Differential artificial bee colony for dynamic environment

Abstract

This paper introduces a novel variant of artificial bee colony algorithm for complex multimodal and dynamic optimization problem. The Differential Artificial Bee Colony (DABC) is proposed to enhance the bees update strategy for improving the quality of solutions. The DABC is also integrated with external archive to preserve the good solutions produced through the generations and contributing to the better search strategy. Comprehensive analysis of proposed algorithm is carried out on standard benchmark problems with higher dimensions (10, 30 and 50) and on dynamic optimization problems. The algorithmic suitability, robustness and convergence rate are investigated. Results show that the performance of the proposed algorithm is better and competitive to those of the other population based stochastic algorithms. © Springer-Verlag Berlin Heidelberg 2011.