An improved artificial bee colony algorithm for minimal time cost reduction

The artificial bee colony (ABC) is a popular heuristic optimization algorithm. Although it has fewer control parameters, it shows competitive performance compared with other population-based algorithms. The ABC algorithm is good at exploration, but poor at exploitation. Recently, a global best-guided ABC (GABC) algorithm, inspired by particle swarm optimization, has been developed to tackle this issue. However, GABC cannot be applied to binary optimization problems. In this paper, we develop an improved ABC (IABC) algorithm with a new food source update strategy. IABC employs information about the global best solution as well as personal best solutions, thus enhancing the local search abilities of the bees. The new algorithm is adjusted to solve the binary optimization problem of minimal time cost reduction. We conduct a series of experiments on four UCI datasets, and our results clearly indicate that our algorithm outperforms the existing ABC algorithms, especially on the medium-sized Mushroom dataset.

[1]  Stefano Pallottino,et al.  Equilibrium traffic assignment for large scale transit networks , 1988 .

[2]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[3]  Yuhua Qian,et al.  Test-cost-sensitive attribute reduction , 2011, Inf. Sci..

[4]  David A Cromwell.  Waiting time information services: an evaluation of how well clearance time statistics can forecast a patient's wait. , 2004, Social science & medicine.

[5]  Yu-Lin He,et al.  Particle swarm optimization for determining fuzzy measures from data , 2011, Inf. Sci..

[6]  Rung Ching Chen,et al.  An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks , 2013, International Journal of Machine Learning and Cybernetics.

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

[8]  William Zhu,et al.  Relationship among basic concepts in covering-based rough sets , 2009, Inf. Sci..

[9]  Rajesh Kumar,et al.  A boundary restricted adaptive particle swarm optimization for data clustering , 2013, Int. J. Mach. Learn. Cybern..

[10]  Alok Singh,et al.  A swarm intelligence approach to the quadratic minimum spanning tree problem , 2010, Inf. Sci..

[11]  Zhang Wen.  Knowledge Reductions in Inconsistent Information Systems , 2003 .

[12]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[13]  M. S. Kiran,et al.  XOR-based artificial bee colony algorithm for binary optimization , 2013 .

[14]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

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

[16]  Guo-Chang Gu,et al.  Research on particle swarm optimization: a review , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[17]  Nguyen Tung Linh,et al.  Application Artificial Bee Colony Algorithm (ABC) for Reconfiguring Distribution Network , 2010, 2010 Second International Conference on Computer Modeling and Simulation.

[18]  Fei-Yue Wang,et al.  Reduction and axiomization of covering generalized rough sets , 2003, Inf. Sci..

[19]  Ajith Abraham,et al.  A Hybrid Differential Artificial Bee Colony Algorithm based tuning of fractional order controller for Permanent Magnet Synchronous Motor drive , 2012, Int. J. Mach. Learn. Cybern..

[20]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[21]  Yiyu Yao,et al.  Attribute reduction in decision-theoretic rough set models , 2008, Inf. Sci..

[22]  Victor S. Sheng,et al.  Feature value acquisition in testing: a sequential batch test algorithm , 2006, ICML.

[23]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

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

[25]  Wei-Chang Yeh,et al.  Feature selection with Intelligent Dynamic Swarm and Rough Set , 2010, Expert Syst. Appl..

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

[27]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[28]  Shichao Zhang,et al.  Cost-sensitive classification with respect to waiting cost , 2010, Knowl. Based Syst..

[29]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[30]  William Zhu,et al.  Attribute reduction of data with error ranges and test costs , 2012, Inf. Sci..

[31]  Lawrence Carin,et al.  Cost-sensitive feature acquisition and classification , 2007, Pattern Recognit..

[32]  Fan Min,et al.  A hierarchical model for test-cost-sensitive decision systems , 2009, Inf. Sci..