Improved Local Search in Artificial Bee Colony using Golden Section Search

Artificial bee colony (ABC), an optimization algorithm is a recent addition to the family of population based search algorithm. ABC has taken its inspiration from the collective intelligent foraging behavior of honey bees. In this study we have incorporated golden section search mechanism in the structure of basic ABC to improve the global convergence and prevent to stick on a local solution. The proposed variant is termed as ILS-ABC. Comparative numerical results with the state-of-art algorithms show the performance of the proposal when applied to the set of unconstrained engineering design problems. The simulated results show that the proposed variant can be successfully applied to solve real life problems.

[1]  Tarun Kumar Sharma,et al.  Some modifications to enhance the performance of Artificial Bee Colony , 2012, 2012 IEEE Congress on Evolutionary Computation.

[2]  Guoqiang Li,et al.  Development and investigation of efficient artificial bee colony algorithm for numerical function optimization , 2012, Appl. Soft Comput..

[3]  Hao Zhang,et al.  A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production , 2012 .

[4]  Ozgur Kisi,et al.  Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm , 2012 .

[5]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[6]  A. A. Gharaveisi,et al.  A NOVEL BACTERIAL FORAGING ALGORITHM FOR OPTIMIZATION PROBLEMS , 2012 .

[7]  Dervis Karaboga,et al.  Artificial bee colony programming for symbolic regression , 2012, Inf. Sci..

[8]  J. Kiefer,et al.  Sequential minimax search for a maximum , 1953 .

[9]  Haluk Gozde,et al.  Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system , 2012 .

[10]  Tarun Kumar Sharma,et al.  Enhancing the food locations in an Artificial Bee Colony algorithm , 2011, SWIS.

[11]  Wei-Chang Yeh,et al.  Artificial bee colony algorithm-neural networks for S-system models of biochemical networks approximation , 2010, Neural Computing and Applications.

[12]  M. Dorigo,et al.  1 Positive Feedback as a Search Strategy , 1991 .

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

[14]  Manoj Kumar Tiwari,et al.  Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM , 2012, Expert Syst. Appl..

[15]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[16]  R. A. Cuninghame-Green,et al.  Applied geometric programming , 1976 .

[17]  Lenan Wu,et al.  Artificial Bee Colony for Two Dimensional Protein Folding , 2012 .

[18]  J. S. Saini,et al.  Optimal thermohydraulic performance of artificially roughened solar air heaters , 1991 .

[19]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[20]  Zheng Zhang,et al.  Pattern Recognition by PSOSQP and Rule based System , 2012 .

[21]  Tarun Kumar Sharma,et al.  Enhancing Different Phases of Artificial Bee Colony for Continuous Global Optimization Problems , 2011, SocProS.

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

[23]  T. Seeley The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies , 1995 .

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

[25]  Ali R. Yildiz,et al.  A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing , 2013, Appl. Soft Comput..

[26]  Qian Xu,et al.  A novel artificial bee colony algorithm with space contraction for unknown parameters identification and time-delays of chaotic systems , 2012, Appl. Math. Comput..

[27]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[28]  Jianzhong Zhou,et al.  An adaptive artificial bee colony algorithm for long-term economic dispatch in cascaded hydropower systems , 2012 .

[29]  Tarun Kumar Sharma,et al.  Artificial bee colony with mean mutation operator for better exploitation , 2012, 2012 IEEE Congress on Evolutionary Computation.

[30]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

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

[32]  Yilong Yin,et al.  SAR image segmentation based on Artificial Bee Colony algorithm , 2011, Appl. Soft Comput..

[33]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

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

[35]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.