A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems

This paper presents a hybridization of particle swarm optimization (PSO) and artificial bee colony (ABC) approaches, based on recombination procedure. The PSO and ABC are population-based iterative methods. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents (employed, onlooker and scout bees) of the ABC do not directly use this information but the global best solution in the ABC is stored at the each iteration. The global best solutions obtained by the PSO and ABC are used for recombination, and the solution obtained from this recombination is given to the populations of the PSO and ABC as the global best and neighbor food source for onlooker bees, respectively. Information flow between particle swarm and bee colony helps increase global and local search abilities of the hybrid approach which is referred to as Hybrid approach based on Particle swarm optimization and Artificial bee colony algorithm, HPA for short. In order to test the performance of the HPA algorithm, this study utilizes twelve basic numerical benchmark functions in addition to CEC2005 composite functions and an energy demand estimation problem. The experimental results obtained by the HPA are compared with those of the PSO and ABC. The performance of the HPA is also compared with that of other hybrid methods based on the PSO and ABC. The experimental results show that the HPA algorithm is an alternative and competitive optimizer for continuous optimization problems.     

人工蜂群算法的无人机航路规划与平滑

航路规划是无人机(UAV)作战任务规划系统的关键组成部分,目标是在适当的时间内为UAV计算出最优或次最优的飞行航路.人工蜂群(ABC)算法是一种最新发展的模拟昆虫王国中蜜蜂群体寻找优良蜜源的群体智能优化算法.采用人工蜂群算法完成无人机的平滑航路规划,首先阐述了人工蜂群算法的基本原理,然后将无人机航路规划问题通过建模转换成为一个多维函数优化问题,利用人工蜂群算法的优势,找到多维函数的最优解,最后对优化后的航路进行了平滑,使UAV对规划后的航路可飞.仿真实验结果表明,此方法可有效规划出航路,且所规划的航路可飞.     

人工蜂群算法在带约束圆形布局问题中的应用

人工蜂群算法是一种基于蜜蜂采蜜机制的新型演化算法。给出了带平衡约束的圆形布局问题的数学模型,介绍了人工蜂群算法的基本过程以及计算流程,将人工蜂群算法应用于带平衡约束的圆形布局优化中。通过两个实例进行仿真计算,并将计算结果与文献结果比较,验证了人工蜂群算法是解决此类问题的一种有效且实用的群智能算法。     

改进人工蜂群算法及其在边缘计算卸载的应用

移动边缘计算（MEC）通过将算力下沉到网络边缘来降低计算时延和设备能耗。针对计算密集型和时延敏感型应用场景,提出了一种单多维动态种群策略的人工蜂群算法（OMABC）来实现计算任务的卸载。建立一个包含云服务器的边缘计算卸载模型,并构建一个以能耗为惩罚项的代价函数;将计算任务的卸载决策转化为人工蜂群算法对代价函数的寻优过程。通过仿真实验,在CEC 2017测试函数上验证了OMABC的有效性,并在边缘计算模型仿真中与本地卸载策略、随机卸载策略、基于粒子群算法（PSO）的卸载策略、基于人工蜂群算法（ABC）的卸载策略进行对比。实验结果表明,基于OMABC的边缘计算卸载策略能够有效降低MEC系统的时延及代价函数,提供更高效的服务。     

基于IAG-ABC算法的路径覆盖测试用例生成技术

针对遗传算法(genetic algorithm,GA)存在搜索初期收敛速度过快、易陷入局部最优解、未能充分结合搜索过程中的反馈信息,同时人工蜂群(artificial bee colony,ABC)算法存在初期寻优速度缓慢、局部搜索具有很大随机性等问题,对遗传算法和人工蜂群算法分别进行了改进,并将改进后的两种算法进行融合,实现两者的优势互补,提出了一种自适应遗传-蜂群（improved adaptive genetic-artificial bee colony, IAG-ABC）算法。采用路径覆盖信息设计引导算法搜索方向的适应度函数,并用IAG-ABC算法实现路径覆盖的测试用例生成,实验结果表明,相对于标准遗传算法和自适应遗传算法,IAG-ABC算法在测试用例生成效率和覆盖率上均有一定的优势。     

A fuzzy DEA and knapsack formulation integrated model for project selection

Project selection has become crucial in the fields of science and engineering. This paper discusses the specific problem of selecting a portfolio of projects that achieves an organization's objectives without exceeding limited capital resources, especially when each project possesses vague input and output data in the selection. In this paper, a data envelopment analysis (DEA), knapsack formulation and fuzzy set theory integrated model is proposed to deal with the problem, and the model is demonstrated via a case study problem in engineering-procurement-construction (EPC) industry. Moreover, this paper applies three constraint handling techniques, which are factor-free penalty function based, to transform a constrained optimization problem into an unconstrained problem, and for the first time adopts the artificial bee colony (ABC) algorithm to search the solutions. The performances of these three constraint handling techniques with respect to the ABC algorithm are compared for the first time in this paper.     

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.     

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。     

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.     

An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

This paper investigates a waste collection problem with the consideration of midway disposal pattern. An artificial bee colony (ABC)-based hybrid approach is developed to handle this problem, in which the hybrid ABC algorithm is proposed to generate the better optimum-seeking performance while a heuristic procedure is proposed to select the disposal trip dynamically and calculate the carbon emissions in waste collection process. The effectiveness of the proposed approach is validated by numerical experiments. Experimental results show that the proposed hybrid approach can solve the investigated problem effectively. The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four popular metaheuristics, namely a genetic algorithm, a particle swarm optimization algorithm, an enhanced ABC algorithm and a hybrid particle swarm optimization algorithm. It is also found that the midway disposal pattern should be used in practice because it reduces the carbon emission at most 7.16% for the investigated instances.     

The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem

The artificial bee colony (ABC) algorithm, inspired intelligent behaviors of real honey bee colonies, was introduced by Karabo?a for numerical function optimization. The basic ABC has high performance and accuracy, if the solution space of the problem is continuous. But when the solution space of the problem is discrete, the basic ABC algorithm should be modified to solve this class optimization problem. In this study, we focused on analysis of discrete ABC with neighborhood operator for well-known traveling salesman problem and different discrete neighborhood operators are replaced with solution updating equations of the basic ABC. Experimental computations show that the promising results are obtained by the discrete version of the basic ABC and which neighborhood operator is better than the others. Also, the results obtained by discrete ABC were enriched with 2- and 3-opt heuristic approaches in order to increase quality of the solutions.     

An artificial bee colony algorithm approach for the team orienteering problem with time windows

This study addresses a highly constrained NP-hard problem called the team orienteering problem with time windows (TOPTW), which belongs to a well-known class of vehicle routing problems. This study proposes a relatively new technique called artificial bee colony (ABC) approach to solve the TOPTW. Moreover, considering that the number of studies for discrete optimization with an ABC algorithm is comparatively low, this study presents a new use of the ABC algorithm for a difficult discrete optimization problem. Additionally, this study introduces a new food source acceptance criterion and a new scout bee search behavior, both of which significantly contribute to the solution quality. The results show that the proposed method is effective, efficient, and comparable to other approaches.     

基于改进的蜂群算法求解专有路径保护设计优化问题

针对光网络故障恢复资源利用的优化问题,采用改进的蜂群算法（IABC）来求解专有路径保护设计优化问题。由于采蜜机理的蜂群算法全局寻优能力较弱,引入禁忌表机制,增强算法搜索全局最优解的能力,并改进蜂群算法的交叉算子,增强算法的收敛速度。通过实验仿真。结果表明与传统的ABC算法相比,IABC能算法大大地提高计算效率,针对较复杂网络资源优化的NP问题提供有效的可行性实施方法。     

A modified ABC algorithm for the stage shop scheduling problem

Stage shop problem is an extension of the mixed shop as well as job shop and open shop. The problem is also a special case of the general shop. In a stage shop, each job has a number of stages; each of which includes one or more operations. As a subset of operations of a job, the operations of a stage can be done without any precedence consideration of each other, whereas the stages themselves should be processed according to a preset sequence. Due to the NP-hardness of the problem, a modified artificial bee colony (ABC) algorithm is suggested. In order to improve the exploitation feature of ABC, an effective neighborhood of the stage shop problem and PSO are used in employed and onlooker bee phases, respectively. In addition, the idea of tabu search is substituted for the greedy selection property of the artificial bee colony algorithm. The proposed algorithm is compared with the traditional ABC and the state-of-the-art CMA-ES. The computational results show that the modified ABC outperforms CMA-ES and completely dominates the traditional ABC. In addition, the proposed algorithm found high quality solutions within short times. For instance, two new optimal solutions and many new upper bounds are discovered for the unsolved benchmarks.     

改进人工蜂群算法优化支持向量机及应用

针对支持向量机(SVM)的惩罚因子和核函数参数选取难度较大的问题,提出利用改进的人工蜂群算法优化支持向量机相关参数的方法.为了提高ABC算法的寻优能力,在原始ABC算法的搜索公式中引入全局搜索因子.利用UCI数据集对优化后的模型进行验证,证明了其良好的性能.将其应用于船舶压载水系统的故障诊断,实验结果表明,IABC算法...     

基于改进人工蜂群的核模糊聚类算法

针对核模糊C均值（KFCM）算法对初始聚类中心敏感、易陷入局部最优的问题,利用人工蜂群（ABC）算法的构架简单、全局收敛速度快的优势,提出了一种改进的人工蜂群算法（IABC）与KFCM迭代相结合的聚类算法。首先,以IABC求得最优解作为KFCM算法的初始聚类中心,IABC在迭代过程中将与当前维度最优解的差值的变化率作为权值,对雇佣蜂的搜索行为进行改进,平衡人工蜂群算法的全局搜索与局部开采能力;其次,以类内距离和类间距离为基础,构造出适应KFCM算法的适应度函数,利用KFCM算法优化聚类中心;最后,IABC和KFCM算法交替执行,实现最佳聚类效果。采用3组Benchmark测试函数6组UCI标准数据集进行仿真实验,实验结果表明,与基于改进人工蜂群的广义模糊聚类（IABC-KGFCM）相比,IABC-KFCM对数据集的聚类有效性指标提高1到4个百分点,具有鲁棒性强和聚类精度高的优势。     

基于改进的人工蜂群的独立分量分析方法

针对快速固定点独立分量分析方法容易陷入局部最优解的问题,提出了一种基于改进的蜂群优化的独立分量分析方法。该方法以信号的峭度作为代价函数,利用人工蜂群方法对其进行优化。在优化的过程中,一方面为了避免随机搜索造成的开采能力弱的问题,在跟随蜂搜索阶段采用当前迭代最优解引导的方式产生新的候选解,另一方面,为了避免产生更差的解,在侦查蜂阶段,利用当前迭代中的最优解与最差解的距离产生新的解,代替最差解,提高人工蜂群优化方法的寻优效果,进而提高独立分量分析的精度。实验仿真验证了算法的性能。     

Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm

This paper aims to propose a novel design approach for on-line path planning of the multiple mobile robots system with free collision. Based on the artificial bee colony (ABC) algorithm, we propose an efficient artificial bee colony (EABC) algorithm for solving the on-line path planning of multiple mobile robots by choosing the proper objective function for target, obstacles, and robots collision avoidance. The proposed EABC algorithm enhances the performance by using elite individuals for preserving good evolution, the solution sharing provides a proper direction for searching, the instant update strategy provides the newest information of solution. By the proposed approach, the next positions of each robot are designed. Thus, the mobiles robots can travel to the designed targets without collision. Finally, simulation results of illustration examples are introduced to show the effectiveness and performance of the proposed approach.     

一种双种群差分蜂群算法

人工蜂群算法(ABC)是一种基于蜜蜂群智能搜索行为的随机优化算法.为了有效改善人工蜂群算法的性能,结合差分进化算法,提出一种新的双种群差分蜂群算法(BDABC).该算法首先通过基于反向学习的策略初始化种群,使得初始化的个体尽可能均匀分布在搜索空间,然后将种群中的个体随机分成两组,每组采用不同的优化策略同时进行寻优,并通过在两群体之间引入交互学习的思想,来提高算法的收敛速度.基于6个标准测试函数的仿真实验表明,BDABC算法能有效避免早熟收敛,全局优化能力和收敛速率都有显著提高.     

基于新型人工蜂群算法的分布式不相关并行机调度

针对考虑预防性维修的分布式不相关并行机调度问题,提出了一种新型人工蜂群算法(ABC)以最小化最大完成时间.为了获得高质量的计算结果,该算法将整个种群划分为1个引领蜂群和3个跟随蜂群,跟随蜂有自己的蜜源且采用新方式跟随引领蜂, 4种蜂群运用彼此各异的搜索策略产生新解以增强种群多样性,提出一种新策略处理侦查蜂的搜索,并利用优化数据更新整个种群.通过大量仿真实验验证了新型ABC在求解所研究问题方面的有效性和优势.