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.     

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.     

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.     

An adaptive artificial bee colony algorithm based on objective function value information

Artificial bee colony (ABC) algorithm is a novel biological-inspired optimization algorithm, which has many advantages compared with other optimization algorithm, such as less control parameters, great global optimization ability and easy to carry out. It has proven to be more effective than some evolutionary algorithms (EAs), particle swarm optimization (PSO) and differential evolution (DE) when testing on both benchmark functions and real issues. ABC, however, its solution search equation is poor at exploitation. For overcoming this insufficiency, two new solution search equations are proposed in this paper. They apply random solutions to take the place of the current solution as base vector in order to get more useful information. Exploitation is further improved on the basis of enhancing exploration by utilizing the information of the current best solution. In addition, the information of objective function value is introduced, which makes it possible to adjust the step-size adaptively. Owing to their respective characteristics, the new solution search equations are combined to construct an adaptive algorithm called MTABC. The methods our proposed balance the exploration and exploitation of ABC without forcing severe extra overhead in respect of function evaluations. The performance of the MTABC algorithm is extensively judged on a set of 20 basic functions and a set of 10 shifted or rotated functions, and is compared favorably with other improved ABCs and several state-of-the-art algorithms. The experimental results show that the proposed algorithm has a higher convergence speed and better search ability for almost all functions.     

基于搜索能力均衡的人工蜂群算法

鉴于标准人工蜂群算法（ABC）局部开发能力不足,提出一种改进搜索策略的人工蜂群算法（IABC）。为提高ABC的局部开发能力,在其雇佣蜂阶段引入了一个新的具有最好个体引导的解搜索方程,为均衡ABC的搜索能力,在ABC跟随蜂阶段的搜索策略中引入了新的随机因素以增强ABC的全局探索能力,为了进一步平衡全局探索和局部开发能力,改进了ABC的侦察蜂搜索机制。为验证IABC的收敛效果,通过在12个复杂基准测试函数上的仿真实验并与其他算法相比较,发现IABC的收敛性能有显著提高。     

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.     

自扰动人工蜂群算法

人工蜂群(Artificial Bee Colony,ABC)算法是一种模仿蜂群寻找蜜源的新型算法,因具有参数简单、灵活性强等优点而被广泛用于解决工程问题。但该算法在早熟、收敛速度慢和个体越界等缺点。为此,提出一种自扰动人工蜂群算法(Novel Artificial Bee Algorithm with Adaptive Disturbance,IGABC)。该算法采用轴对称策略处理蜂群中的越界个体,提高了算法的搜索效率。通过改进全局搜索方程的结构,同时加入带阈值的线性递增策略,提出一种全新的自适应搜索方程。自适应搜索方程提高了算法的收敛精度并加快了速度。为了获得更好的全局最优解,提出一种自扰动方法对全局最优解进行扰动。选取18个基准测试函数以及近4年提出的6个改进ABC算法进行对比实验,结果表明,该算法在收敛速度和精度上均有较大的优势,尤其在处理Rosenbrock等很难寻优的复杂函数时,收敛精度提高了16个数量级。     

An efficient and robust artificial bee colony algorithm for numerical optimization

Artificial bee colony (ABC) algorithm has already shown more effective than other population-based algorithms. However, ABC is good at exploration but poor at exploitation, which results in an issue on convergence performance in some cases. To improve the convergence performance of ABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed. In ERABC, a combinatorial solution search equation is introduced to accelerate the search process. And in order to avoid being trapped in local minima, chaotic search technique is employed on scout bee phase. Meanwhile, to reach a kind of sustainable evolutionary ability, reverse selection based on roulette wheel is applied to keep the population diversity. In addition, to enhance the global convergence, chaotic initialization is used to produce initial population. Finally, experimental results tested on 23 benchmark functions show that ERABC has a very good performance when compared with two ABC-based algorithms.     

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））。     

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.     

Memetic search in artificial bee colony algorithm

Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over benchmark as well as real world optimization problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is a enough chance to skip the true solution due to large step size. In order to balance between diversity and convergence capability of the ABC, a new local search phase is integrated with the basic ABC to exploit the search space identified by the best individual in the swarm. In the proposed phase, ABC works as a local search algorithm in which, the step size that is required to update the best solution, is controlled by Golden Section Search approach. The proposed strategy is named as Memetic ABC (MeABC). In MeABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. MeABC is established as a modified ABC algorithm through experiments over 20 test problems of different complexities and 4 well known engineering optimization problems.     

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.     

Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation

Artificial bee colony (ABC) algorithm is an efficient biological-inspired optimization method, which mimics the foraging behavior of honey bees to solve the complex and nonlinear optimization problems. However, in some cases, it suffers from inefficient exploration, low exploitation and slow convergence rate. These shortcomings cause the problem of stagnation at local optimum which is dangerous in determining the true solution (optima) of the problem. Therefore, in the present paper, an attempt has been made toward the removal of the drawbacks from the classical ABC by proposing a novel hybrid method called SCABC algorithm. The SCABC algorithm hybridizes the ABC with sine cosine algorithm (SCA) to upgrade the level of exploitation and exploration in the classical ABC algorithm. The SCA is a recently introduced algorithm, which uses the trigonometric functions sine and cosine to perform the search. The validation of the SCABC algorithm is performed on a well-known benchmark set of 23 optimization problems. The various analysis metrics such as statistical, convergence and performance index analysis verify the better search ability of the SCABC as compared to classical ABC, SCA. The comparison with some other optimization algorithms demonstrates a comparatively better state of exploitation and exploration in the SCABC algorithm. Moreover, the SCABC is also employed on multilevel thresholding problems. The various performance measures demonstrate the efficacy of the SCABC algorithm in determining the optimal thresholds of gray images.

     

An efficient artificial bee colony algorithm with application to nonlinear predictive control

In this paper a constrained nonlinear predictive control algorithm, that uses the artificial bee colony (ABC) algorithm to solve the optimization problem, is proposed. The main objective is to derive a simple and efficient control algorithm that can solve the nonlinear constrained optimization problem with minimal computational time. Indeed, a modified version, enhancing the exploring and the exploitation capabilities, of the ABC algorithm is proposed and used to design a nonlinear constrained predictive controller. This version allows addressing the premature and the slow convergence drawbacks of the standard ABC algorithm, using a modified search equation, a well-known organized distribution mechanism for the initial population and a new equation for the limit parameter. A convergence statistical analysis of the proposed algorithm, using some well-known benchmark functions is presented and compared with several other variants of the ABC algorithm. To demonstrate the efficiency of the proposed algorithm in solving engineering problems, the constrained nonlinear predictive control of the model of a Multi-Input Multi-Output industrial boiler is considered. The control performances of the proposed ABC algorithm-based controller are also compared to those obtained using some variants of the ABC algorithms.     

基于精英解和随机个体邻域信息的改进人工蜂群算法

针对人工蜂群(ABC)算法开发能力差、收敛速度慢的缺点,分别提出适用于雇佣蜂和观察蜂阶段的搜索方程,其中前者用到精英解、随机选择个体及其邻域的有益信息,后者用到群体最优解的信息.所提出的搜索方程在一定程度上不仅能够加快改进算法的收敛速度,而且由于随机选择个体的引入在一定意义上可以保证算法的探索能力.对22个基准测试函数的仿真实验结果表明,所提出的算法在大多数测试函数上的性能优于对比算法.     

受粒子群和差分进化启发的人工蜂群算法

针对基本人工蜂群算法搜索策略探索能力强而开发能力弱的特点,受粒子群和差分进化思想的启发,提出了两种新的搜索策略：PSO-DE-PABC和PSO-DE-GABC。前者在随机个体附近产生新的候选位置以提高算法的多样性;后者在最优解附近产生新的候选位置以提高算法的收敛速度,并加入差分进化中的差异向量来增加种群的多样性。在此基础上,引入维度因子来控制算法的收敛速度,并且使用一种利用当前种群信息的侦查策略来增强算法的局部搜索能力。通过对10组标准测试函数的实验仿真并与基本ABC、GABC和ABC/best算法相比,结果表明PSO-DE-GABC和PSO-DE-PABC对数值优化具有更高的收敛速度和收敛精度。     

BMNABC: Binary Multi-Neighborhood Artificial Bee Colony for High-Dimensional Discrete Optimization Problems

Abstract

Many meta-heuristic algorithms have been proposed to solve continuous optimization problems. Hence, researchers have applied various techniques to change these algorithms for discrete search spaces. Artificial bee colony (ABC) algorithm is one of the well-known algorithms for real search spaces. ABC has a good ability in exploration but it is weak in exploitation. Several binary versions of ABC have been proposed so far. Since the methods are based on the standard ABC, they have the disadvantage of ABC. In this article, a new binary ABC called binary multi-neighborhood ABC (BMNABC) has been introduced to enhance the exploration and exploitation abilities in the phases of ABC. BMNABC applies the near and far neighborhood information with a new probability function in the first and second phases. A more conscious search than the standard ABC is done in the third phase for those solutions which have been not improved in the previous phases. The performance of algorithm has been evaluated by low- and high-dimensional functions and the 0-1 multidimensional knapsack problems. The proposed method has been compared with state-of-the-art algorithms. The results showed that BMNABC had a better performance in terms of solution accuracy and convergence speed.     

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design.     

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.     

基于向量整体扰动的快速收敛人工蜂群算法

针对标准人工蜂群算法收敛速度慢和易陷入早熟收敛等问题,提出一种快速收敛人工蜂群算法。首先借助反向学习理论初始化种群来提高初始解的分布质量,并在雇佣蜂和跟随蜂阶段引入向量整体扰动搜索方程加快局部搜索;然后为了跳出局部最优,采用一种随机更新搜索策略来增加蜂群多样性以平衡全局探索和局部利用能力;最后通过八个标准测试函数的仿真实验,发现所提出的算法与几个改进的人工蜂群算法相比,具有更快的收敛速度且获得了更高的求解精度,验证了算法的优越性。