Generalized opposition-based artificial bee colony algorithm

The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed generalized opposition-based ABC (GOABC) is compared to the performance of ABC and opposition-based ABC (OABC) using the CEC05 benchmarks library.

[1]  Mohammed El-Abd,et al.  Opposition-based artificial bee colony algorithm , 2011, GECCO '11.

[2]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

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

[4]  Zhijian Wu,et al.  A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[5]  Zhijian Wu,et al.  Enhancing particle swarm optimization using generalized opposition-based learning , 2011, Inf. Sci..

[6]  Mohammed El-Abd,et al.  Performance assessment of foraging algorithms vs. evolutionary algorithms , 2012, Inf. Sci..

[7]  Hui Wang,et al.  Opposition-based particle swarm algorithm with cauchy mutation , 2007, 2007 IEEE Congress on Evolutionary Computation.

[8]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[9]  Dervis Karaboga,et al.  Parameter Tuning for the Artificial Bee Colony Algorithm , 2009, ICCCI.

[10]  Zhijian Wu,et al.  Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems , 2011, Soft Comput..

[11]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

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

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

[14]  Mohammed El-Abd,et al.  Black-box optimization benchmarking for noiseless function testbed using artificial bee colony algorithm , 2010, GECCO '10.

[15]  Lin Han,et al.  A Novel Opposition-Based Particle Swarm Optimization for Noisy Problems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[16]  D. Karaboga,et al.  Artificial Bee Colony ( ABC ) , Harmony Search and Bees Algorithms on Numerical Optimization , 2009 .

[17]  Martin Middendorf,et al.  Performance evaluation of artificial bee colony optimization and new selection schemes , 2011, Memetic Comput..

[18]  Jing Wang,et al.  Space transformation search: a new evolutionary technique , 2009, GEC '09.

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

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