A Spatially Informative Optic Flow Model of Bee Colony With Saccadic Flight Strategy for Global Optimization

This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.

[1]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[2]  M. Rockstein Bees. Their Vision, Chemical Senses, and Language , 1952 .

[3]  D. N. Lee The optic flow field: the foundation of vision. , 1980, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[4]  Lee Dn,et al.  The optic flow field: the foundation of vision. , 1980 .

[5]  R. Menzel,et al.  Color Vision Honey Bees: Phenomena and Physiological Mechanisms , 1989 .

[6]  S. Zhang,et al.  Range perception through apparent image speed in freely flying honeybees , 1991, Visual Neuroscience.

[7]  P. A. Moritz,et al.  Bees as Superorganisms , 1992, Springer Berlin Heidelberg.

[8]  J. Biesmeijer,et al.  Modelling collective foraging by means of individual behaviour rules in honey-bees , 1998, Behavioral Ecology and Sociobiology.

[9]  Masafumi Hagiwara,et al.  Bee System: Finding Solution by a Concentrated Search , 1998 .

[10]  Mandyam V. Srinivasan,et al.  Motion detection in insect orientation and navigation , 1999, Vision Research.

[11]  T. Carew Behavioral Neurobiology: The Cellular Organization of Natural Behavior , 2000 .

[12]  S. Pratt,et al.  Information flow, opinion polling and collective intelligence in house-hunting social insects. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[13]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[14]  T. Judd The waggle dance of the honey bee: Which bees following a dancer successfully acquire the information? , 1994, Journal of Insect Behavior.

[15]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

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

[17]  Yaochu Jin,et al.  A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..

[18]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[19]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

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

[21]  Martin Egelhaaf,et al.  Saccadic flight strategy facilitates collision avoidance: closed-loop performance of a cyberfly , 2008, Biological Cybernetics.

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

[23]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[24]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[25]  Q. Henry Wu,et al.  Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior , 2009, IEEE Transactions on Evolutionary Computation.

[26]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[27]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[28]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

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

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

[31]  Mesut Gündüz,et al.  A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems , 2013, Appl. Soft Comput..

[32]  Swagatam Das,et al.  Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization , 2013, Appl. Soft Comput..