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.     

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.     

A hybrid artificial bee colony for a nurse rostering problem

The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem. This paper proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empower its exploitation capability and the usage of HCO is controlled by hill climbing rate (HCR) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset.     

A restructured artificial bee colony optimizer combining life-cycle,local search and crossover operations for droplet property prediction in printable electronics fabrication

For printable electronics fabrication, a major challenge is the print resolution and accuracy delivered by a drop-on-demand piezoelectric inkjet printhead. In order to meet the challenging requirements of printable electronics fabrication, this paper proposes a novel restructured artificial bee colony optimizer called HABC for optimal prediction of the droplet volume and velocity. The main idea of HABC is to develop an adaptive and cooperative scheme by combining life-cycle, Powell’s search and crossover-based social learning strategies for complex optimizations. HABC is a more biologically-realistic model that the reproduce and die dynamically throughout the foraging process and the population size varies as the algorithm runs. With the crossover operator, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powell’s search enables the bees deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. The proposed algorithm is benchmarked against other four state-of-the-art bio-inspired algorithms using both classical and CEC2005 test function suites. Then HABC is applied to predict the printing quality using nano-silver ink. Statistical analysis of all these tests highlights the significant performance improvement due to the beneficial combination and shows that the proposed HABC outperforms the reference algorithms.     

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.     

Dynamic clustering with improved binary artificial bee colony algorithm

One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is accepted as one of the simple, novel and efficient binary variant of the artificial bee colony, the applied mechanism for generating new solutions concerning to the information of similarity between the solutions only consider one similarity case i.e. it does not handle all similarity cases. To cover this issue, new solution generation mechanism of the discrete artificial bee colony is enhanced using all similarity cases through the genetically inspired components. Furthermore, the superiority of the proposed algorithm is demonstrated by comparing it with the basic discrete artificial bee colony, binary particle swarm optimization, genetic algorithm in dynamic (automatic) clustering, in which the number of clusters is determined automatically i.e. it does not need to be specified in contrast to the classical techniques. Not only evolutionary computation based algorithms, but also classical approaches such as fuzzy C-means and K-means are employed to put forward the effectiveness of the proposed approach in clustering. The obtained results indicate that the discrete artificial bee colony with the enhanced solution generator component is able to reach more valuable solutions than the other algorithms in dynamic clustering, which is strongly accepted as one of the most difficult NP-hard problem by researchers.     

基于人工蜂群算法的电网故障诊断

针对电网故障诊断中的0-1规划问题,从代数和几何角度优化了人工蜂群算法。仿真结果表明,人工蜂群算法具有可行性和合理性,并且综合性能显著优于传统的遗传算法;在两种人工蜂群算法中,基于几何思想的人工蜂群算法具有更好的稳定性和搜索能力,更加适用于对稳定性和精准度要求很高的场合。     

求解车辆路径问题的人工蜂群算法

采用人工蜂群算法对车辆路径问题进行求解,给出食物源的自然数编码方法,并采用邻域倒位方法生成候选食物源。应用算法求解了多个车辆路径问题的实例,并将结果与其它一些启发式算法进行了比较和分析。计算结果表明,人工蜂群算法可以有效求解车辆路径问题,同时也为算法求解其它一些组合优化问题提供了有益思路。     

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.     

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。     

基于混合人工蜂群算法的多目标柔性作业车间调度问题研究

传统的优化算法在求解面对多目标柔性作业车间调度时,往往求解效率低且难以获得最优解。为了求解多目标柔性作业车间调度问题,设计了混合人工蜂群算法。种群的初始化采用了多种方法相结合的策略。在人工蜂群算法的不同阶段采用不同的搜索机制,在雇佣蜂阶段采用开发搜索,针对跟随蜂阶段蜜蜂跟随的对象的优秀解进行小幅度的更新,从而提高了搜索的表现。禁忌搜索与改进的人工蜂群算法相结合,有效的提升了获得最优解的概率。通过相关文献中的标准实例对设计的混合人工蜂群算法进行一系列求解测试,实验的结果有效的说明了算法在求解柔性作业车间调度问题时效果显著。通过求解结果对比表明人工蜂群算法的高效性和优越性。     

A modified artificial bee colony approach for the 0-1 knapsack problem

The 0-1 knapsack problem (KP01) is one of the classical NP-hard problems in operation research and has a number of engineering applications. In this paper, the BABC-DE (binary artificial bee colony algorithm with differential evolution), a modified artificial bee colony algorithm, is proposed to solve KP01. In BABC-DE, a new binary searching operator which comprehensively considers the memory and neighbour information is designed in the employed bee phase, and the mutation and crossover operations of differential evolution are adopted in the onlooker bee phase. In order to make the searching solution feasible, a repair operator based on greedy strategy is employed. Experimental results on different dimensional KP01s verify the efficiency of the proposed method, and it gets superior performance compared with other five metaheuristic algorithms.     

求解指路标志指引路径规划问题的改进人工蜂群算法*

摘要:针对指路标志指引路径规划问题,提出了一种基于改进人工蜂群算法的求解方法。首先,基于路网拓扑表达,对指路标志指引路径规划问题进行论述;其次,考虑指路标志指引路径规划问题的离散型特点,设计了人工蜂群算法求解的具体的方法和步骤;为了提高人工蜂群算法求解指路标志指引路径规划问题的收敛速度和寻优性能,引入遗传交叉因子、精英保留策略和动态侦查蜂机制对传统人工蜂群算法进行改进;最后,选取广州市大学城作为试验区域,将改进的人工蜂群算法用于求解指路标志指引路径规划问题,试验结果表明：改进后的算法有效的解决了传统人工蜂群算法在求解指路标志指引路径规划问题时收敛速度慢、易早熟等的缺陷,更具可行性。     

A food source-updating information-guided artificial bee colony algorithm

Artificial bee colony algorithm simulates the foraging behavior of honey bees, which has shown good performance in many application problems and large-scale optimization problems. To model the bees foraging behavior more accurately, a food source-updating information-guided artificial bee colony algorithm is proposed in this paper. In this algorithm, some food source-updating information obtained during optimizing time is introduced to redefine the foraging strategies of artificial bees. The proposed algorithm has been tested on a set of test functions with dimension 30, 100, 1000 and compared with some recently proposed related algorithms. The experimental results show that the performance of artificial bee colony algorithm is significantly improved for both rotated problems and large-scale problems. Compared with the related algorithms, the proposed algorithm can achieve better or competitive performance on most test functions and greatly better performance on parts of test functions.

     

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.     

Fast artificial bee colony and its application to stereo correspondence

Nature-inspired meta-heuristics have gained popularity for solutions to many real-world complex problems, and the artificial bee colony algorithm is one of the most powerful optimisation methods among meta-heuristics. However, inefficient exploitation of onlooker bees prevents the artificial bee colony algorithm from finding the final result accurately and efficiently for complex problems. In this paper, a novel optimisation method is proposed based on the artificial bee colony algorithm. The proposed optimisation method adaptively exploits onlooker bees over generations. In addition, the proposed optimisation method is applied to a stereo-matching problem to minimise the segment-based integer energy function, which is also introduced in this paper. The experimental results show that the proposed optimisation method outperforms state-of-the-art population-based meta-heuristics, such as the genetic algorithm, differential evolution, conventional artificial bee colony, and clonal selection algorithm, for benchmark functions as well as for the stereo-matching problem.     

基于混合人工蜂群算法的并行测试任务优化研究

并行测试技术可以同时进行多个任务的测试,提高资源利用率,节约测试成本;并行测试调度问题是一种复杂的组合优化问题,是并行测试技术的核心要素;并行测试系统作为并行测试技术的载体,自身的性能和求解效率尤其重要;对并行测试完成时间极限定理进行了研究,建立了并行测试任务调度的数学模型,分析了传统元启发式算法求解并行测试问题的不足,提出了基于动态规划的递归搜索技术和人工蜂群算法相结合的混合人工蜂群算法,并采用整数规划精确算法和遗传算法对混合人工蜂群算法进行验证;得出结论采用混合人工蜂群算法进行并行测试任务的调度节约了接近50%的时间,降低了约20%的硬件资源占用,提高了测试效率,可以满足工程实际的应用。     

An efficient algorithm for high-dimensional function optimization

To solve high-dimensional function optimization problems, many evolutionary algorithms have been proposed. In this paper, we propose a new cooperative coevolution orthogonal artificial bee colony (CCOABC) algorithm in an attempt to address the issue effectively. Cooperative coevolution frame, a popular technique in evolutionary algorithms for large scale optimization problems, is adopted in this paper. This frame decomposes the problem into several subcomponents by random grouping, which is a novel decomposition strategy mainly for tackling nonseparable functions. This strategy can increase the probability of grouping interacting variables in one subcomponent. And for each subcomponent, an improved artificial bee colony (ABC) algorithm, orthogonal ABC, is employed as the subcomponent optimizer. In orthogonal ABC, an Orthogonal Experimental Design method is used to let ABC evolve in a quick and efficient way. The algorithm has been evaluated on standard high-dimensional benchmark functions. Compared with other four state-of-art evolutionary algorithms, the simulation results demonstrate that CCOABC is a highly competitive algorithm for solving high-dimensional function optimization problems.     

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.     

基于渗透式人工蜂群与蚁群优化混合的负载平衡算法

针对当前云计算负载平衡调度过程中出现的虚拟机迁移效率低和能耗高问题,提出了一种基于渗透式人工蜂群与蚁群混合优化负载平衡算法,该算法将化学渗透行为与生物启发的负载平衡算法相结合,在充分利用人工蜂群和蚁群两种优化算法优点的同时,将渗透技术应用于负载均衡。由于渗透技术支持通过云基础设施迁移的虚拟机的自动部署,从而克服了现有仿生算法在实现物理机之间负载平衡方面的缺点,提高了迁移效率。实验结果表明,以现有负载平衡算法相比,提出的算法在迁移性能上提升明显。