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

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.