A new global best guided artificial bee colony algorithm with application in robot path planning

Abstract Artificial bee colony has received much attention in recent years as a competitive population-based optimization algorithm. However, its slow convergence speed and one-dimensional search strategy limit it from demonstrating advantage in separable functions. To address these concerning issues, this paper introduces a coevolution framework into ABC and designs a global best leading artificial bee colony algorithm with an improved strategy to accelerate its convergence and conquer the dependency of dimension separately. A set of classical and Congress on Evolutionary Computation 2015 benchmark functions are adopted for validating the efficiency of our algorithm. In addition, in order to show the practicality of our algorithm, a robot path-planning problem is tested, and our algorithm still achieves superior results.