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A Brute-Force Search Based Artificial Bee Colony Algorithm for Numerical Functions Optimization

Aditya Rajan


Artificial-bee-colony is a well-known nature inspired algorithm which mimics the forging behaviour of honeybees. This algorithm has been applied successfully to solve numerous mathematical / computational problems and gives better results in comparison to same class of algorithms. In spite of this, limitations are associated with ABC-algorithm which affects the performance of algorithm such as stuck in local optima, lack of diverse population, memory-less etc.This research work focuses on the memory less nature of ABC-algorithm and improves its efficiency by integrating the memory-pool and brute force search.Concept of memory-pool is introduced to memorize previous successful experiences of foraging behaviour. As, the size of memory-pool increases, a brute force approach is applied to identify the least feasible candidate solution and replaces it with higher feasible candidate solution in memory-pool. The proposed algorithm is tested on several mathematical functions and results show that proposed algorithm provides better convergence in comparison to other variants.


Artificial Bee Colony, Memory Pool, Optimization, Numerical Analysis

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