Computer Science 분야 (03): Algorithms; Optimization; Search equation

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms’ performances.

Artificial bee colony (ABC) is a recently proposed optimization technique which has shown to be competitive to other population-based stochastic algorithms. However, ABC is good at exploration but poor at exploitation because of its solution search strategy. Thus, to obtain an efficient performance, utilizing different characteristics of solution search strategies can be appropriate during different stages of the search process to achieve a tradeoff between exploration and exploitation. In this paper, we propose a novel multi-strategy ensemble ABC (MEABC) algorithm. In MEABC, a pool of distinct solution search strategies coexists throughout the search process and competes to produce offspring. Experiments are conducted on a set of commonly used numerical benchmark functions, including the CEC 2013 shifted and rotated problems. Results show that MEABC performs significantly better than, or at least comparable to, some well-established evolutionary algorithms.

Artificial Bee Colony (ABC) is a Swarm Intelligence algorithm that has obtained meta-heuristic researchers’ attention and favor over recent years. It comprises good balance between exploitation (employed bee phase and onlooker bee phase) and exploration (scout bee phase). As nowadays, more researchers are using ABC and its variants as a control group to perform comparisons, it is crucial that comparisons with other algorithms are fair. This paper points to some misapprehensions when comparing meta-heuristic algorithms based on iterations (generations or cycles) with special emphasis on ABC. We hope that through our findings this paper can be treated as a beacon to remind researchers to learn from these mistakes.

Artificial bee colony (ABC) algorithm has been introduced for solving numerical optimization problems, inspired collective behavior of honey bee colonies. ABC algorithm has three phases named as employed bee, onlooker bee and scout bee. In the model of ABC, only one design parameter of the optimization problem is updated by the artificial bees at the ABC phases by using interaction in the bees. This updating has caused the slow convergence to global or near global optimum for the algorithm. In order to accelerate convergence of the method, using a control parameter (modification rate-MR) has been proposed for ABC but this approach is based on updating more design parameters than one. In this study, we added directional information to ABC algorithms, instead of updating more design parameters than one. The performance of proposed approach was examined on well-known nine numerical benchmark functions and obtained results are compared with basic ABC and ABCs with MR. The experimental results show that the proposed approach is very effective method for solving numeric benchmark functions and successful in terms of solution quality, robustness and convergence to global optimum.