A model for controlling variance in the Artificial Bee Colony algorithm
Satish Chandra, Vivek Kothari, Mudita Sharma · 2015
The problem of solving large optimization problems has gained attention due to increasing numbers of constraints. Out of the numerous techniques, which have been used for solving such problems, one, which has gained much popularity in recent times, is that of the stochastic population based search. Evolutionary or Swarm based search algorithms are based on this principle. Artificial Bee Colony (ABC) algorithm is new entrant into this fray. Unlike several other population based search algorithms, the ABC, however, shows large amounts of variance in its runs. The algorithm has also not been modeled with any significant degree of success. After briefly covering the Artificial Bee Colony algorithm (used in recent solution to the variation problem), this paper proposes and analyzes a mathematical model for this algorithm (and others showing the same characteristics). This work then gives overview of Genetic Algorithm and looks at a recently proposed strategy for controlling variation using Genetic operators. It concludes by analyzing the modification, outlining the generality and applicability of proposed model and discussing future work.