The Adaptive Chemotactic Foraging with Differential Evolution algorithm

摘要

This work proposes the application of a novel evolutionary approach called the Adaptive Chemotactic Foraging with Differential Evolution algorithm (ACF_DE) on benchmark problems. This method is based on the well-known Bacterial Foraging Optimization Algorithm (BFOA), applying appropriate Differential Evolution operators and including an adaptation scheme of the chemotaxis step size to concentrate the search in the desired optimal zone. The hybrid system is compared with those of related methods on benchmark problems showing its high performance in overcoming slow and premature convergence.