Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
A Novel Opposition-Based Particle Swarm Optimization for Noisy Problems
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Parameter Tuning for the Artificial Bee Colony Algorithm
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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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 opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed opposition-based ABC (OABC) is compared to the performance of ABC and opposition-based Differential Evolution (ODE) when applied to the Black-Box Optimization Benchmarking (BBOB) library introduced in the previous two GECCO conferences.