宽蜂群算法的应用范围的有效的尝试,同时也给车辆路径问题提供了一种新的解决方法。     

量子群智能优化算法综述

随着科学技术的不断发展,最优化理论及其衍生出的算法已经广泛应用于人们的日常工作与生活当中,现实世界中的很多问题都可以被描述为组合优化问题。群智能优化算法这些年来被证明在解决组合优化问题方面效果显著,将当下处于研究热点的量子计算概念引入群智能优化算法形成的量子群智能优化算法,为更好地解决组合优化问题提出了一个新的研究方向。在过去的二十多年里,许多量子群智能优化算法被不断开发出来,同时在此基础上进行了大量改进与应用。综述了量子蚁群算法、量子粒子群算法、量子人工鱼群算法、量子人工蜂群算法、量子布谷鸟搜索算法、量子混合蛙跳算法、量子萤火虫算法、量子蝙蝠算法等量子群智能优化算法,并对量子群智能优化算法面临的问题以及未来研究方向进行了深入探讨。     

DBC优化算法在瓦斯突出预测中的应用

针对煤矿瓦斯突出因素的复杂性,提出一种新的智能优化算法一双混沌搜索蜂群（DBC）优化算法,应用于煤矿瓦斯突出的预测中．DBC优化算法对人工蜂群算法进行有效改进,在人工蜂群算法的基础上,将混沌优化机制引入蜂群的寻优过程中,利用混沌序列初始化食物源,以提高食物源的质量,防止算法的早熟收敛;同时利用混沌搜索机制进行局部搜索,以改善蜂群的区域搜索能力,解决算法易陷入局部极小值的问题．最后,利用DBC对MLPNN进行训练,建立瓦斯突出预测模型．实验结果表明,该方法对瓦斯突出具有较好的预测结果．     

基于自主学习行为的教与学优化算法

针对教与学优化（TLBO）算法收敛精度较低、易于早熟收敛等问题,提出一种基于自主学习行为的教与学优化算法（SLTLBO）。SLTLBO算法为学生构建了更加完善的学习框架,学生在完成常规"教"阶段与"学"阶段的学习行为之外,将进一步对比自己与教师、最差学生的差异,自主完成多样化的学习操作,以提高自己的知识水平,提高算法的收敛精度;同时学生通过高斯搜索的自主学习反思行为跳出局部区域,实现更好的全局搜索。利用10个基准测试函数对SLTLBO算法进行了性能测试,并将SLTLBO算法与粒子群优化（PSO）算法、智能蜂群（ABC）算法以及TLBO算法进行结果比对,实验结果验证了SLTLBO算法的有效性。     

浅析智能优化算法

算法优化在许多的工程领域得到了广泛的应用,而求解线性、非线性、随机和几何规划等各种最优化的问题也得到了快速发展。智能优化算法是利用自然界中的事物与优化过程中所具有的某些相似性而进行搜索的一种搜索算法,相对于传统的优化算法,智能优化算法在求解速度等方面具有显著优点。     

冯诺依曼人工蜂群算法求解RFID网络优化问题

本文提出了一种具有冯诺依曼社会结构的新型人工蜂群算法(VNABC)。本文采用四个测试函数验证VNABC算法性能,并将其应用于求解射频识别系统中的读写器网络覆盖和防冲突问题。试验结果表明,与基本人工蜂群算法和粒子群优化算法比较,VNABC算法求解复杂优化问题收敛速度较快、求解精度更高,从而为应用智能方法求解RFID系统优化问题提供了有效的可行方案。     

蜂群算法

蜂群算法是一种非数值优化计算方法,建立在蜜蜂自组织型与群体智能基础之上,是近几年比较热门的智能算法。本文主要介绍了蜂群算法的研究背景、基本原理、要素构成、算法流程和优缺点等现状,并对蜂群算法存在的问题进行了一些讨论,在此基础上提出了未来蜂群算法的发展方向。     

The continuous artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABC_bin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABC_bin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABC_bin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABC_bin is an alternative and simple binary optimization tool in terms of solution quality and robustness.     

A tree-structured random walking swarm optimizer for multimodal optimization

This paper develops a novel tree structured random walking swarm optimizer for seeking multiple optima in multimodal landscapes. First, we show that the artificial bee colony algorithm has some distinct advantages over the other swarm intelligence algorithms for accomplishing the multimodal optimization task, from analytical and experimental perspectives. Then, a tree-structured niching strategy is developed to assist the algorithm in exploring multiple optima simultaneously. The strategy constructs a weighted complete graph based on the positions of the food sources (candidate solutions). A minimum spanning tree that encodes the distribution of the food sources is built upon the complete graph to guide the search of the bee swarm. Each artificial bee sets out from a food source and flies along the edges of the tree to gather information about the search space. The dance trajectories of bees are simulated by a random walk model considering both distance and fitness information. Then, mutant vectors are selected from the trajectories to update the food source. This graph-based search method is introduced to simultaneously promote the progress of exploitation and exploration in multimodal environments. Extensive experiments indicate that our proposed algorithm outperforms several state-of-the-art algorithms.     

Artificial bee colony algorithm and pattern search hybridized for global optimization

Artificial bee colony algorithm is one of the most recently proposed swarm intelligence based optimization algorithm. A memetic algorithm which combines Hooke–Jeeves pattern search with artificial bee colony algorithm is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the exploration phase realized by artificial bee colony algorithm and the exploitation phase completed by pattern search. The proposed algorithm was tested on a comprehensive set of benchmark functions, encompassing a wide range of dimensionality. Results show that the new algorithm is promising in terms of convergence speed, solution accuracy and success rate. The performance of artificial bee colony algorithm is much improved by introducing a pattern search method, especially in handling functions having narrow curving valley, functions with high eccentric ellipse and some complex multimodal functions.     

Tent 混沌人工蜂群与粒子群混合算法

针对人工蜂群和粒子群算法的优势与缺陷,提出一种Tent混沌人工蜂群粒子群混合算法.首先利用Tent混沌反向学习策略初始化种群;然后划分双子群,利用Tent混沌人工蜂群算法和粒子群算法协同进化;最后应用重组算子选择最优个体作为跟随蜂的邻域蜜源和粒子群的全局极值.仿真结果表明,该算法不仅能有效避免早熟收敛,而且能有效跳出局部极值,与其他最新人工蜂群和粒子群算法相比具有较强的全局搜索能力和局部搜索能力.     

The improvement of glowworm swarm optimization for continuous optimization problems

Glowworm swarm optimization (GSO) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to enhances accuracy and convergence rate of the GSO, two strategies about the movement phase of GSO are proposed. One is the greedy acceptance criteria for the glowworms update their position one-dimension by one-dimension. The other is the new movement formulas which are inspired by artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). To compare and analyze the performance of our proposed improvement GSO, a number of experiments are carried out on a set of well-known benchmark global optimization problems. The effects of the parameters about the improvement algorithms are discussed by uniform design experiment. Numerical results reveal that the proposed algorithms can find better solutions when compared to classical GSO and other heuristic algorithms and are powerful search algorithms for various global optimization problems.     

引入人工蜂群搜索算子的粒子群算法

针对标准粒子群算法易出现早熟现象和收敛速度慢等问题,提出一种引入人工蜂群搜索算子的粒子群算法.首先利用人工蜂群搜索算子很强的探索能力,对粒子搜索到的历史最优位置进行搜索以帮助算法快速跳出局部最优点;然后,为了提高算法的全局收敛速度,提出一种基于混沌和反学习的初始化方法.通过12个标准测试函数的仿真实验并与其他算法相比较,所得结果表明所提出的算法具有较快的收敛速度和很强的跳出局部最优的能力.