改进的蜂群算法

针对蜂群算法收敛速度缓慢、容易出现早熟的问题,提出一种改进的蜂群算法(IABC)。IABC在跟随阶段食物源更新中根据邻域个体食物源质量调整信息共享程度,并且随着搜索进程减弱当前食物源的影响、增强邻域信息共享强度,使蜂群在搜索初期快速收敛到最优食物源所在区域、在搜索后期提高全局收敛性能。函数测试结果表明,IABC有效地提高了ABC的收敛速度和优化精度,特别适合复杂函数的优化问题。     

Improved artificial bee colony algorithm for global optimization

The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely “ABC/best/1” and “ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing “ABC/rand/1” and “ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.     

一种强化互学习的人工蜂群算法

为了解决基本蜂群算法存在的收敛速度慢、易陷入局部最优等问题,并提高算法在探索和开发方面的寻优性能,提出一种改进的蜂群算法,称为强化互学习的人工蜂群算法（EMLABC）,针对不同种类蜜蜂分别采用不同的搜索策略,首先对于雇佣蜂通过采用提高交叉变动学习频率以及同时面向多个较优近邻学习的机制来增强算法的全局探索能力并且避免早熟;其次针对跟随蜂采用深化的互学习策略,使新生子代保持倾向于在潜在更优区域进行搜索,进而提高算法的收敛性能和精度。在16个标准测试集函数和基本蜂群算法以及最近几个变种进行对比测试,结果表明EMLABC在收敛速度、准确寻优能力和稳定性上都有显著的提升。     

An artificial bee colony algorithm for learning Bayesian networks

One basic approach to learn Bayesian networks (BNs) from data is to apply a search procedure to explore the set of candidate networks for the database in light of a scoring metric, where the most popular stochastic methods are based on some meta-heuristic mechanisms, such as Genetic Algorithm, Evolutionary Programming and Ant Colony Optimization. In this paper, we have developed a new algorithm for learning BNs which employs a recently introduced meta-heuristic: artificial bee colony (ABC). All the phases necessary to tackle our learning problem using this meta-heuristic are described, and some experimental results to compare the performance of our ABC-based algorithm with other algorithms are given in the paper.     

分阶段搜索的改进人工蜂群算法

针对人工蜂群(ABC)及其改进算法在求解高维复杂函数优化问题时,存在求解精度低、收敛速度慢、易陷入局部寻优且改进算法控制参数多的不足,提出一种分阶段搜索的改进人工蜂群算法。该算法设计了分阶段雇佣蜂搜索策略,使雇佣蜂在不同阶段具备不同的搜索特点,降低了算法陷入局部极值的概率;定义逃逸半径,使其能够更好地指导早熟个体跳出局部极值,避免了逃逸行为的盲目性;同时,采用均匀分布结合反向学习的初始化策略,促使初始解分布均匀且质量较优。通过对优化问题中8个典型高维复杂函数的仿真实验结果表明,该改进算法求解精度更高,收敛速度更快,更加适合高维复杂函数求解。     

Composite artificial bee colony algorithms: From component-based analysis to high-performing algorithms

The artificial bee colony (ABC) algorithm is a swarm intelligence algorithm inspired by the intelligent foraging behavior of a honeybee swarm. In recent years, several ABC variants that modify some components of the original ABC algorithm have been proposed. Although there are some comparison studies in the literature, the individual contribution of each proposed modification is often unknown. In this paper, the proposed modifications are tested with a systematic experimental study that by a component-wise analysis tries to identify their impact on algorithm performance. This study is done on two benchmark sets in continuous optimization. In addition to this analysis, two new variants of ABC algorithms for each of the two benchmark sets are proposed. To do so, the best components are selected for each step of the Composite ABC algorithms. The performance of the proposed algorithms were compared against that of ten recent ABC algorithms, as well as against several recent state-of-the-art algorithms. The comparison results showed that our proposed algorithms outperform other ABC algorithms. Moreover, the composite ABC algorithms are superior to several state-of-the-art algorithms proposed in the literature.     

Structural inverse analysis by hybrid simplex artificial bee colony algorithms

A hybrid simplex artificial bee colony algorithm (HSABCA) which combines Nelder–Mead simplex method with artificial bee colony algorithm (ABCA) is proposed for inverse analysis problems. The proposed algorithm is applied to parameter identification of concrete dam-foundation systems. To verify the performance of HSABCA, it is compared with the basic ABCA and a real coded genetic algorithm (RCGA) on two examples: a gravity dam and an arc dam. Results show that the proposed algorithm is an efficient tool for inverse analysis and it performs much better than ABCA and RCGA on such problems.     

Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization

The Journal of Supercomputing - This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO)...     

增强开发能力的改进人工蜂群算法

为解决人工蜂群（ABC）算法收敛速度慢、精度不高和易于陷入局部最优等问题，提出一种增强开发能力的改进人工蜂群算法。一方面，将得出的最优解以两种方式直接引入雇佣蜂搜索公式中，通过最优解指导雇佣蜂的邻域搜索行为，以增强算法的开发或局部搜索能力；另一方面，在旁观蜂搜索公式中结合当前解及其随机邻域进行搜索，以改善算法的全局优化能力。对多个常用基准测试函数的仿真实验结果表明，在收敛速度、精度和全局优化能力等方面，所提算法总体上优于其他类似的ABC算法（例如ABC/best）和集成多种搜索策略的ABC算法（例如ABCVSS（ABC algorithm with Variable Search Strategy）和ABCMSSCE（ABC algorithm with Multi-Search Strategy Cooperative Evolutionary））。     

基于K-means的改进人工蜂群聚类算法

针对K-means聚类算法对初始聚类中心敏感和易陷入局部最优解的缺点,提出一种基于K-means的人工蜂群(ABC)聚类算法。将改进的人工蜂群算法和K-means迭代相结合,使算法对初始聚类中心的依赖性和陷入局部最优解的可能性降低,提高了算法的稳定性。通过基于反向学习的初始化策略,增强了初始群体的多样性。利用非线性选择策略,改善了过早收敛问题,提高了搜索效率。通过对邻域搜索范围的动态调整,提高了算法收敛速度,增强了局部寻优能力。实验结果表明,该算法不仅克服了K-means算法稳定性差的缺点,而且具有良好的性能和聚类效果。     

基于中心解的改进人工蜂群算法

为了解决人工蜂群(ABC)算法在用于函数优化时所具有的局部探索能力不强、收敛精度不高的问题,提出一种基于中心解的人工蜂群算法。该算法结合中心解和当前最优候选解的优点,并将中心解引入到跟随蜂的局部变异策略中。跟随蜂采用轮盘赌的形式,选择某些适应度值较好的蜜源,在雇佣蜂中心解的基础上深度局部寻优,并在每次迭代中逐维更新蜜源每一维度的值。为了验证该算法的有效性,选择六个基准测试函数对三种算法进行仿真对比实验。与标准ABC算法和Best-so-far ABC算法相比,改进的ABC算法的求解精度有较大幅度提高,特别是对于Rastrigin函数,两种不同维数下均达到了理论最优值。实验结果表明:所提算法在收敛速度和寻优精度上都有明显改善。     

基于Spark的人工蜂群改进算法

针对人工蜂群（ABC）算法求解组合优化问题时效率低的问题,提出了基于Spark云计算框架的并行ABC改进算法。首先,将蜂群划分为子蜂群并将蜂群构造为弹性分布式数据集,子蜂群使用广播机制交换优秀个体;然后,采用一系列转换算子,实现蜜蜂寻找解过程的并行化;最后,用万有引力质量计算代替轮盘赌概率计算,减少计算量。通过旅行商问题（TSP）求解说明了算法的可行性。实验结果表明：对比标准ABC算法,所提算法加速比最大达到3.24;对比未改进的并行ABC算法,该算法收敛速度提高约10%。所提算法在复杂问题求解方面优势更加明显。     

基于信息熵的改进人工蜂群算法

为了克服人工蜂群算法在处理复杂性问题时收敛速度慢、收敛精度不高、易早熟等缺陷,在原始人工蜂群算法的基础上引入信息熵。信息熵本身是不确定性的一种度量,由信息熵的值来度量人工蜂群算法中跟随蜂选择的不确定性,通过控制信息熵的值达到控制算法中跟随蜂选择过程的目的,实现算法的自适应调节。通过对测试函数和不同规模TSP问题的模拟仿真,对人工蜂群算法、蚁群算法和其他改进方法进行了对比,验证了所提出改进方法的可行性和有效性。     

基于多策略融合的改进人工蜂群算法

针对标准人工蜂群算法存在易陷入局部最优、收敛速度慢等缺陷,提出一种基于多策略融合的改进人工蜂群算法。为了避免陷入局部最优,引入可调压排序选择策略,以保证种群的多样性;同时,通过跟随蜂阶段将线性调整全局引导策略、自适应动态调整因子策略与标准人工蜂群算法的更新策略组成一个动态调整策略集,通过比较食物源的当前质量值与上次迭代质量值对动态策略进行调整,以加快算法的收敛速度。利用标准测试函数进行实验仿真,结果表明该算法不仅提高了求解精度,而且加快了收敛速度,迭代次数明显减少。     

改进近邻人工蜂群算法求解柔性作业车间调度问题

为了更好地解决以最小化最大完工时间为目标的柔性作业车间调度问题,提出了一种改进的人工蜂群算法。首先,采用随机选择和反向学习策略来提高初始蜜源的质量。同时,设计了一种新颖的特征表示方式,用于计算蜜源之间的距离。在引领蜂阶段,通过引入交叉和变异策略来优化种群中的近距离蜜源。在探索蜂阶段,引入了六种变邻域方法,以扩大解空间的搜索范围。而在侦查蜂阶段,则根据蜜源的潜力值剔除局部最优个体。在15个数据集上进行了广泛实验,实验结果表明,该改进算法性能明显优于其他四种著名的群智能优化算法。该研究为解决柔性作业车间调度问题提供了一种新的有效方法,对于实际生产调度具有重要的实用价值。     

面向星地协同观测规划问题的改进人工蜂群算法

卫星和地面观测资源利用各自搭载的载荷可以完成灾害预报、环境监测、目标发现等任务,地面观测资源可以与卫星配合共同完成观测任务,提升任务观测效果.通过规划将众多的观测任务分配给有限的卫星、地面观测资源来执行可以让协同观测发挥作用,同时得出卫星和地面观测资源的协同任务执行方案,对任务规划提出了很高的要求.基于此,对星地协同观...     

自适应人工蜂群算法的盲源分离

盲源分离(BSS)是传感器信号处理领域研究热点,针对传统盲源分离算法大多存在收敛速度慢、分离精度低、适用场合窄的缺点,提出了一种基于自适应人工蜂群算法的盲源分离.利用Givens旋转变换降低计算量,搜索策略引入自适应全局指导项动态调节最优解导向作用,选择策略采用自适应Boltz-mann轮盘赌作改进平衡迭代各阶段选择压力集中程度.实验表明:基于自适应人工蜂群算法的盲源分离,能够加快收敛速度并显著提高分离精度至约3个数量级.     

An enhanced artificial bee colony algorithm with adaptive differential operators

Artificial bee colony algorithm (ABC) has been shown to be very effective to solve global optimization problems (GOPs). However, ABC performs well in exploration but relatively poorly in exploitation resulting in a slow convergence when it is used to handle complex GOPs. Differential evolution (DE) benefits from its differential operators, namely mutation operator and crossover operator, which could perturb multiple variables simultaneously and has shown a fast convergence speed. In order to improve ABC’s exploitation ability and accelerate its convergence, in this paper, we propose an enhanced ABC algorithm named ABCADE, which remedy the limitation of ABC by exploiting the advantage of differential operators. Particularly, in ABCADE, the employed bees employ differential operators to produce candidate solutions with an increasing probability, and the two important parameters (scale factor F and crossover rate CR) of differential operators are adaptively adjusted through Gaussian distribution. Moreover, to significantly differentiate the good solutions and bad solutions in a population, and put more effort in the exploitation around the good solutions, we design a new selection probability method for onlooker bees. To verify the performance of ABCADE, we compare ABCADE with other representative state-of-the-art ABC and DE algorithms, the comparison results on a set of 22 benchmark functions with various dimension sizes demonstrate that ABCADE obtains superior or comparable performance to other algorithms.