Abstract:
To improve the performance of Artificial Bee Colony algorithm
(ABC), an Improved ABC (IABC) for global optimization was proposed with the
opposition-based initialization method. Inspired by particle swarm optimization
algorithm and differential evolution algorithm, a new search mechanism was also
developed to balance the exploration and exploitation abilities. The algorithms
was applied to 4 benchmark function with effects of selective probability p.
To verify the performance of IABC algorithm, 10 benchmark functions were tested
with various dimensions. Numerical results demonstrated the proposed algorithms
outperformed the ABC in global optimization problems.
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