A hybrid approach to artificial bee colony algorithm

In this paper, we put forward a hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation. The bee life-cycle model is firstly constructed, which means that each individual can reproduce or die dynamically throughout the searching process and population size can dynamically vary during execution. With the comprehensive learning, the bees incorporate the information of global best solution into the search equation for exploration, while the Powell’s search enables the bees deeply to exploit around the promising area. Finally, we instantiate a hybrid artificial bee colony (HABC) optimizer based on the proposed model, namely HABC. Comprehensive test experiments based on the well-known CEC 2014 benchmarks have been carried out to compare the performance of HABC against other bio-mimetic algorithms. Our numerical results prove the effectiveness of the proposed hybridization scheme and demonstrate the performance superiority of the proposed algorithm.     

An Artificial Bee Colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization

Artificial Bee Colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees’ search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that the our proposed algorithm is effective and robust and outperform than other algorithms.     

具有自适应全局最优引导快速搜索策略的人工蜂群算法

针对人工蜂群算法存在开发与探索能力不平衡的缺点,提出了具有自适应全局最优引导快速搜索策略的改进算法.在该策略中,首先采蜜蜂利用自适应搜索方程平衡了不同搜索方法的探索和开发能力;其次跟随蜂利用全局最优引导邻域搜索方程对蜜源进行精细化搜索,以提高其收敛精度和全局搜索能力.14个标准测试函数的仿真结果表明,相比其他算法,所提出的改进算法有效平衡了算法的开发与探索能力,并提高了其最优解的精度及收敛速度.     

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））。     

基于排序选择和精英引导的改进人工蜂群算法

针对人工蜂群算法收敛速度较慢、收敛精度不高的问题,提出一种基于排序选择和精英引导的改进人工蜂群算法.分析观察蜂概率选择方法在适应值变化时对于精英个体优选的不足,提出一种排序选择方法,用以替代概率选择方法,从而提高算法的收敛速度.利用精英个体对搜索的引导作用,分别提出针对采蜜蜂和观察蜂的改进邻域搜索方程,从而提高算法的搜索效率.与其他人工蜂群算法的对比结果表明,所提出的改进方法能够有效提升算法的收敛速度和收敛精度.     

Artificial Bee Colony Algorithm Based on Adaptive Search Equation and Extended Memory

From the perspective of psychology, a modified artificial bee colony algorithm (ABC, for short) based on adaptive search equation and extended memory (ABCEM, for short) for global optimization is proposed in this paper. In the proposed ABCEM algorithm, an extended memory factor is introduced into store employed bees’ and onlooker bees’ historical information comprising recent food sources, personal best food sources, and global best food sources, and the solution search equation for the employed bees is equipped with adaptive ability. Moreover, a parameter is employed to describe the importance of the extended memory. Furthermore, the extended memory is added to two solution search equations for the employed bees and the onlookers to improve the quality of food source. To evaluate the proposed algorithm, experiments are conducted on a set of numerical benchmark functions. The results show that the proposed algorithm can balance the exploration and exploitation, and can improve the accuracy of optima solutions and convergence speed compared with other current improved ABCs for global optimization in most of the tested functions.     

基于分段搜索策略的自适应差分进化人工蜂群算法

针对人工蜂群算法在求解函数优化问题时存在的探索能力强,而开发能力不足和收敛性能差的问题,本文提出一种基于分段搜索策略的自适应差分进化人工蜂群算法。该算法将改进后的差分进化算法中的变异操作引入到观察蜂的局部搜索策略中,让观察蜂在雇佣蜂逐维变异后的当前最优解周围进行局部搜索,并采用分段搜索的方式更新蜜源,以提高其局部搜索能力。仿真实验结果表明,与基本人工蜂群算法相比,改进后的算法有效地平衡了算法的探索能力和开发能力,并提高了算法的寻优精度和收敛速度。     

ABC-based manufacturing scheduling for unrelated parallel machines with machine-dependent and job sequence-dependent setup times

To date, the topic of unrelated parallel machine scheduling problems with machine-dependent and job sequence-dependent setup times has received relatively little research attention. In this study, a hybrid artificial bee colony (HABC) algorithm is presented to solve this problem with the objective of minimizing the makespan. The performance of the proposed HABC algorithm was evaluated by comparing its solutions to state-of-the-art metaheuristic algorithms and a high performing artificial bee colony (ABC)-based algorithm. Extensive computational results indicate that the proposed HABC algorithm significantly outperforms these best-so-far algorithms. Since the problem addressed in this study is a core topic for numerous industrial applications, this article may help to reduce the gap between theoretical progress and industrial practice.     

融合Powell搜索法的粒子群优化算法

为了进一步提高多模态函数寻优的效率,提出一种融合Powell搜索法的粒子群优化算法.将PSO算法的全局搜索能力与Powell法的强局部寻优能力有机地结合起来,在保证求解速度,尽可能找到全部极值点的同时提高了解的精确性.由于该算法只利用了函数值信息而不需要计算导数,是求解可微和不可微多模态函数优化问题的通用方法.仿真实验表明了新混合算法的有效性.     

Linear search applied to global motion estimation

Gradient-based algorithms for global motion estimation are effective in many image-processing tasks. However, when analytical estimation of derivatives of objective function is not possible, linear search based algorithms such as Powell perform better than the gradient-based ones. In this paper we propose global motion estimation algorithm that exploits linear search based algorithm, particularly Powell, instead of commonly used gradient-based one. We also introduce a new approach for extracting global motion parameters called Two Step Powell-based GME. Using this approach we further improve the Powell-based GME. The proposed Powell-based GME outperforms Gauss–Newton algorithm (gradient-based) in terms of PSNR. The proposed Two Step Powell GME algorithm outperforms Powell-based GME in terms of PSNR and computational time.     

具有角色转换的自适应人工蜂群算法

提出一种具有引领蜂与跟随蜂动态协调机制的改进人工蜂群算法（DHABC）。根据优化函数的寻优状态,设计了引领蜂与跟随蜂动态角色转换机制,以更好地适应全局和局部搜索;为使算法能够更好地进行局部兼顾更大范围搜索,设计了引领蜂与跟随蜂间位置信息的共享方式;为提高算法的求解速度,设计了跟随蜂进化代数起始值的计算方法;通过仿真和比较实验,改进算法较其他ABC改进算法及其他智能优化算法既参数少,便于应用,又求解精度较高。     

一种混合人工蜂群算法求解顺序相依拆卸线平衡问题

拆卸线平衡问题直接影响回收再制造成本.为此,构建了最小工作站开启数量、最短总拆卸时间、均衡工作站空闲时间、尽早拆卸有危害和高需求零部件的多目标顺序相依拆卸线平衡问题优化模型,提出一种混合人工蜂群算法.所提出算法在观察蜂跟随阶段采用分阶段选择评价法,以便更好地区分蜜源;在侦查蜂开采阶段构建基于全局学习的搜索机制,以提高开采能力.蜜蜂寻优过程中设计了简化变邻域搜索策略,提高了寻优效率.对比实验结果验证了模型的有效性和算法的优越性.     

Gaussian bare-bones artificial bee colony algorithm

As a relatively new global optimization technique, artificial bee colony (ABC) algorithm becomes popular in recent years for its simplicity and effectiveness. However, there is still an inefficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this drawback, a Gaussian bare-bones ABC is proposed, where a new search equation is designed based on utilizing the global best solution. Furthermore, we employ the generalized opposition-based learning strategy to generate new food sources for scout bees, which is beneficial to discover more useful information for guiding search. A comprehensive set of experiments is conducted on 23 benchmark functions and a real-world optimization problem to verify the effectiveness of the proposed approach. Some well-known ABC variants and state-of-the-art evolutionary algorithms are used for comparison. The experimental results show that the proposed approach offers higher solution quality and faster convergence speed.     

基于粗配准和互信息的脑部MR图像配准算法

现有的医学图像配准算法一般都存在需要人工介入、配准时间过长等问题.为了寻找快速、精确、鲁棒性强的自动配准算法,在采用主轴矩方法进行脑部MR(核磁共振)图像的初始配准的基础上,提出局部搜索算法对图像求得更精确的配准.实验表明,该方法的配准精度和现有的Powell算法都可以达到亚像素级,但局部搜索方法和Powell算法相比较,平均配准时间大大缩短;即便和采用了主轴矩粗配准的Powell算法相比较,配准效率也提高了一倍左右.主轴矩粗配准算法提高了配准效率,局部搜索算法则保证了配准的精度.     

医学图像配准的优化算法改进研究

在医学图像配准优化算法中通常采用Powell法,由于基本Powell法随迭代次数的增加,搜索方向容易趋于线性相关,为此提出了一种改进的Powell法,该算法随着迭代的增加,搜索方向的共轭程度逐渐增强可避免线性相关.将边缘检测与最大互信息相结合,提高原有算法的性能,较准确地完成图像配准任务.对提出的配准算法进行了Matlab仿真实验并对仿真结果做了分析.     

Multiprocessor task scheduling in multistage hybrid flowshops: A hybrid artificial bee colony algorithm with bi-directional planning

In this paper, the problem of scheduling multistage hybrid flowshops with multiprocessor tasks is contemplated. This is a strongly NP-hard problem for which a hybrid artificial bee colony (HABC) algorithm with bi-directional planning is developed to minimize makespan. To validate the effectiveness of the proposed algorithm, computational experiments were tested on two well-known benchmark problem sets. The computational evaluations manifestly support the high performance of the proposed HABC against the best-so-far algorithms applied in the literature for the same benchmark problem sets.     

蜂群算法解决集聚约束调度问题

为满足真实调度环境中常见的集聚约束问题,本文提出以蜂群优化为基础的调度算法,形成个性化调度方案。算法通过模仿蜂群的"觅食"和"舞蹈"行为实现寻优操作,通过赋予蜜蜂不同的"信念"实现种群的多样化,通过将集聚约束以社会规范的形式融合到蜜蜂觅食过程中满足用户对调度的个性化要求,通过蜜蜂在舞蹈过程中展示行走路径和选择参考路径实现蜂群"经验"共享。对若干标准算例的测试结果及与其它算法的比较验证了本文算法的有效性。     

图像分割在医学图像处理中的应用研究

图像分割是图像处理中的重要工作,医学图像的多样性和复杂性使其在图像分割中具有较大的难度。阈值法由于高效、简单而成为图像分割的重要方法,但对于复杂的医学图像,其效果并不很理想。Powell法是最好的直接搜索法,利用改进的Powell法可以更好地搜索目标。为此,提出了一种将Otsu法和Powell法相结合的图像分割方法,仿真实验表明,该方法可以快速有效地分割图像,鲁棒性强。     

基于刺激-响应分工机制的人工蜂群算法

针对人工蜂群算法中探索与开采的不平衡以及由此导致的求解精度低、收敛速度慢等问题,提出一种基于刺激-响应分工机制的人工蜂群算法.将探索和开采看成两种不同的搜索任务,令蜜蜂在雇佣蜂阶段执行探索,在跟随蜂阶段执行开采.根据种群多样性设计搜索任务的环境刺激,利用搜索成功率设计蜜蜂个体的响应阈值.在刺激-响应分工机制下,蜜蜂在雇...     

基于差分蜂群算法的电力系统经济负荷分配

针对电力系统经济负荷分配问题,提出一种有效的差分蜂群算法．受差分进化算法的启发,该算法基于差分进化操作改进了雇佣蜂的搜索方式,提高了探索能力和收敛速度．此外,提出一种有效的修复机制以保证新个体的可行性．该算法在带有阀点效应和多燃料特征的典型电力系统经济负荷分配问题上进行了测试．仿真结果验证了所提算法的有效性．