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Chaotic and Co-variance Based Artificial Bee Colony Algorithm
Shashank Gupta, Divya Kumar and K.K. Mishra

Artificial Bee Colony algorithm (ABC) is a nature-inspired heuristics optimization methodology which is competitive to other population-based stochastic algorithms. Recent studies have shown that ABC is good in exploration but poor in exploitation. In this paper, we have applied co-variance matrix adoption and chaotic map in order to improve the convergence toward the solution of ABC algorithm. Proposed chaotic and covariance-based ABC (C2ABC) gives a better result than ABC and it’s variants most of the time when applied to Black-Box Optimization Benchmarking (BBOB). Our literature proves that proposed C2ABC is better than most of the variants of ABC for continuous global optimization problem.

Keywords: Swarm intelligence, artificial bee colony (ABC), chaotic map, covariance, opposition-based learning

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