Self-adaptive Artificial Bee Colony Algorithm Research Articles

An efficient self-adaptive artificial bee colony algorithm for the distributed resource-constrained hybrid flowshop problem

Distributed factory processing has attracted the attention and application of many companies because of the low cost and high flexibility. In the present study, the self-adaptive artificial bee colony algorithm (SABC) is presented to solve the distributed resource-constrained hybrid flowshop scheduling (DRCHFS) problems aiming to minimize the makespan. In the proposed algorithm, the two-dimensional vector solution representation is employed. Then, resource constraint in the decoding process is considered. In addition, a self-adaptive perturbation structure and local search strategy based on the critical factory are investigated to enhance searching abilities. The proposed algorithm is tested based on a randomly generated set of the real shop scheduling system, and then numerically analyze and compare the proposed algorithm with the existing heuristic algorithms to verify its effectiveness.

A Self-adaptive Artificial Bee Colony Algorithm with Symmetry Initialization

The Artificial Bee Colony (ABC) algorithm is an optimization algorithm inspired by the foraging behavior of bee swarms. Similar to some evolutionary algorithms, there is a main limitation in ABC, i.e., in many problems, ABC is good at exploration but poor at exploitation. Thus, in order to overcome this limitation and improve the performance of ABC when dealing with various kinds of optimization problems, we proposed a self-adaptive artificial bee colony algorithm with symmetry initialization (SABC-SI). In our SABC-SI algorithm, a novel population initialization method based on half space and symmetry is designed, and such method can increase the diversity of initial solutions. Besides, a self- adaptive search mechanism and several new Candidate Solution Generating Strategies (CSGSes) have also been developed. Consequently, the evolutionary strategies can be selected dynamically according to their search performance. Moreover, the selection operator is improved by eliminating some of the poor solutions and making good use of the two best solutions in both the current and previous generations. The novel algorithm was tested on 25 different benchmark functions. The experimental results show that SABC-SI outperforms several state-of-the-art algorithms, which indicates that it has great potential to be applied to a wide range of optimization problems.

A self-adaptive artificial bee colony algorithm based on global best for global optimization

Intelligent optimization algorithms based on evolutionary and swarm principles have been widely researched in recent years. The artificial bee colony (ABC) algorithm is an intelligent swarm algorithm for global optimization problems. Previous studies have shown that the ABC algorithm is an efficient, effective, and robust optimization method. However, the solution search equation used in ABC is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although some complex strategies for generating candidate solutions have recently been developed, the universality and robustness of these new algorithms are still insufficient. This is mainly because only one strategy is adopted in the modified ABC algorithm. In this paper, we propose a self-adaptive ABC algorithm based on the global best candidate (SABC-GB) for global optimization. Experiments are conducted on a set of 25 benchmark functions. To ensure a fair comparison with other algorithms, we employ the same initial population for all algorithms on each benchmark function. Besides, to validate the feasibility of SABC-GB in real-world application, we demonstrate its application to a real clustering problem based on the K-means technique. The results demonstrate that SABC-GB is superior to the other algorithms for solving complex optimization problems. It means that it is a new technique to improve the ABC by introducing self-adaptive mechanism.

An improved self-adaptive artificial bee colony algorithm for global optimisation

In this paper we propose an improved self-adaptive artificial bee colony algorithm (IS-ABC) for accurate numerical function optimisation. A modified self-adaptive mechanism based on 1/5th success rule is embedded in the structure of ABC to enhance the speed of the algorithm. The proposed algorithm has been tested on various numerical benchmark functions including the non-traditional functions proposed in CEC 2008 competition. To further validate performance of proposed algorithm we had also considered two challenging real world continuous optimisation problems. The results obtained with self-adaptive ABC have been compared with those obtained by new state-of-variants of PSO, DE, etc.