Two modified Artificial Bee Colony algorithms inspired by Grenade Explosion Method

Abstract Artificial Bee Colony (ABC) algorithm, a popular swarm intelligence technique based on the intelligent foraging behavior of honey bees, is good at exploration but poor at exploitation. Grenade Explosion Method (GEM) which mimics the mechanism of a grenade explosion has high reliability and fast convergence. Two modified versions of ABC inspired by GEM, namely GABC1 and GABC2, are first proposed to enhance the classical ABC׳s exploitation ability. GEM is embedded in the employed bees׳ phase of GABC1, whereas it is embedded in the onlooker bees׳ phase of GABC2. The performance differences between GABC1 and GABC2 were assessed on two sets of well-known benchmark functions and compared with that of the classical ABC and several other improved ABC algorithms. The experiments show that GABC1 has similar or better performance than GABC2 in most cases, but GABC2 performs more robust and effective than GABC1 on all the functions, they significantly outperform the competitors. These results suggest that the proposed algorithms can effectively serve as alternatives for solving global optimization problems.

[1]  Wei-Der Chang,et al.  Nonlinear CSTR control system design using an artificial bee colony algorithm , 2013, Simul. Model. Pract. Theory.

[2]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[3]  Rajiv Tiwari,et al.  Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms , 2014 .

[4]  Wei-Chang Yeh,et al.  Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..

[5]  Cheng Wu,et al.  A hybrid artificial bee colony algorithm for the job shop scheduling problem , 2013 .

[6]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[7]  Haibin Duan,et al.  Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning , 2014, Neurocomputing.

[8]  Wei-Chang Yeh,et al.  Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm , 2012, Neurocomputing.

[9]  Zabih Ghassemlooy,et al.  Routing and wavelength assignment in optical networks using Artificial Bee Colony algorithm , 2013 .

[10]  Qian Wang,et al.  A modified artificial bee colony algorithm based on converge-onlookers approach for global optimization , 2013, Appl. Math. Comput..

[11]  Tiranee Achalakul,et al.  Reducing bioinformatics data dimension with ABC-kNN , 2013, Neurocomputing.

[12]  M. Shariat Panahi,et al.  GEM: A novel evolutionary optimization method with improved neighborhood search , 2009, Appl. Math. Comput..

[13]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[14]  Ataollah Ebrahimzadeh,et al.  Recognition of control chart patterns using an intelligent technique , 2013, Appl. Soft Comput..

[15]  Dervis Karaboga,et al.  A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems , 2011, Appl. Soft Comput..

[16]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[17]  Ali Ahrari,et al.  On the utility of randomly generated functions for performance evaluation of evolutionary algorithms , 2010, Optim. Lett..

[18]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[19]  Tiranee Achalakul,et al.  The best-so-far ABC with multiple patrilines for clustering problems , 2013, Neurocomputing.

[20]  Nurhan Karaboga,et al.  Elimination of noise on transcranial Doppler signal using IIR filters designed with artificial bee colony - ABC-algorithm , 2013, Digit. Signal Process..

[21]  Masoud Shariat Panahi,et al.  On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms , 2010, Appl. Math. Comput..

[22]  Wan-li Xiang,et al.  An efficient and robust artificial bee colony algorithm for numerical optimization , 2013, Comput. Oper. Res..

[23]  Lingling Huang,et al.  A global best artificial bee colony algorithm for global optimization , 2012, J. Comput. Appl. Math..

[24]  Xiaohui Yan,et al.  A new approach for data clustering using hybrid artificial bee colony algorithm , 2012, Neurocomputing.

[25]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[26]  Raimondo Betti,et al.  Identification of structural models using a modified Artificial Bee Colony algorithm , 2013 .

[27]  Erik Valdemar Cuevas Jiménez,et al.  A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation , 2013, Expert Syst. Appl..

[28]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[29]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks , 2007, MDAI.

[30]  Swagatam Das,et al.  Co-evolving bee colonies by forager migration: A multi-swarm based Artificial Bee Colony algorithm for global search space , 2014, Appl. Math. Comput..

[31]  Ali Husseinzadeh Kashan,et al.  DisABC: A new artificial bee colony algorithm for binary optimization , 2012, Appl. Soft Comput..

[32]  Ali Ahrari,et al.  Grenade Explosion Method - A novel tool for optimization of multimodal functions , 2010, Appl. Soft Comput..

[33]  Nurhan Karaboga,et al.  Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm , 2013, Eng. Appl. Artif. Intell..

[34]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[35]  Quan-Ke Pan,et al.  A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion , 2013 .