Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism

Abstract

Artificial bee colony (ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies, an ABC variant named hybrid ABC (HABC) algorithm is proposed. Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

“…Hd-ABC employs a binary space partitioning (BSP) tree to save useful information of the evaluated solutions. Fan et al (2018) proposed a new ABC variant named hybrid ABC (HABC) algorithm. In HABC, inspired by real honeybees, a memory mechanism is introduced to remember the previous successful experiences and further direct the next foraging behaviour.…”

K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

“…However, it may lead to clustering conflict when the initial clustering center is excessive dense. The maximum distance method proposed in [34] reduces the number of iterations effectively, but there would be the problem of initial point deviation. It is possible that the product of two distances is the same, but the density of the points is quite different.…”

“…In order to make fair comparison, the parameter settings are referred as [35]. The algorithm is compared with the classic ABC, the Hybrid Artificial Bee Colony which proposed memory mechanisms (HABC) [34], the Improved Artificial Bee Colony which charges permutation as employed to represent the solutions (IABC) [36] and the DFSABC algorithm respectively. In order to verify the effectiveness of each component of the algorithm, CAABC-1 and CAABC-2 are also compared.…”

A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem

“…As it needs two controlling parameters only it is considered as the highly flexible algorithm, due to its flexibility as compared to the other SI methods it is used for solving many real-world optimization problems [41]. A few drawbacks of the ABC are; slow when used in serial processing because a lot of computation is required for fitness function assessment [42].…”