The document summarizes several research papers that apply the artificial bee colony (ABC) algorithm to various optimization problems. The ABC algorithm is a new, simple, and robust optimization technique. Several papers find that ABC performs comparably or better than other algorithms like genetic algorithms, particle swarm optimization, and evolutionary strategies at clustering, constrained optimization, numerical function optimization, truss structure design optimization, and optimizing recurrent neural networks for stock price forecasting. The results demonstrate that ABC can efficiently solve multi-modal and multi-dimensional optimization problems using relatively few control parameters.

1. Authors Title Result
Dervis Karaboga, A novel clustering Artificial Bee Colony algorithm, which is a new, simple and robust optimization
technique, is used in clustering of the benchmark classification problems for
Celal Ozturk, approach: Artificial Bee
classification purpose. The performance of the ABC algorithm is compared
2011 Colony (ABC) algorithm with Particle Swarm Optimization algorithm and other techniques which are
widely used by the researchers. The results of the experiments show that the
Artificial Bee Colony algorithm can successfully be applied to clustering for the
purpose of classification.
Dervis A modified Artificial Bee This paper describes a modified ABC algorithm for constrained
Karaboga∗, Colony (ABC) algorithm for optimization problems and compares the performance of the modified
constrained optimization ABC algorithm against those of state-of-the-art algorithms for a set of
Bahriye Akay problems constrained test problems. The result show that ABC algorithm can be
2011 efficiently used for solving constrained optimization problems.
Dervis Karaboga, A comparative study of In this work, ABC is used for optimizing a large set of numerical test
Bahriye Akay Artificial Bee Colony functions the performance of ABC algorithm was compared with those of GA,
algorithm PSO, DE and ES optimization algorithms. From the results obtained in this
2009 work, it can be show that the performance of ABC algorithm is better than or
similar to that of these algorithms although it uses less control parameters and
it can be efficiently used for solving multimodal and multidimensional
optimization problems.
Mustafa Sonmez, Artificial Bee Colony In this paper, the Artificial Bee Colony algorithm with an adaptive
2011 algorithm for optimization of penalty function approach (ABC-AP) is proposed to minimize the
truss structures weight of truss structures The results of the ABC-AP is compared
with the results of other optimization algorithms, the result show that
this algorithm is a powerful search and optimization technique for
structural design.
T-Jung Hsieh, Forecasting stock markets This study presents an integrated system where wavelet transforms and
using wavelet transforms and recurrent neural network (RNN) based on artificial bee colony (abc) algorithm
H-Fen Hsiao, recurrent neural networks: An (called ABC-RNN) are combined for stock price forecasting. The system
W-Chang Yeh, integrated system based on involves three stages: (1) data preprocessing using the Wavelet Transform
2011 artificial bee colony algorithm (WT), (2) the RNN, which has a simple architecture and uses numerous
fundamental and technical indicators, and (3) the use of the Artificial Bee
Colony Algorithm (ABC) to optimize the RNN weights and biases under a
parameter space design. As these simulation results demonstrate, the
proposed system is highly promising and can be implemented in a real-time
trading system for forecasting stock prices and maximizing profits.