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.     

Enhanced artificial bee colony algorithm through differential evolution

Artificial bee colony algorithm (ABC) is a relatively new optimization algorithm. However, ABC does well in exploration but badly in exploitation. One possible way to improve the exploitation ability of the algorithm is to combine ABC with other operations. Differential evolution (DE) can be considered as a good choice for this purpose. Based on this consideration, we propose a new algorithm, i.e. DGABC, which combines DE with gbest-guided ABC (GABC) by an evaluation strategy with an attempt to utilize more prior information of the previous search experience to speed up the convergence. In addition, to improve the global convergence, when producing the initial population, a chaotic opposition-based population initialization method is employed. The comparison results on a set of 27 benchmark functions demonstrate that the proposed method has better performance than the other algorithms.     

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.     

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.     

基于模拟退火及蜂群算法的优化特征选择算法

为了解决中文文本分类中初始特征空间维数过高带来的“维数灾难”问题,提高分类精度和分类效率,提出了一种基于模拟退火及蜂群算法的优化特征选择算法.该算法中,以蜂群算法流程为主体,根据蜜蜂群体觅食的特点快速寻找最优解,并且针对蜂群算法容易陷入局部最优解的问题,把模拟退火算法机制引入其中.该算法既保留了蜂群算法群体寻优的特点,又可以有效地避免陷入局部最优解.通过选择合适的收益率函数和温度下降函数,用实验的方法与卡方统计、信息增益和互信息等算法进行比较,表明了该算法的可行性和有效性.     

A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

The Journal of Supercomputing - Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their...     

A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem

To minimize the makespan in permutation flowshop scheduling problems, a hybrid discrete artificial bee colony (HDABC) algorithm is presented. In the HDABC, each solution to the problem is called a food source and represented by a discrete job permutation. First, the initial population with certain quality and diversity is generated from Greedy Randomized Adaptive Search Procedure (GRASP) based on Nawaz–Enscore–Ham (NEH) heuristics. Second, the discrete operators and algorithm, such as insert, swap, path relinking and GRASP are applied to generate new solution for the employed bees, onlookers and scouts. Moreover, local search is applied to the best one. The presented algorithm is tested on scheduling problem benchmarks. Experimental results show its efficiency.     

A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem.A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.     

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.     

基于混合搜索的多种群人工蜂群算法

针对经典人工蜂群（ABC）算法搜索策略存在搜索机制单一、群体全局搜索与局部搜索运算耦合性较高的问题,提出一种基于混合搜索的多种群人工蜂群（MPABC） 算法。首先,将种群按照适应度值进行排序,得到一个有序队列,进而将其划分为随机子群、核心子群和平衡子群三类有序子群;其次,针对不同子群结合相应的个体选择机制与搜索策略,构建出不同的差异向量;最后,在群体的搜索过程中,通过三类子群实现对具有不同适应度函数值个体的有效控制,来增强群体全局搜索和局部搜索的平衡能力。通过对16个标准测试函数进行仿真实验并与具有可变搜索策略的人工蜂群（ABCVSS）算法、基于选择概率的改进人工蜂群（MABC）算法、基于粒子群策略的多精英人工蜂群（PS-MEABC）算法、基于符号函数的多搜索策略人工蜂群（MSSABC）算法和优化高维复杂函数的改进人工蜂群（IABC）算法共五种典型的蜂群算法进行了对比,实验结果显示MPABC具有较好的优化效果;与ABC算法相比,MPABC在求解高维（100维）复杂问题上的收敛速度提高了约23%,且求解精度更优。     

A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms

In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.     

反向人工蜂群算法求解混合流水车间调度问题

针对以最小化最大完工时间为优化目标的混合流水车间调度问题,提出一种融合反向学习策略的反向人工蜂群算法求解该问题。首先,根据混合流水车间调度问题的特点,建立了对应的数学模型和仿真优化模型;其次,在寻优过程中为了避免陷入局部最优,分别在种群初始化、雇佣蜂和观察蜂三个阶段引入了反向学习策略,采用两点间逆序策略和元素交换策略加快寻优速度,并采用精英保优策略保留最优解;最后,选取2个实例和21个不同规模的benchmark算例进行仿真实验,通过与相关算法的实验结果进行对比分析,验证了所提算法能有效求解此类问题。     

一种转轴人工蜂群算法

为了提高人工蜂群(ABC)算法的局部搜索能力,加快其收敛速度,将Rosenbrock转轴搜索的方法引入ABC算法,提出了一种转轴ABC算法.该算法每隔一定的迭代次数,就在ABC算法找到的当前极值的邻域内用Rosenbrock方法进行一次转轴搜索,以引导算法找到函数值下降最快的方向.此外,新算法利用对立策略对算法随机产生的初始种群进行调整,得到了质量较高的初始种群.通过对几个标准测试函数的性能测试,验证了算法的快速收敛性和稳定性,说明对其的改进是可行且有效的.     

A grey artificial bee colony algorithm

Artificial bee colony (ABC) algorithm is a very popular population-based algorithm. Unfortunately, there exists a shortcoming of slow convergence rate, which partly results from random choices of neighbor individuals regarding its solution search equation. A novel scheme for the choice of neighbors is introduced based on grey relational degrees between a current individual and its neighbors to overcome the insufficiency. Then, the chosen neighbor is used to guide the search process. Additionally, inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed. What is more, solution search equations using information of the best individual, an opposition-based learning method and a chaotic initialization technique are also integrated into the proposed algorithm called grey artificial bee colony algorithm (GABC for short). Subsequently, the effectiveness and efficiency of GABC are validated on a test suite composed of fifty-seven benchmark functions. Furthermore, it is also compared with a few state-of-the-art algorithms. The related experimental results show the effectiveness and superiority of GABC.     

A modified artificial bee colony algorithm

Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose an improved solution search equation, which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation. Then, in order to make full use of and balance the exploration of the solution search equation of ABC and the exploitation of the proposed solution search equation, we introduce a selective probability P and get the new search mechanism. In addition, to enhance the global convergence, when producing the initial population, both chaotic systems and opposition-based learning methods are employed. The new search mechanism together with the proposed initialization makes up the modified ABC (MABC for short), which excludes the probabilistic selection scheme and scout bee phase. Experiments are conducted on a set of 28 benchmark functions. The results demonstrate good performance of MABC in solving complex numerical optimization problems when compared with two ABC-based algorithms.     

具有混沌差分进化搜索的人工蜂群算法

针对人工蜂群算法的不足,结合差分进化算法中的变异思想,提出一种改进的人工蜂群算法。其基本思想是在标准人工蜂群算法中观察蜂更新蜜源的阶段,使用差分进化算子对蜜源进行更新,在差分变异算子中引入混沌序列,以提高观察蜂在此阶段的局部搜索能力,最终获得最优蜜源。仿真结果表明,引入混沌差分进化搜索的蜂群算法无论在解的求解精度上还是算法的收敛速度上均优于标准人工蜂群算法,适合于复杂函数的全局优化问题。     

求解分布式两阶段混合流水车间调度的反馈人工蜂群算法

针对考虑工厂适用性和附加资源的分布式两阶段混合流水车间调度问题(DTHFSP), 本文提出了一种反馈人工蜂群算法(FABC), 以最小化最大完成时间和总延迟时间, 该算法利用一种新型反馈机制动态调整搜索策略集.为此, 本文共设计了5 种特点各异的搜索策略, 将其用于初始策略集和备选策略集, 同时, 建立并调整雇佣蜂群和跟随蜂群的共享策略集, 雇佣蜂阶段和跟随蜂阶段在种群划分的基础上采用随机选择和自适应选择方式确定搜索策略, 在侦查蜂阶段完成后, 对搜索策略集进行动态调整. 文章进行了大量的计算实验, 计算结果表明, FABC策略合理有效, 且它对所求解的DTHFSP具有较强的搜索优势.     

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.     

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.